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stringlengths 82
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| code_codestyle
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| style_context
stringlengths 111
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'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
A_ = logging.get_logger(__name__)
A_ = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
A_ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
A_ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627,
3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'whisper'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=51865 , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1536 , SCREAMING_SNAKE_CASE_=1536 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=50257 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1500 , SCREAMING_SNAKE_CASE_=448 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=[220, 50256] , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=0.05 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=7 , **SCREAMING_SNAKE_CASE_ , ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = num_mel_bins
lowerCamelCase_ = d_model
lowerCamelCase_ = encoder_layers
lowerCamelCase_ = encoder_attention_heads
lowerCamelCase_ = decoder_layers
lowerCamelCase_ = decoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = activation_function
lowerCamelCase_ = init_std
lowerCamelCase_ = encoder_layerdrop
lowerCamelCase_ = decoder_layerdrop
lowerCamelCase_ = use_cache
lowerCamelCase_ = encoder_layers
lowerCamelCase_ = scale_embedding # scale factor will be sqrt(d_model) if True
lowerCamelCase_ = max_source_positions
lowerCamelCase_ = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
lowerCamelCase_ = classifier_proj_size
lowerCamelCase_ = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase_ = apply_spec_augment
lowerCamelCase_ = mask_time_prob
lowerCamelCase_ = mask_time_length
lowerCamelCase_ = mask_time_min_masks
lowerCamelCase_ = mask_feature_prob
lowerCamelCase_ = mask_feature_length
lowerCamelCase_ = mask_feature_min_masks
lowerCamelCase_ = median_filter_width
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , suppress_tokens=SCREAMING_SNAKE_CASE_ , begin_suppress_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def UpperCamelCase( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
lowerCamelCase_ = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
lowerCamelCase_ = {0: 'batch'}
else:
lowerCamelCase_ = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction='inputs' )
return common_inputs
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 22050 , SCREAMING_SNAKE_CASE_ = 5.0 , SCREAMING_SNAKE_CASE_ = 220 , ) -> Mapping[str, Any]:
'''simple docstring'''
lowerCamelCase_ = OrderedDict()
lowerCamelCase_ = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , time_duration=SCREAMING_SNAKE_CASE_ , frequency=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = encoder_inputs['input_features'].shape[2]
lowerCamelCase_ = encoder_sequence_length // 2 if self.use_past else seq_length
lowerCamelCase_ = super().generate_dummy_inputs(
preprocessor.tokenizer , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoder_inputs.pop('input_features' )
lowerCamelCase_ = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
lowerCamelCase_ = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def UpperCamelCase( self ) -> float:
'''simple docstring'''
return 1E-3
| 42 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
A_ = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
A_ = {
"allenai/longformer-base-4096": 4_096,
"allenai/longformer-large-4096": 4_096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = (
list(range(ord('!' ) ,ord('~' ) + 1 ) ) + list(range(ord('¡' ) ,ord('¬' ) + 1 ) ) + list(range(ord('®' ) ,ord('ÿ' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase ,__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as vocab_handle:
lowerCamelCase_ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ ,lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCamelCase_ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ' '.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = word
return word
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(' ' ) )
return bpe_tokens
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = ''.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '\n' )
lowerCamelCase_ = 0
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase_ = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE_ ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ' ' + text
return (text, kwargs)
| 42 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A_ = logging.get_logger(__name__)
A_ = {"tokenizer_file": "tokenizer.json"}
A_ = {
"tokenizer_file": {
"bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
"bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
"bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
"bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
"bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
"bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
"bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
},
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE_ = None
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
lowerCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , pre_tok_state.pop('type' ) )
lowerCamelCase_ = add_prefix_space
lowerCamelCase_ = pre_tok_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = add_prefix_space
def UpperCamelCase( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> BatchEncoding:
'''simple docstring'''
lowerCamelCase_ = kwargs.get('is_split_into_words' , SCREAMING_SNAKE_CASE_ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
' pretokenized inputs.' )
return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> BatchEncoding:
'''simple docstring'''
lowerCamelCase_ = kwargs.get('is_split_into_words' , SCREAMING_SNAKE_CASE_ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
' pretokenized inputs.' )
return super()._encode_plus(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ) + [self.eos_token_id] )
if len(SCREAMING_SNAKE_CASE_ ) > self.model_max_length:
lowerCamelCase_ = input_ids[-self.model_max_length :]
return input_ids
| 42 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A_ = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
A_ = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
A_ = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = RealmTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="[UNK]" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="[PAD]" , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[MASK]" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , do_lower_case=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , tokenize_chinese_chars=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , SCREAMING_SNAKE_CASE_ ) != do_lower_case
or normalizer_state.get('strip_accents' , SCREAMING_SNAKE_CASE_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , normalizer_state.pop('type' ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = do_lower_case
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = PaddingStrategy.MAX_LENGTH
lowerCamelCase_ = text
lowerCamelCase_ = kwargs.pop('text_pair' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = kwargs.pop('return_tensors' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(SCREAMING_SNAKE_CASE_ ):
if batch_text_pair is not None:
lowerCamelCase_ = batch_text_pair[idx]
else:
lowerCamelCase_ = None
lowerCamelCase_ = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoded_candidates.get('input_ids' )
lowerCamelCase_ = encoded_candidates.get('attention_mask' )
lowerCamelCase_ = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(SCREAMING_SNAKE_CASE_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(SCREAMING_SNAKE_CASE_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {key: item for key, item in output_data.items() if len(SCREAMING_SNAKE_CASE_ ) != 0}
return BatchEncoding(SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
lowerCamelCase_ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
lowerCamelCase_ = DDPMScheduler()
lowerCamelCase_ = AudioDiffusionPipeline(vqvae=SCREAMING_SNAKE_CASE_ , unet=self.dummy_unet , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 , return_dict=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = self.dummy_vqvae_and_unet
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(raw_audio=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , start_step=5 , steps=10 )
lowerCamelCase_ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = self.dummy_unet_condition
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=SCREAMING_SNAKE_CASE_ , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = torch.rand((1, 1, 10) )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , encoding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = torch_device
lowerCamelCase_ = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 42 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = str(__UpperCamelCase )
return len(__UpperCamelCase ) == 9 and set(__UpperCamelCase ) == set('123456789' )
def _UpperCamelCase ( ) -> int | None:
for base_num in range(99_99 ,49_99 ,-1 ):
lowerCamelCase_ = 10_00_02 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
for base_num in range(3_33 ,99 ,-1 ):
lowerCamelCase_ = 1_00_20_03 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = IFInpaintingSuperResolutionPipeline
SCREAMING_SNAKE_CASE_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
SCREAMING_SNAKE_CASE_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
SCREAMING_SNAKE_CASE_ = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
return self._get_superresolution_dummy_components()
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ) -> int:
'''simple docstring'''
if str(SCREAMING_SNAKE_CASE_ ).startswith('mps' ):
lowerCamelCase_ = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = floats_tensor((1, 3, 16, 16) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'original_image': original_image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
self._test_save_load_local()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 42 |
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = spanish_id.replace('-' ,'' ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
lowerCamelCase_ = int(spanish_id_clean[0:8] )
lowerCamelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(__UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 1 |
'''simple docstring'''
from math import pow, sqrt
def _UpperCamelCase ( *__UpperCamelCase ) -> bool:
lowerCamelCase_ = len(__UpperCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) ,6 )
if validate(__UpperCamelCase ,__UpperCamelCase )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) ,6 )
if validate(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) ,6 )
if validate(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a ,2 ) ,6 )
if validate(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a ,2 ) / molar_mass ,6 )
if validate(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 42 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = ''
for i in table:
res += inp[i - 1]
return res
def _UpperCamelCase ( __UpperCamelCase ) -> Tuple:
return data[1:] + data[0]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = ''
for i in range(len(__UpperCamelCase ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
lowerCamelCase_ = int('0b' + data[0] + data[-1] ,2 )
lowerCamelCase_ = int('0b' + data[1:3] ,2 )
return bin(s[row][col] )[2:]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> int:
lowerCamelCase_ = message[:4]
lowerCamelCase_ = message[4:]
lowerCamelCase_ = apply_table(__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = xor(__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = apply_sbox(__UpperCamelCase ,temp[:4] ) # noqa: E741
lowerCamelCase_ = apply_sbox(__UpperCamelCase ,temp[4:] )
lowerCamelCase_ = '0' * (2 - len(__UpperCamelCase )) + l # noqa: E741
lowerCamelCase_ = '0' * (2 - len(__UpperCamelCase )) + r
lowerCamelCase_ = apply_table(l + r ,__UpperCamelCase )
lowerCamelCase_ = xor(__UpperCamelCase ,__UpperCamelCase )
return temp + right
if __name__ == "__main__":
A_ = input("Enter 10 bit key: ")
A_ = input("Enter 8 bit message: ")
A_ = [6, 3, 7, 4, 8, 5, 10, 9]
A_ = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
A_ = [2, 4, 3, 1]
A_ = [2, 6, 3, 1, 4, 8, 5, 7]
A_ = [4, 1, 3, 5, 7, 2, 8, 6]
A_ = [4, 1, 2, 3, 2, 3, 4, 1]
A_ = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
A_ = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
A_ = apply_table(key, paa_table)
A_ = temp[:5]
A_ = temp[5:]
A_ = left_shift(left)
A_ = left_shift(right)
A_ = apply_table(left + right, pa_table)
A_ = left_shift(left)
A_ = left_shift(right)
A_ = left_shift(left)
A_ = left_shift(right)
A_ = apply_table(left + right, pa_table)
# encryption
A_ = apply_table(message, IP)
A_ = function(expansion, sa, sa, keya, temp)
A_ = temp[4:] + temp[:4]
A_ = function(expansion, sa, sa, keya, temp)
A_ = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
A_ = apply_table(CT, IP)
A_ = function(expansion, sa, sa, keya, temp)
A_ = temp[4:] + temp[:4]
A_ = function(expansion, sa, sa, keya, temp)
A_ = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 42 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = "auto" ) -> List[str]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = 7.5 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
# get prompt text embeddings
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
lowerCamelCase_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase_ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowerCamelCase_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = text_embeddings.shape
lowerCamelCase_ = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ = 42
if negative_prompt is None:
lowerCamelCase_ = ['']
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = text_input_ids.shape[-1]
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ = uncond_embeddings.shape[1]
lowerCamelCase_ = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowerCamelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase_ = latents_reference.to(self.device )
lowerCamelCase_ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowerCamelCase_ = (latents_shape[3] - latents_shape_reference[3]) // 2
lowerCamelCase_ = (latents_shape[2] - latents_shape_reference[2]) // 2
lowerCamelCase_ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowerCamelCase_ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowerCamelCase_ = 0 if dx < 0 else dx
lowerCamelCase_ = 0 if dy < 0 else dy
lowerCamelCase_ = max(-dx , 0 )
lowerCamelCase_ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowerCamelCase_ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase_ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 1 / 0.18_215 * latents
lowerCamelCase_ = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowerCamelCase_ = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) , return_tensors='pt' ).to(
self.device )
lowerCamelCase_ ,lowerCamelCase_ = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowerCamelCase_ = None
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ , nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
A_ = [0, 2, 4, 6, 8]
A_ = [1, 3, 5, 7, 9]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 ,-1 ,-1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
lowerCamelCase_ = 0
for digit in range(10 ):
lowerCamelCase_ = digit
result += reversible_numbers(
0 ,(remainder + 2 * digit) // 10 ,__UpperCamelCase ,__UpperCamelCase )
return result
lowerCamelCase_ = 0
for digita in range(10 ):
lowerCamelCase_ = digita
if (remainder + digita) % 2 == 0:
lowerCamelCase_ = ODD_DIGITS
else:
lowerCamelCase_ = EVEN_DIGITS
for digita in other_parity_digits:
lowerCamelCase_ = digita
result += reversible_numbers(
remaining_length - 2 ,(remainder + digita + digita) // 10 ,__UpperCamelCase ,__UpperCamelCase ,)
return result
def _UpperCamelCase ( __UpperCamelCase = 9 ) -> int:
lowerCamelCase_ = 0
for length in range(1 ,max_power + 1 ):
result += reversible_numbers(__UpperCamelCase ,0 ,[0] * length ,__UpperCamelCase )
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 |
'''simple docstring'''
import pprint
import requests
A_ = "https://zenquotes.io/api"
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
A_ = random_quotes()
pprint.pprint(response)
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=[10, 20, 30, 40] , SCREAMING_SNAKE_CASE_=[1, 1, 2, 1] , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="relu" , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , ) -> str:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embeddings_size
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = depths
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_labels
lowerCamelCase_ = scope
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = TFResNetModel(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = TFResNetForImageClassification(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ = (
{'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = TFResNetModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return
@unittest.skip(reason='ResNet does not use inputs_embeds' )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='ResNet does not support input and output embeddings' )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
def check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = model_class(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ = self.model_tester.num_stages
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCamelCase_ = layer_type
lowerCamelCase_ = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFResNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( ) -> Dict:
lowerCamelCase_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='tf' )
# forward pass
lowerCamelCase_ = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
lowerCamelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=33 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ) -> int:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = EsmModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = EsmForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = EsmForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = ()
SCREAMING_SNAKE_CASE_ = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = EsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase_ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = EsmModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowerCamelCase_ = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowerCamelCase_ = create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE_ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.empty(2 , 4 , 30 )
lowerCamelCase_ = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowerCamelCase_ = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowerCamelCase_ = embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE_ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = 33
lowerCamelCase_ = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
# compare the actual values for a slice.
lowerCamelCase_ = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 | 1 |
'''simple docstring'''
import sys
def _UpperCamelCase ( __UpperCamelCase ) -> Optional[Any]:
lowerCamelCase_ = len(__UpperCamelCase )
lowerCamelCase_ = [[0 for x in range(__UpperCamelCase )] for x in range(__UpperCamelCase )]
lowerCamelCase_ = [[0 for x in range(__UpperCamelCase )] for x in range(__UpperCamelCase )]
for chain_length in range(2 ,__UpperCamelCase ):
for a in range(1 ,n - chain_length + 1 ):
lowerCamelCase_ = a + chain_length - 1
lowerCamelCase_ = sys.maxsize
for c in range(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
lowerCamelCase_ = cost
lowerCamelCase_ = c
return matrix, sol
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
if i == j:
print('A' + str(__UpperCamelCase ) ,end=' ' )
else:
print('(' ,end=' ' )
print_optiomal_solution(__UpperCamelCase ,__UpperCamelCase ,optimal_solution[i][j] )
print_optiomal_solution(__UpperCamelCase ,optimal_solution[i][j] + 1 ,__UpperCamelCase )
print(')' ,end=' ' )
def _UpperCamelCase ( ) -> Optional[int]:
lowerCamelCase_ = [30, 35, 15, 5, 10, 20, 25]
lowerCamelCase_ = len(__UpperCamelCase )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
lowerCamelCase_ ,lowerCamelCase_ = matrix_chain_order(__UpperCamelCase )
print('No. of Operation required: ' + str(matrix[1][n - 1] ) )
print_optiomal_solution(__UpperCamelCase ,1 ,n - 1 )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 42 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCAmelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
super().__init__()
lowerCamelCase_ = model
lowerCamelCase_ = 2
lowerCamelCase_ = nn.Linear(self.model.config.hidden_size , self.num_labels )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
pass
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> int:
# load longformer model from model identifier
lowerCamelCase_ = LongformerModel.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = LightningModel(__UpperCamelCase )
lowerCamelCase_ = torch.load(__UpperCamelCase ,map_location=torch.device('cpu' ) )
lightning_model.load_state_dict(ckpt['state_dict'] )
# init longformer question answering model
lowerCamelCase_ = LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__UpperCamelCase )
print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''' )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
A_ = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 42 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'xlm-prophetnet'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 30522 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2 , **SCREAMING_SNAKE_CASE_ , ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = num_encoder_layers
lowerCamelCase_ = num_encoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = num_decoder_layers
lowerCamelCase_ = num_decoder_attention_heads
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = init_std # Normal(0, this parameter)
lowerCamelCase_ = activation_function
# parameters for xlmprophetnet
lowerCamelCase_ = ngram
lowerCamelCase_ = num_buckets
lowerCamelCase_ = relative_max_distance
lowerCamelCase_ = disable_ngram_loss
lowerCamelCase_ = eps
# 3 Types of Dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = dropout
lowerCamelCase_ = use_cache
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , add_cross_attention=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float]:
if not all(isinstance(__UpperCamelCase ,(float, int) ) for val in locals().values() ):
lowerCamelCase_ = f'''Input values must either be float or int: {list(locals().values() )}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = ((x * distance) / (z + distance)) * scale
lowerCamelCase_ = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise TypeError('Axis must be a str' )
lowerCamelCase_ = locals()
del input_variables["axis"]
if not all(isinstance(__UpperCamelCase ,(float, int) ) for val in input_variables.values() ):
lowerCamelCase_ = (
'Input values except axis must either be float or int: '
f'''{list(input_variables.values() )}'''
)
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = (angle % 3_60) / 4_50 * 1_80 / math.pi
if axis == "z":
lowerCamelCase_ = x * math.cos(__UpperCamelCase ) - y * math.sin(__UpperCamelCase )
lowerCamelCase_ = y * math.cos(__UpperCamelCase ) + x * math.sin(__UpperCamelCase )
lowerCamelCase_ = z
elif axis == "x":
lowerCamelCase_ = y * math.cos(__UpperCamelCase ) - z * math.sin(__UpperCamelCase )
lowerCamelCase_ = z * math.cos(__UpperCamelCase ) + y * math.sin(__UpperCamelCase )
lowerCamelCase_ = x
elif axis == "y":
lowerCamelCase_ = x * math.cos(__UpperCamelCase ) - z * math.sin(__UpperCamelCase )
lowerCamelCase_ = z * math.cos(__UpperCamelCase ) + x * math.sin(__UpperCamelCase )
lowerCamelCase_ = y
else:
raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\'' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')
| 42 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = x
lowerCamelCase_ = y
for step in range(__UpperCamelCase ): # noqa: B007
lowerCamelCase_ = a * a - b * b + x
lowerCamelCase_ = 2 * a * b + y
lowerCamelCase_ = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55 ) for i in colorsys.hsv_to_rgb(__UpperCamelCase ,1 ,1 ) )
def _UpperCamelCase ( __UpperCamelCase = 8_00 ,__UpperCamelCase = 6_00 ,__UpperCamelCase = -0.6 ,__UpperCamelCase = 0 ,__UpperCamelCase = 3.2 ,__UpperCamelCase = 50 ,__UpperCamelCase = True ,) -> Image.Image:
lowerCamelCase_ = Image.new('RGB' ,(image_width, image_height) )
lowerCamelCase_ = img.load()
# loop through the image-coordinates
for image_x in range(__UpperCamelCase ):
for image_y in range(__UpperCamelCase ):
# determine the figure-coordinates based on the image-coordinates
lowerCamelCase_ = figure_width / image_width * image_height
lowerCamelCase_ = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowerCamelCase_ = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowerCamelCase_ = get_distance(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowerCamelCase_ = get_color_coded_rgb(__UpperCamelCase )
else:
lowerCamelCase_ = get_black_and_white_rgb(__UpperCamelCase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
A_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 42 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
A_ = logging.get_logger(__name__)
A_ = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'perceiver'
def __init__( self , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=1280 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=26 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="kv" , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1E-12 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=262 , SCREAMING_SNAKE_CASE_=2048 , SCREAMING_SNAKE_CASE_=56 , SCREAMING_SNAKE_CASE_=[368, 496] , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=1920 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=[1, 16, 224, 224] , **SCREAMING_SNAKE_CASE_ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = num_latents
lowerCamelCase_ = d_latents
lowerCamelCase_ = d_model
lowerCamelCase_ = num_blocks
lowerCamelCase_ = num_self_attends_per_block
lowerCamelCase_ = num_self_attention_heads
lowerCamelCase_ = num_cross_attention_heads
lowerCamelCase_ = qk_channels
lowerCamelCase_ = v_channels
lowerCamelCase_ = cross_attention_shape_for_attention
lowerCamelCase_ = self_attention_widening_factor
lowerCamelCase_ = cross_attention_widening_factor
lowerCamelCase_ = hidden_act
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = initializer_range
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = use_query_residual
# masked language modeling attributes
lowerCamelCase_ = vocab_size
lowerCamelCase_ = max_position_embeddings
# image classification attributes
lowerCamelCase_ = image_size
# flow attributes
lowerCamelCase_ = train_size
# multimodal autoencoding attributes
lowerCamelCase_ = num_frames
lowerCamelCase_ = audio_samples_per_frame
lowerCamelCase_ = samples_per_patch
lowerCamelCase_ = output_shape
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def UpperCamelCase( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCamelCase_ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCamelCase_ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('inputs', dynamic_axis),
('attention_mask', dynamic_axis),
] )
@property
def UpperCamelCase( self ) -> float:
'''simple docstring'''
return 1E-4
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 3 , SCREAMING_SNAKE_CASE_ = 40 , SCREAMING_SNAKE_CASE_ = 40 , ) -> Mapping[str, Any]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowerCamelCase_ = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowerCamelCase_ = preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=SCREAMING_SNAKE_CASE_ )
# Generate dummy inputs according to compute batch and sequence
lowerCamelCase_ = [' '.join(['a'] ) * seq_length] * batch_size
lowerCamelCase_ = dict(preprocessor(SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase_ = inputs.pop('input_ids' )
return inputs
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowerCamelCase_ = compute_effective_axis_dimension(SCREAMING_SNAKE_CASE_ , fixed_dimension=OnnxConfig.default_fixed_batch )
lowerCamelCase_ = self._generate_dummy_images(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = dict(preprocessor(images=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase_ = inputs.pop('pixel_values' )
return inputs
else:
raise ValueError(
'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.' )
| 42 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase_ = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase_ = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase_ = outgoing_gradient**2 + 4
lowerCamelCase_ = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase_ = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase_ = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase_ = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase_ = x_minus if isclose(__UpperCamelCase ,__UpperCamelCase ) else x_plus
lowerCamelCase_ = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _UpperCamelCase ( __UpperCamelCase = 1.4 ,__UpperCamelCase = -9.6 ) -> int:
lowerCamelCase_ = 0
lowerCamelCase_ = first_x_coord
lowerCamelCase_ = first_y_coord
lowerCamelCase_ = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = next_point(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
from collections import namedtuple
A_ = namedtuple("from_to", "from_ to")
A_ = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1_000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00_454, 264.172),
"cubicyard": from_to(0.76_455, 1.30_795),
"cubicfoot": from_to(0.028, 35.3_147),
"cup": from_to(0.000_236_588, 4_226.75),
}
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
if from_type not in METRIC_CONVERSION:
raise ValueError(
f'''Invalid \'from_type\' value: {from_type!r} Supported values are:\n'''
+ ', '.join(__UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f'''Invalid \'to_type\' value: {to_type!r}. Supported values are:\n'''
+ ', '.join(__UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
raise ValueError(
'Warning: upper bound of deterministic test is exceeded. '
'Pass allow_probable=True to allow probabilistic test. '
'A return value of True indicates a probable prime.' )
# array bounds provided by analysis
lowerCamelCase_ = [
20_47,
1_37_36_53,
25_32_60_01,
32_15_03_17_51,
2_15_23_02_89_87_47,
3_47_47_49_66_03_83,
3_41_55_00_71_72_83_21,
1,
3_82_51_23_05_65_46_41_30_51,
1,
1,
31_86_65_85_78_34_03_11_51_16_74_61,
3_31_70_44_06_46_79_88_73_85_96_19_81,
]
lowerCamelCase_ = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__UpperCamelCase ,1 ):
if n < _p:
# then we have our last prime to check
lowerCamelCase_ = primes[:idx]
break
lowerCamelCase_ ,lowerCamelCase_ = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
lowerCamelCase_ = False
for r in range(__UpperCamelCase ):
lowerCamelCase_ = pow(__UpperCamelCase ,d * 2**r ,__UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
lowerCamelCase_ = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def _UpperCamelCase ( ) -> None:
assert not miller_rabin(5_61 )
assert miller_rabin(5_63 )
# 2047
assert not miller_rabin(83_82_01 )
assert miller_rabin(83_82_07 )
# 1_373_653
assert not miller_rabin(17_31_60_01 )
assert miller_rabin(17_31_60_17 )
# 25_326_001
assert not miller_rabin(30_78_38_66_41 )
assert miller_rabin(30_78_38_66_53 )
# 3_215_031_751
assert not miller_rabin(1_71_30_45_57_48_01 )
assert miller_rabin(1_71_30_45_57_48_19 )
# 2_152_302_898_747
assert not miller_rabin(2_77_97_99_72_83_07 )
assert miller_rabin(2_77_97_99_72_83_27 )
# 3_474_749_660_383
assert not miller_rabin(1_13_85_00_23_90_94_41 )
assert miller_rabin(1_13_85_00_23_90_95_27 )
# 341_550_071_728_321
assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 42 | 1 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = 1.0 if scale is None else scale
lowerCamelCase_ = 0.0 if loc is None else loc
super().__init__(SCREAMING_SNAKE_CASE_ , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=SCREAMING_SNAKE_CASE_ )] )
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
return self.base_dist.mean * self.scale + self.loc
@property
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
return self.base_dist.variance * self.scale**2
@property
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
return self.variance.sqrt()
class UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = args_dim
lowerCamelCase_ = nn.ModuleList([nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for dim in args_dim.values()] )
lowerCamelCase_ = domain_map
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple[torch.Tensor]:
'''simple docstring'''
lowerCamelCase_ = [proj(SCREAMING_SNAKE_CASE_ ) for proj in self.proj]
return self.domain_map(*SCREAMING_SNAKE_CASE_ )
class UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
super().__init__()
lowerCamelCase_ = function
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
return self.function(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
def __init__( self , SCREAMING_SNAKE_CASE_ = 1 ) -> None:
'''simple docstring'''
lowerCamelCase_ = dim
lowerCamelCase_ = {k: dim * self.args_dim[k] for k in self.args_dim}
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
if self.dim == 1:
return self.distribution_class(*SCREAMING_SNAKE_CASE_ )
else:
return Independent(self.distribution_class(*SCREAMING_SNAKE_CASE_ ) , 1 )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , ) -> Distribution:
'''simple docstring'''
lowerCamelCase_ = self._base_distribution(SCREAMING_SNAKE_CASE_ )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(SCREAMING_SNAKE_CASE_ , loc=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , event_dim=self.event_dim )
@property
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
return () if self.dim == 1 else (self.dim,)
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return len(self.event_shape )
@property
def UpperCamelCase( self ) -> float:
'''simple docstring'''
return 0.0
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> nn.Module:
'''simple docstring'''
return ParameterProjection(
in_features=SCREAMING_SNAKE_CASE_ , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def UpperCamelCase( self , *SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
raise NotImplementedError()
@staticmethod
def UpperCamelCase( SCREAMING_SNAKE_CASE_ ) -> torch.Tensor:
'''simple docstring'''
return (x + torch.sqrt(torch.square(SCREAMING_SNAKE_CASE_ ) + 4.0 )) / 2.0
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = {"df": 1, "loc": 1, "scale": 1}
SCREAMING_SNAKE_CASE_ = StudentT
@classmethod
def UpperCamelCase( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ = cls.squareplus(SCREAMING_SNAKE_CASE_ ).clamp_min(torch.finfo(scale.dtype ).eps )
lowerCamelCase_ = 2.0 + cls.squareplus(SCREAMING_SNAKE_CASE_ )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = {"loc": 1, "scale": 1}
SCREAMING_SNAKE_CASE_ = Normal
@classmethod
def UpperCamelCase( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ = cls.squareplus(SCREAMING_SNAKE_CASE_ ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = {"total_count": 1, "logits": 1}
SCREAMING_SNAKE_CASE_ = NegativeBinomial
@classmethod
def UpperCamelCase( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = cls.squareplus(SCREAMING_SNAKE_CASE_ )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Distribution:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = distr_args
if self.dim == 1:
return self.distribution_class(total_count=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ )
else:
return Independent(self.distribution_class(total_count=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ ) , 1 )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None ) -> Distribution:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 42 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ = 16
A_ = 32
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 16 ,__UpperCamelCase = "bert-base-cased" ) -> List[Any]:
lowerCamelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = load_dataset('glue' ,'mrpc' )
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase_ = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCamelCase_ = datasets.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,load_from_cache_file=__UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase_ = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(__UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__UpperCamelCase ,padding='max_length' ,max_length=1_28 ,return_tensors='pt' )
return tokenizer.pad(__UpperCamelCase ,padding='longest' ,return_tensors='pt' )
# Instantiate dataloaders.
lowerCamelCase_ = DataLoader(
tokenized_datasets['train'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
lowerCamelCase_ = DataLoader(
tokenized_datasets['validation'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
model.eval()
lowerCamelCase_ = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__UpperCamelCase ) - 1:
lowerCamelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCamelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__UpperCamelCase ,references=__UpperCamelCase ,)
lowerCamelCase_ = metric.compute()
return eval_metric["accuracy"]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[str]:
# Initialize accelerator
lowerCamelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase_ = config['lr']
lowerCamelCase_ = int(config['num_epochs'] )
lowerCamelCase_ = int(config['seed'] )
lowerCamelCase_ = int(config['batch_size'] )
lowerCamelCase_ = args.model_name_or_path
set_seed(__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = get_dataloaders(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,return_dict=__UpperCamelCase )
# Instantiate optimizer
lowerCamelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase_ = optimizer_cls(params=model.parameters() ,lr=__UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowerCamelCase_ = 1
lowerCamelCase_ = (len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase_ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase ,num_warmup_steps=0 ,num_training_steps=__UpperCamelCase ,)
else:
lowerCamelCase_ = DummyScheduler(__UpperCamelCase ,total_num_steps=__UpperCamelCase ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase_ = 0
lowerCamelCase_ = evaluate.load('glue' ,'mrpc' )
lowerCamelCase_ = num_epochs
if args.partial_train_epoch is not None:
lowerCamelCase_ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCamelCase_ = args.resume_from_checkpoint.split('epoch_' )[1]
lowerCamelCase_ = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCamelCase_ = int(__UpperCamelCase ) + 1
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
accelerator.print('resumed checkpoint performance:' ,__UpperCamelCase )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' ,lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' ,optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir ,f'''state_{starting_epoch-1}.json''' ) ,'r' ) as f:
lowerCamelCase_ = json.load(__UpperCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCamelCase_ = {}
for epoch in range(__UpperCamelCase ,__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.loss
lowerCamelCase_ = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCamelCase_ = f'''epoch_{epoch}'''
lowerCamelCase_ = os.path.join(args.output_dir ,__UpperCamelCase )
accelerator.save_state(__UpperCamelCase )
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = accuracy
lowerCamelCase_ = lr_scheduler.get_lr()[0]
lowerCamelCase_ = optimizer.param_groups[0]['lr']
lowerCamelCase_ = epoch
lowerCamelCase_ = overall_step
accelerator.print(f'''epoch {epoch}:''' ,__UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,f'''state_{epoch}.json''' ) ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> str:
lowerCamelCase_ = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' ,type=__UpperCamelCase ,default='bert-base-cased' ,help='Path to pretrained model or model identifier from huggingface.co/models.' ,required=__UpperCamelCase ,)
parser.add_argument(
'--output_dir' ,type=__UpperCamelCase ,default='.' ,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,)
parser.add_argument(
'--resume_from_checkpoint' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If the training should continue from a checkpoint folder.' ,)
parser.add_argument(
'--partial_train_epoch' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If passed, the training will stop after this number of epochs.' ,)
parser.add_argument(
'--num_epochs' ,type=__UpperCamelCase ,default=2 ,help='Number of train epochs.' ,)
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> tuple[int, int]:
try:
lowerCamelCase_ = float(__UpperCamelCase )
except ValueError:
raise ValueError('Please enter a valid number' )
lowerCamelCase_ = decimal - int(__UpperCamelCase )
if fractional_part == 0:
return int(__UpperCamelCase ), 1
else:
lowerCamelCase_ = len(str(__UpperCamelCase ).split('.' )[1] )
lowerCamelCase_ = int(decimal * (10**number_of_frac_digits) )
lowerCamelCase_ = 10**number_of_frac_digits
lowerCamelCase_ ,lowerCamelCase_ = denominator, numerator
while True:
lowerCamelCase_ = dividend % divisor
if remainder == 0:
break
lowerCamelCase_ ,lowerCamelCase_ = divisor, remainder
lowerCamelCase_ ,lowerCamelCase_ = numerator / divisor, denominator / divisor
return int(__UpperCamelCase ), int(__UpperCamelCase )
if __name__ == "__main__":
print(f'''{decimal_to_fraction(2) = }''')
print(f'''{decimal_to_fraction(89.0) = }''')
print(f'''{decimal_to_fraction("67") = }''')
print(f'''{decimal_to_fraction("45.0") = }''')
print(f'''{decimal_to_fraction(1.5) = }''')
print(f'''{decimal_to_fraction("6.25") = }''')
print(f'''{decimal_to_fraction("78td") = }''')
| 42 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> np.ndarray:
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
lowerCamelCase_ = ksize + 1
lowerCamelCase_ = np.zeros((ksize, ksize) ,dtype=np.floataa )
# each value
for y in range(__UpperCamelCase ):
for x in range(__UpperCamelCase ):
# distance from center
lowerCamelCase_ = x - ksize // 2
lowerCamelCase_ = y - ksize // 2
# degree to radiant
lowerCamelCase_ = theta / 1_80 * np.pi
lowerCamelCase_ = np.cos(_theta )
lowerCamelCase_ = np.sin(_theta )
# get kernel x
lowerCamelCase_ = cos_theta * px + sin_theta * py
# get kernel y
lowerCamelCase_ = -sin_theta * px + cos_theta * py
# fill kernel
lowerCamelCase_ = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
A_ = imread("../image_data/lena.jpg")
# turn image in gray scale value
A_ = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
A_ = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
A_ = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
A_ = out / out.max() * 255
A_ = out.astype(np.uinta)
imshow("Original", gray)
imshow("Gabor filter with 20x20 mask and 6 directions", out)
waitKey(0)
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
A_ = 1.6_0_2_1E-1_9 # units = C
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif conductivity < 0:
raise ValueError('Conductivity cannot be negative' )
elif electron_conc < 0:
raise ValueError('Electron concentration cannot be negative' )
elif mobility < 0:
raise ValueError('mobility cannot be negative' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['transformers', 'torch', 'note_seq']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 42 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['flax'] )
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['flax'] )
| 42 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
lowerCamelCase_ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
lowerCamelCase_ = DDPMScheduler()
lowerCamelCase_ = AudioDiffusionPipeline(vqvae=SCREAMING_SNAKE_CASE_ , unet=self.dummy_unet , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 , return_dict=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = self.dummy_vqvae_and_unet
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(raw_audio=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , start_step=5 , steps=10 )
lowerCamelCase_ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = self.dummy_unet_condition
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=SCREAMING_SNAKE_CASE_ , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = torch.rand((1, 1, 10) )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , encoding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = torch_device
lowerCamelCase_ = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = u
for i in range(1 ,__UpperCamelCase ):
lowerCamelCase_ = temp * (u - i)
return temp
def _UpperCamelCase ( ) -> None:
lowerCamelCase_ = int(input('enter the numbers of values: ' ) )
lowerCamelCase_ = []
for _ in range(__UpperCamelCase ):
y.append([] )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
y[i].append(__UpperCamelCase )
lowerCamelCase_ = 0
print('enter the values of parameters in a list: ' )
lowerCamelCase_ = list(map(__UpperCamelCase ,input().split() ) )
print('enter the values of corresponding parameters: ' )
for i in range(__UpperCamelCase ):
lowerCamelCase_ = float(input() )
lowerCamelCase_ = int(input('enter the value to interpolate: ' ) )
lowerCamelCase_ = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 ,__UpperCamelCase ):
for j in range(n - i ):
lowerCamelCase_ = y[j + 1][i - 1] - y[j][i - 1]
lowerCamelCase_ = y[0][0]
for i in range(1 ,__UpperCamelCase ):
summ += (ucal(__UpperCamelCase ,__UpperCamelCase ) * y[0][i]) / math.factorial(__UpperCamelCase )
print(f'''the value at {value} is {summ}''' )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _UpperCamelCase ( __UpperCamelCase = 8 ) -> str:
lowerCamelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(__UpperCamelCase )
lowerCamelCase_ = i // 3
lowerCamelCase_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowerCamelCase_ = (
chars_incl
+ random(__UpperCamelCase ,quotient + remainder )
+ random(__UpperCamelCase ,__UpperCamelCase )
+ random(__UpperCamelCase ,__UpperCamelCase )
)
lowerCamelCase_ = list(__UpperCamelCase )
shuffle(__UpperCamelCase )
return "".join(__UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Tuple:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 8 ) -> bool:
if len(__UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowerCamelCase_ = any(char in ascii_uppercase for char in password )
lowerCamelCase_ = any(char in ascii_lowercase for char in password )
lowerCamelCase_ = any(char in digits for char in password )
lowerCamelCase_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _UpperCamelCase ( ) -> Optional[int]:
lowerCamelCase_ = int(input('Please indicate the max length of your password: ' ).strip() )
lowerCamelCase_ = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' ,password_generator(__UpperCamelCase ) )
print(
'Alternative Password generated:' ,alternative_password_generator(__UpperCamelCase ,__UpperCamelCase ) ,)
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'rwkv'
SCREAMING_SNAKE_CASE_ = {'max_position_embeddings': 'context_length'}
def __init__( self , SCREAMING_SNAKE_CASE_=50277 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = context_length
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = attention_hidden_size if attention_hidden_size is not None else hidden_size
lowerCamelCase_ = intermediate_size if intermediate_size is not None else 4 * hidden_size
lowerCamelCase_ = layer_norm_epsilon
lowerCamelCase_ = rescale_every
lowerCamelCase_ = use_cache
lowerCamelCase_ = bos_token_id
lowerCamelCase_ = eos_token_id
super().__init__(
tie_word_embeddings=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
| 42 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowerCamelCase_ = test_metrics
@require_cpu
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
debug_launcher(self.test_metrics.main )
@require_single_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
self.test_metrics.main()
@require_multi_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices.''' )
lowerCamelCase_ = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=os.environ.copy() )
| 42 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
A_ = 50_003
A_ = 50_002
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = PLBartTokenizer
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = False
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = PLBartTokenizer(SCREAMING_SNAKE_CASE_ , language_codes='base' , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = PLBartTokenizer(SCREAMING_SNAKE_CASE_ , language_codes='base' , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
lowerCamelCase_ = tokenizer.vocab_size
lowerCamelCase_ = [tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ) for x in range(end - 4 , SCREAMING_SNAKE_CASE_ )]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['__java__', '__python__', '__en_XX__', '<mask>'] )
lowerCamelCase_ = 'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ ).input_ids
self.assertEqual(
tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = PLBartTokenizer(SCREAMING_SNAKE_CASE_ , language_codes='multi' , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
lowerCamelCase_ = tokenizer.vocab_size
lowerCamelCase_ = [tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ) for x in range(end - 7 , SCREAMING_SNAKE_CASE_ )]
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , ['__java__', '__python__', '__en_XX__', '__javascript__', '__php__', '__ruby__', '__go__'] )
lowerCamelCase_ = 'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ ).input_ids
self.assertEqual(
tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'uclanlp/plbart-python-en_XX'
SCREAMING_SNAKE_CASE_ = [
'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
]
SCREAMING_SNAKE_CASE_ = [
'Returns the maximum value of a b c.',
'Sums the values of a b c.',
]
SCREAMING_SNAKE_CASE_ = [
1_34,
54_52,
3_34_60,
3_34_41,
3_34_63,
3_34_65,
3_34_63,
3_34_49,
9_88,
20,
3_34_56,
19,
3_34_56,
7_71,
39,
42_58,
8_89,
33_18,
3_34_41,
3_34_63,
3_34_65,
3_34_63,
3_34_49,
24_71,
2,
PYTHON_CODE,
]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes='base' , src_lang='python' , tgt_lang='en_XX' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__'] , 50001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__'] , 50002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__'] , 50003 )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = ['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
self.assertIsInstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__'] ) , [50004, 50001] )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = PLBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='java' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[150, 242, 2, 50003]],
'attention_mask': [[1, 1, 1, 1]],
# java
'forced_bos_token_id': 50001,
} , )
| 42 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
if hor == 1_28:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 64, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
lowerCamelCase_ = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
lowerCamelCase_ = model.state_dict()
lowerCamelCase_ = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 6_55_36,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> Tuple:
lowerCamelCase_ = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 1_28, 2_56),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 6_55_36,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
lowerCamelCase_ = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
lowerCamelCase_ = model
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin' )
with open('hub/hopper-medium-v2/value_function/config.json' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 42 | 1 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
lowerCamelCase_ = sorted(zip(__UpperCamelCase ,__UpperCamelCase ) ,key=lambda __UpperCamelCase : x[0] / x[1] ,reverse=__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = [i[0] for i in r], [i[1] for i in r]
lowerCamelCase_ = list(accumulate(__UpperCamelCase ) )
lowerCamelCase_ = bisect(__UpperCamelCase ,__UpperCamelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A_ = 250_004
A_ = 250_020
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MBartTokenizer
SCREAMING_SNAKE_CASE_ = MBartTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/mbart-large-en-ro'
SCREAMING_SNAKE_CASE_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE_ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 250020 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 42 | 1 |
'''simple docstring'''
import pprint
import requests
A_ = "https://zenquotes.io/api"
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
A_ = random_quotes()
pprint.pprint(response)
| 42 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'ylacombe/bark-small'
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = 'en_speaker_1'
lowerCamelCase_ = 'This is a test string'
lowerCamelCase_ = 'speaker_embeddings_path.json'
lowerCamelCase_ = 'speaker_embeddings'
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowerCamelCase_ = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCamelCase_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowerCamelCase_ = 35
lowerCamelCase_ = 2
lowerCamelCase_ = 8
lowerCamelCase_ = {
'semantic_prompt': np.ones(SCREAMING_SNAKE_CASE_ ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase_ = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string )
lowerCamelCase_ = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 42 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A_ = logging.get_logger()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = True ) -> List[str]:
print(f'''Converting {name}...''' )
with torch.no_grad():
if hidden_sizes == 1_28:
if name[-1] == "S":
lowerCamelCase_ = timm.create_model('levit_128s' ,pretrained=__UpperCamelCase )
else:
lowerCamelCase_ = timm.create_model('levit_128' ,pretrained=__UpperCamelCase )
if hidden_sizes == 1_92:
lowerCamelCase_ = timm.create_model('levit_192' ,pretrained=__UpperCamelCase )
if hidden_sizes == 2_56:
lowerCamelCase_ = timm.create_model('levit_256' ,pretrained=__UpperCamelCase )
if hidden_sizes == 3_84:
lowerCamelCase_ = timm.create_model('levit_384' ,pretrained=__UpperCamelCase )
from_model.eval()
lowerCamelCase_ = LevitForImageClassificationWithTeacher(__UpperCamelCase ).eval()
lowerCamelCase_ = OrderedDict()
lowerCamelCase_ = from_model.state_dict()
lowerCamelCase_ = list(from_model.state_dict().keys() )
lowerCamelCase_ = list(our_model.state_dict().keys() )
print(len(__UpperCamelCase ) ,len(__UpperCamelCase ) )
for i in range(len(__UpperCamelCase ) ):
lowerCamelCase_ = weights[og_keys[i]]
our_model.load_state_dict(__UpperCamelCase )
lowerCamelCase_ = torch.randn((2, 3, 2_24, 2_24) )
lowerCamelCase_ = from_model(__UpperCamelCase )
lowerCamelCase_ = our_model(__UpperCamelCase ).logits
assert torch.allclose(__UpperCamelCase ,__UpperCamelCase ), "The model logits don't match the original one."
lowerCamelCase_ = name
print(__UpperCamelCase )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
lowerCamelCase_ = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f'''Pushed {checkpoint_name}''' )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = None ,__UpperCamelCase = True ) -> Optional[Any]:
lowerCamelCase_ = 'imagenet-1k-id2label.json'
lowerCamelCase_ = 10_00
lowerCamelCase_ = (1, num_labels)
lowerCamelCase_ = 'huggingface/label-files'
lowerCamelCase_ = num_labels
lowerCamelCase_ = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type='dataset' ) ,'r' ) )
lowerCamelCase_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
lowerCamelCase_ = idalabel
lowerCamelCase_ = {v: k for k, v in idalabel.items()}
lowerCamelCase_ = partial(__UpperCamelCase ,num_labels=__UpperCamelCase ,idalabel=__UpperCamelCase ,labelaid=__UpperCamelCase )
lowerCamelCase_ = {
'levit-128S': 1_28,
'levit-128': 1_28,
'levit-192': 1_92,
'levit-256': 2_56,
'levit-384': 3_84,
}
lowerCamelCase_ = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84] ,num_attention_heads=[4, 6, 8] ,depths=[2, 3, 4] ,key_dim=[16, 16, 16] ,drop_path_rate=0 ,),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84] ,num_attention_heads=[4, 8, 12] ,depths=[4, 4, 4] ,key_dim=[16, 16, 16] ,drop_path_rate=0 ,),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[1_92, 2_88, 3_84] ,num_attention_heads=[3, 5, 6] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0 ,),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[2_56, 3_84, 5_12] ,num_attention_heads=[4, 6, 8] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0 ,),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[3_84, 5_12, 7_68] ,num_attention_heads=[6, 9, 12] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0.1 ,),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] ,__UpperCamelCase ,names_to_config[model_name] ,__UpperCamelCase ,__UpperCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
return config, expected_shape
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
A_ = parser.parse_args()
A_ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 42 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
A_ = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
A_ = {
"allenai/longformer-base-4096": 4_096,
"allenai/longformer-large-4096": 4_096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = (
list(range(ord('!' ) ,ord('~' ) + 1 ) ) + list(range(ord('¡' ) ,ord('¬' ) + 1 ) ) + list(range(ord('®' ) ,ord('ÿ' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase ,__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as vocab_handle:
lowerCamelCase_ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ ,lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCamelCase_ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ' '.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = word
return word
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(' ' ) )
return bpe_tokens
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = ''.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '\n' )
lowerCamelCase_ = 0
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase_ = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE_ ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ' ' + text
return (text, kwargs)
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["GPTSw3Tokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A_ = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
A_ = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
A_ = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = RealmTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="[UNK]" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="[PAD]" , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[MASK]" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , do_lower_case=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , tokenize_chinese_chars=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , SCREAMING_SNAKE_CASE_ ) != do_lower_case
or normalizer_state.get('strip_accents' , SCREAMING_SNAKE_CASE_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , normalizer_state.pop('type' ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = do_lower_case
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = PaddingStrategy.MAX_LENGTH
lowerCamelCase_ = text
lowerCamelCase_ = kwargs.pop('text_pair' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = kwargs.pop('return_tensors' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(SCREAMING_SNAKE_CASE_ ):
if batch_text_pair is not None:
lowerCamelCase_ = batch_text_pair[idx]
else:
lowerCamelCase_ = None
lowerCamelCase_ = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoded_candidates.get('input_ids' )
lowerCamelCase_ = encoded_candidates.get('attention_mask' )
lowerCamelCase_ = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(SCREAMING_SNAKE_CASE_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(SCREAMING_SNAKE_CASE_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {key: item for key, item in output_data.items() if len(SCREAMING_SNAKE_CASE_ ) != 0}
return BatchEncoding(SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE_ = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
SCREAMING_SNAKE_CASE_ = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
SCREAMING_SNAKE_CASE_ = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
SCREAMING_SNAKE_CASE_ = False
@property
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
return 32
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
return 32
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.time_input_dim
@property
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return self.time_input_dim * 4
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
return 100
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowerCamelCase_ = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_ )
return model
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def UpperCamelCase( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = self.dummy_unet
lowerCamelCase_ = self.dummy_movq
lowerCamelCase_ = {
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.00_085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
lowerCamelCase_ = DDIMScheduler(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
SCREAMING_SNAKE_CASE_ )
# create init_image
lowerCamelCase_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase_ = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert('RGB' ).resize((256, 256) )
# create hint
lowerCamelCase_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith('mps' ):
lowerCamelCase_ = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = 'cpu'
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase_ = output.images
lowerCamelCase_ = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
lowerCamelCase_ = image[0, -3:, -3:, -1]
lowerCamelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ = np.array(
[0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy' )
lowerCamelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
lowerCamelCase_ = init_image.resize((512, 512) )
lowerCamelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
lowerCamelCase_ = torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_ ) ).float() / 255.0
lowerCamelCase_ = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowerCamelCase_ = 'A robot, 4k photo'
lowerCamelCase_ = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth' , torch_dtype=torch.floataa )
lowerCamelCase_ = pipeline.to(SCREAMING_SNAKE_CASE_ )
pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCamelCase_ ,lowerCamelCase_ = pipe_prior(
SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , strength=0.85 , generator=SCREAMING_SNAKE_CASE_ , negative_prompt='' , ).to_tuple()
lowerCamelCase_ = pipeline(
image=SCREAMING_SNAKE_CASE_ , image_embeds=SCREAMING_SNAKE_CASE_ , negative_image_embeds=SCREAMING_SNAKE_CASE_ , hint=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=100 , height=512 , width=512 , strength=0.5 , output_type='np' , )
lowerCamelCase_ = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 42 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = str(__UpperCamelCase )
return len(__UpperCamelCase ) == 9 and set(__UpperCamelCase ) == set('123456789' )
def _UpperCamelCase ( ) -> int | None:
for base_num in range(99_99 ,49_99 ,-1 ):
lowerCamelCase_ = 10_00_02 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
for base_num in range(3_33 ,99 ,-1 ):
lowerCamelCase_ = 1_00_20_03 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['image_processor', 'feature_extractor']
SCREAMING_SNAKE_CASE_ = 'TvltImageProcessor'
SCREAMING_SNAKE_CASE_ = 'TvltFeatureExtractor'
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
super().__init__(image_processor=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = image_processor
lowerCamelCase_ = feature_extractor
def __call__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
'''simple docstring'''
if images is None and audio is None:
raise ValueError('You need to specify either an `images` or `audio` input to process.' )
lowerCamelCase_ = None
if images is not None:
lowerCamelCase_ = self.image_processor(SCREAMING_SNAKE_CASE_ , mask_pixel=SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if images_mixed is not None:
lowerCamelCase_ = self.image_processor(SCREAMING_SNAKE_CASE_ , is_mixed=SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
lowerCamelCase_ = self.feature_extractor(
SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , mask_audio=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {}
if audio is not None:
output_dict.update(SCREAMING_SNAKE_CASE_ )
if images is not None:
output_dict.update(SCREAMING_SNAKE_CASE_ )
if images_mixed_dict is not None:
output_dict.update(SCREAMING_SNAKE_CASE_ )
return output_dict
@property
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.image_processor.model_input_names
lowerCamelCase_ = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 42 |
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = spanish_id.replace('-' ,'' ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
lowerCamelCase_ = int(spanish_id_clean[0:8] )
lowerCamelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(__UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 1 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class UpperCAmelCase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def UpperCamelCase( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
if PartialState._shared_state == {}:
raise RuntimeError(
'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.' )
lowerCamelCase_ = kwargs.pop('main_process_only' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = kwargs.pop('in_order' , SCREAMING_SNAKE_CASE_ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE_ ):
if self._should_log(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ ,lowerCamelCase_ = self.process(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.logger.log(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
elif in_order:
lowerCamelCase_ = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowerCamelCase_ ,lowerCamelCase_ = self.process(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.logger.log(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
state.wait_for_everyone()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = None ) -> Optional[int]:
if log_level is None:
lowerCamelCase_ = os.environ.get('ACCELERATE_LOG_LEVEL' ,__UpperCamelCase )
lowerCamelCase_ = logging.getLogger(__UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__UpperCamelCase ,{} )
| 42 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ = 16
A_ = 32
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 16 ,__UpperCamelCase = "bert-base-cased" ) -> List[Any]:
lowerCamelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = load_dataset('glue' ,'mrpc' )
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase_ = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCamelCase_ = datasets.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,load_from_cache_file=__UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase_ = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(__UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__UpperCamelCase ,padding='max_length' ,max_length=1_28 ,return_tensors='pt' )
return tokenizer.pad(__UpperCamelCase ,padding='longest' ,return_tensors='pt' )
# Instantiate dataloaders.
lowerCamelCase_ = DataLoader(
tokenized_datasets['train'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
lowerCamelCase_ = DataLoader(
tokenized_datasets['validation'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
model.eval()
lowerCamelCase_ = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__UpperCamelCase ) - 1:
lowerCamelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCamelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__UpperCamelCase ,references=__UpperCamelCase ,)
lowerCamelCase_ = metric.compute()
return eval_metric["accuracy"]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[str]:
# Initialize accelerator
lowerCamelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase_ = config['lr']
lowerCamelCase_ = int(config['num_epochs'] )
lowerCamelCase_ = int(config['seed'] )
lowerCamelCase_ = int(config['batch_size'] )
lowerCamelCase_ = args.model_name_or_path
set_seed(__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = get_dataloaders(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,return_dict=__UpperCamelCase )
# Instantiate optimizer
lowerCamelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase_ = optimizer_cls(params=model.parameters() ,lr=__UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowerCamelCase_ = 1
lowerCamelCase_ = (len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase_ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase ,num_warmup_steps=0 ,num_training_steps=__UpperCamelCase ,)
else:
lowerCamelCase_ = DummyScheduler(__UpperCamelCase ,total_num_steps=__UpperCamelCase ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase_ = 0
lowerCamelCase_ = evaluate.load('glue' ,'mrpc' )
lowerCamelCase_ = num_epochs
if args.partial_train_epoch is not None:
lowerCamelCase_ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCamelCase_ = args.resume_from_checkpoint.split('epoch_' )[1]
lowerCamelCase_ = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCamelCase_ = int(__UpperCamelCase ) + 1
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
accelerator.print('resumed checkpoint performance:' ,__UpperCamelCase )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' ,lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' ,optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir ,f'''state_{starting_epoch-1}.json''' ) ,'r' ) as f:
lowerCamelCase_ = json.load(__UpperCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCamelCase_ = {}
for epoch in range(__UpperCamelCase ,__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.loss
lowerCamelCase_ = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCamelCase_ = f'''epoch_{epoch}'''
lowerCamelCase_ = os.path.join(args.output_dir ,__UpperCamelCase )
accelerator.save_state(__UpperCamelCase )
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = accuracy
lowerCamelCase_ = lr_scheduler.get_lr()[0]
lowerCamelCase_ = optimizer.param_groups[0]['lr']
lowerCamelCase_ = epoch
lowerCamelCase_ = overall_step
accelerator.print(f'''epoch {epoch}:''' ,__UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,f'''state_{epoch}.json''' ) ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> str:
lowerCamelCase_ = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' ,type=__UpperCamelCase ,default='bert-base-cased' ,help='Path to pretrained model or model identifier from huggingface.co/models.' ,required=__UpperCamelCase ,)
parser.add_argument(
'--output_dir' ,type=__UpperCamelCase ,default='.' ,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,)
parser.add_argument(
'--resume_from_checkpoint' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If the training should continue from a checkpoint folder.' ,)
parser.add_argument(
'--partial_train_epoch' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If passed, the training will stop after this number of epochs.' ,)
parser.add_argument(
'--num_epochs' ,type=__UpperCamelCase ,default=2 ,help='Number of train epochs.' ,)
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = "auto" ) -> List[str]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = 7.5 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
# get prompt text embeddings
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
lowerCamelCase_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase_ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowerCamelCase_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = text_embeddings.shape
lowerCamelCase_ = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ = 42
if negative_prompt is None:
lowerCamelCase_ = ['']
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = text_input_ids.shape[-1]
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ = uncond_embeddings.shape[1]
lowerCamelCase_ = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowerCamelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase_ = latents_reference.to(self.device )
lowerCamelCase_ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowerCamelCase_ = (latents_shape[3] - latents_shape_reference[3]) // 2
lowerCamelCase_ = (latents_shape[2] - latents_shape_reference[2]) // 2
lowerCamelCase_ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowerCamelCase_ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowerCamelCase_ = 0 if dx < 0 else dx
lowerCamelCase_ = 0 if dy < 0 else dy
lowerCamelCase_ = max(-dx , 0 )
lowerCamelCase_ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowerCamelCase_ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase_ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 1 / 0.18_215 * latents
lowerCamelCase_ = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowerCamelCase_ = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) , return_tensors='pt' ).to(
self.device )
lowerCamelCase_ ,lowerCamelCase_ = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowerCamelCase_ = None
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ , nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = None
class UpperCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = PandasConfig
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowerCamelCase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(SCREAMING_SNAKE_CASE_ , (str, list, tuple) ):
lowerCamelCase_ = data_files
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCamelCase_ = [dl_manager.iter_files(SCREAMING_SNAKE_CASE_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
lowerCamelCase_ = []
for split_name, files in data_files.items():
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCamelCase_ = [dl_manager.iter_files(SCREAMING_SNAKE_CASE_ ) for file in files]
splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_ , gen_kwargs={'files': files} ) )
return splits
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> pa.Table:
'''simple docstring'''
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowerCamelCase_ = table_cast(SCREAMING_SNAKE_CASE_ , self.config.features.arrow_schema )
return pa_table
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_ ) ):
with open(SCREAMING_SNAKE_CASE_ , 'rb' ) as f:
lowerCamelCase_ = pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE_ ) )
yield i, self._cast_table(SCREAMING_SNAKE_CASE_ )
| 42 |
'''simple docstring'''
import pprint
import requests
A_ = "https://zenquotes.io/api"
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
A_ = random_quotes()
pprint.pprint(response)
| 42 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'xlm-prophetnet'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 30522 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2 , **SCREAMING_SNAKE_CASE_ , ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = num_encoder_layers
lowerCamelCase_ = num_encoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = num_decoder_layers
lowerCamelCase_ = num_decoder_attention_heads
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = init_std # Normal(0, this parameter)
lowerCamelCase_ = activation_function
# parameters for xlmprophetnet
lowerCamelCase_ = ngram
lowerCamelCase_ = num_buckets
lowerCamelCase_ = relative_max_distance
lowerCamelCase_ = disable_ngram_loss
lowerCamelCase_ = eps
# 3 Types of Dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = dropout
lowerCamelCase_ = use_cache
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , add_cross_attention=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 42 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=33 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ) -> int:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = EsmModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = EsmForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = EsmForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = ()
SCREAMING_SNAKE_CASE_ = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = EsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase_ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = EsmModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowerCamelCase_ = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowerCamelCase_ = create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE_ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.empty(2 , 4 , 30 )
lowerCamelCase_ = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowerCamelCase_ = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowerCamelCase_ = embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE_ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = 33
lowerCamelCase_ = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
# compare the actual values for a slice.
lowerCamelCase_ = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 | 1 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'data2vec-audio'
def __init__( self , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=(512, 512, 512, 512, 512, 512, 512) , SCREAMING_SNAKE_CASE_=(5, 2, 2, 2, 2, 2, 2) , SCREAMING_SNAKE_CASE_=(10, 3, 3, 3, 3, 2, 2) , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=19 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=0.05 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_="sum" , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=(512, 512, 512, 512, 1500) , SCREAMING_SNAKE_CASE_=(5, 3, 3, 1, 1) , SCREAMING_SNAKE_CASE_=(1, 2, 3, 1, 1) , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = hidden_size
lowerCamelCase_ = feat_extract_activation
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = conv_bias
lowerCamelCase_ = num_conv_pos_embeddings
lowerCamelCase_ = num_conv_pos_embedding_groups
lowerCamelCase_ = conv_pos_kernel_size
lowerCamelCase_ = len(self.conv_dim )
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = hidden_dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = feat_proj_dropout
lowerCamelCase_ = final_dropout
lowerCamelCase_ = layerdrop
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = initializer_range
lowerCamelCase_ = vocab_size
lowerCamelCase_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase_ = mask_time_prob
lowerCamelCase_ = mask_time_length
lowerCamelCase_ = mask_time_min_masks
lowerCamelCase_ = mask_feature_prob
lowerCamelCase_ = mask_feature_length
lowerCamelCase_ = mask_feature_min_masks
# ctc loss
lowerCamelCase_ = ctc_loss_reduction
lowerCamelCase_ = ctc_zero_infinity
# adapter
lowerCamelCase_ = add_adapter
lowerCamelCase_ = adapter_kernel_size
lowerCamelCase_ = adapter_stride
lowerCamelCase_ = num_adapter_layers
lowerCamelCase_ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowerCamelCase_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = xvector_output_dim
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
return math.prod(self.conv_stride )
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 42 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'glpn'
def __init__( self , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=[2, 2, 2, 2] , SCREAMING_SNAKE_CASE_=[8, 4, 2, 1] , SCREAMING_SNAKE_CASE_=[32, 64, 160, 256] , SCREAMING_SNAKE_CASE_=[7, 3, 3, 3] , SCREAMING_SNAKE_CASE_=[4, 2, 2, 2] , SCREAMING_SNAKE_CASE_=[1, 2, 5, 8] , SCREAMING_SNAKE_CASE_=[4, 4, 4, 4] , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=1E-6 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=-1 , **SCREAMING_SNAKE_CASE_ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = num_channels
lowerCamelCase_ = num_encoder_blocks
lowerCamelCase_ = depths
lowerCamelCase_ = sr_ratios
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = patch_sizes
lowerCamelCase_ = strides
lowerCamelCase_ = mlp_ratios
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = initializer_range
lowerCamelCase_ = drop_path_rate
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = decoder_hidden_size
lowerCamelCase_ = max_depth
lowerCamelCase_ = head_in_index
| 42 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'xlm-prophetnet'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 30522 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2 , **SCREAMING_SNAKE_CASE_ , ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = num_encoder_layers
lowerCamelCase_ = num_encoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = num_decoder_layers
lowerCamelCase_ = num_decoder_attention_heads
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = init_std # Normal(0, this parameter)
lowerCamelCase_ = activation_function
# parameters for xlmprophetnet
lowerCamelCase_ = ngram
lowerCamelCase_ = num_buckets
lowerCamelCase_ = relative_max_distance
lowerCamelCase_ = disable_ngram_loss
lowerCamelCase_ = eps
# 3 Types of Dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = dropout
lowerCamelCase_ = use_cache
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , add_cross_attention=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 42 | 1 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> int:
assert isinstance(__UpperCamelCase ,__UpperCamelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' ,[False, True] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> List[str]:
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,cache_dir=__UpperCamelCase ,keep_in_memory=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase ,__UpperCamelCase )
@pytest.mark.parametrize(
'features' ,[
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] ,)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Dict:
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
lowerCamelCase_ = features.copy() if features else default_expected_features
lowerCamelCase_ = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,features=__UpperCamelCase ,cache_dir=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase ,__UpperCamelCase )
@pytest.mark.parametrize(
'features' ,[
None,
{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
] ,)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
lowerCamelCase_ = features.copy() if features else default_expected_features
lowerCamelCase_ = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,features=__UpperCamelCase ,cache_dir=__UpperCamelCase ).read()
assert isinstance(__UpperCamelCase ,__UpperCamelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
lowerCamelCase_ = {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
lowerCamelCase_ = features.copy()
lowerCamelCase_ = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,features=__UpperCamelCase ,cache_dir=__UpperCamelCase ).read()
assert isinstance(__UpperCamelCase ,__UpperCamelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('split' ,[None, NamedSplit('train' ), 'train', 'test'] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,cache_dir=__UpperCamelCase ,split=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase ,__UpperCamelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' ,[str, list] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Optional[Any]:
if issubclass(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = jsonl_path
elif issubclass(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = [jsonl_path]
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,cache_dir=__UpperCamelCase ).read()
_check_json_dataset(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=("train",) ) -> Optional[int]:
assert isinstance(__UpperCamelCase ,__UpperCamelCase )
for split in splits:
lowerCamelCase_ = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' ,[False, True] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Dict:
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCamelCase_ = JsonDatasetReader({'train': jsonl_path} ,cache_dir=__UpperCamelCase ,keep_in_memory=__UpperCamelCase ).read()
_check_json_datasetdict(__UpperCamelCase ,__UpperCamelCase )
@pytest.mark.parametrize(
'features' ,[
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] ,)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Any:
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
lowerCamelCase_ = features.copy() if features else default_expected_features
lowerCamelCase_ = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCamelCase_ = JsonDatasetReader({'train': jsonl_path} ,features=__UpperCamelCase ,cache_dir=__UpperCamelCase ).read()
_check_json_datasetdict(__UpperCamelCase ,__UpperCamelCase )
@pytest.mark.parametrize('split' ,[None, NamedSplit('train' ), 'train', 'test'] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Any:
if split:
lowerCamelCase_ = {split: jsonl_path}
else:
lowerCamelCase_ = 'train'
lowerCamelCase_ = {'train': jsonl_path, 'test': jsonl_path}
lowerCamelCase_ = tmp_path / 'cache'
lowerCamelCase_ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
lowerCamelCase_ = JsonDatasetReader(__UpperCamelCase ,cache_dir=__UpperCamelCase ).read()
_check_json_datasetdict(__UpperCamelCase ,__UpperCamelCase ,splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def _UpperCamelCase ( __UpperCamelCase ) -> Union[str, Any]:
return json.load(__UpperCamelCase )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
return [json.loads(__UpperCamelCase ) for line in buffer]
class UpperCAmelCase :
'''simple docstring'''
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , lines=SCREAMING_SNAKE_CASE_ ).write()
buffer.seek(0 )
lowerCamelCase_ = load_json_function(SCREAMING_SNAKE_CASE_ )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert isinstance(exported_content[0] , SCREAMING_SNAKE_CASE_ )
assert len(SCREAMING_SNAKE_CASE_ ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , lines=SCREAMING_SNAKE_CASE_ , orient=SCREAMING_SNAKE_CASE_ ).write()
buffer.seek(0 )
lowerCamelCase_ = load_json(SCREAMING_SNAKE_CASE_ )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(SCREAMING_SNAKE_CASE_ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(SCREAMING_SNAKE_CASE_ ) == 10
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , lines=SCREAMING_SNAKE_CASE_ , num_proc=2 ).write()
buffer.seek(0 )
lowerCamelCase_ = load_json_function(SCREAMING_SNAKE_CASE_ )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert isinstance(exported_content[0] , SCREAMING_SNAKE_CASE_ )
assert len(SCREAMING_SNAKE_CASE_ ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , lines=SCREAMING_SNAKE_CASE_ , orient=SCREAMING_SNAKE_CASE_ , num_proc=2 ).write()
buffer.seek(0 )
lowerCamelCase_ = load_json(SCREAMING_SNAKE_CASE_ )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(SCREAMING_SNAKE_CASE_ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(SCREAMING_SNAKE_CASE_ ) == 10
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , num_proc=0 )
@pytest.mark.parametrize('compression, extension' , [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')] )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = tmp_path_factory.mktemp('data' ) / f'''test.json.{extension}'''
lowerCamelCase_ = str(shared_datadir / f'''test_file.json.{extension}''' )
JsonDatasetWriter(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , compression=SCREAMING_SNAKE_CASE_ ).write()
with fsspec.open(SCREAMING_SNAKE_CASE_ , 'rb' , compression='infer' ) as f:
lowerCamelCase_ = f.read()
with fsspec.open(SCREAMING_SNAKE_CASE_ , 'rb' , compression='infer' ) as f:
lowerCamelCase_ = f.read()
assert exported_content == original_content
| 42 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = x
lowerCamelCase_ = y
for step in range(__UpperCamelCase ): # noqa: B007
lowerCamelCase_ = a * a - b * b + x
lowerCamelCase_ = 2 * a * b + y
lowerCamelCase_ = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55 ) for i in colorsys.hsv_to_rgb(__UpperCamelCase ,1 ,1 ) )
def _UpperCamelCase ( __UpperCamelCase = 8_00 ,__UpperCamelCase = 6_00 ,__UpperCamelCase = -0.6 ,__UpperCamelCase = 0 ,__UpperCamelCase = 3.2 ,__UpperCamelCase = 50 ,__UpperCamelCase = True ,) -> Image.Image:
lowerCamelCase_ = Image.new('RGB' ,(image_width, image_height) )
lowerCamelCase_ = img.load()
# loop through the image-coordinates
for image_x in range(__UpperCamelCase ):
for image_y in range(__UpperCamelCase ):
# determine the figure-coordinates based on the image-coordinates
lowerCamelCase_ = figure_width / image_width * image_height
lowerCamelCase_ = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowerCamelCase_ = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowerCamelCase_ = get_distance(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowerCamelCase_ = get_color_coded_rgb(__UpperCamelCase )
else:
lowerCamelCase_ = get_black_and_white_rgb(__UpperCamelCase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
A_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 42 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
# Construct model
if openai_config_file == "":
lowerCamelCase_ = OpenAIGPTConfig()
else:
lowerCamelCase_ = OpenAIGPTConfig.from_json_file(__UpperCamelCase )
lowerCamelCase_ = OpenAIGPTModel(__UpperCamelCase )
# Load weights from numpy
load_tf_weights_in_openai_gpt(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Save pytorch-model
lowerCamelCase_ = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
lowerCamelCase_ = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() ,__UpperCamelCase )
print(f'''Save configuration file to {pytorch_config_dump_path}''' )
with open(__UpperCamelCase ,'w' ,encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--openai_checkpoint_folder_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--openai_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
A_ = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 42 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase_ = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase_ = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase_ = outgoing_gradient**2 + 4
lowerCamelCase_ = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase_ = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase_ = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase_ = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase_ = x_minus if isclose(__UpperCamelCase ,__UpperCamelCase ) else x_plus
lowerCamelCase_ = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _UpperCamelCase ( __UpperCamelCase = 1.4 ,__UpperCamelCase = -9.6 ) -> int:
lowerCamelCase_ = 0
lowerCamelCase_ = first_x_coord
lowerCamelCase_ = first_y_coord
lowerCamelCase_ = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = next_point(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ = logging.get_logger(__name__)
A_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
A_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
A_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
A_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
A_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
A_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
A_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
A_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
A_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
A_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
A_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
A_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
A_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
A_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
A_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
A_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
A_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
A_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
A_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
A_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_MAPPING
A_ = auto_class_update(FlaxAutoModel)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_PRETRAINING_MAPPING
A_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
A_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_MASKED_LM_MAPPING
A_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
A_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
A_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
A_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
A_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
A_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
A_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
A_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class UpperCAmelCase ( _BaseAutoModelClass ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
A_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)
| 42 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
raise ValueError(
'Warning: upper bound of deterministic test is exceeded. '
'Pass allow_probable=True to allow probabilistic test. '
'A return value of True indicates a probable prime.' )
# array bounds provided by analysis
lowerCamelCase_ = [
20_47,
1_37_36_53,
25_32_60_01,
32_15_03_17_51,
2_15_23_02_89_87_47,
3_47_47_49_66_03_83,
3_41_55_00_71_72_83_21,
1,
3_82_51_23_05_65_46_41_30_51,
1,
1,
31_86_65_85_78_34_03_11_51_16_74_61,
3_31_70_44_06_46_79_88_73_85_96_19_81,
]
lowerCamelCase_ = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__UpperCamelCase ,1 ):
if n < _p:
# then we have our last prime to check
lowerCamelCase_ = primes[:idx]
break
lowerCamelCase_ ,lowerCamelCase_ = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
lowerCamelCase_ = False
for r in range(__UpperCamelCase ):
lowerCamelCase_ = pow(__UpperCamelCase ,d * 2**r ,__UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
lowerCamelCase_ = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def _UpperCamelCase ( ) -> None:
assert not miller_rabin(5_61 )
assert miller_rabin(5_63 )
# 2047
assert not miller_rabin(83_82_01 )
assert miller_rabin(83_82_07 )
# 1_373_653
assert not miller_rabin(17_31_60_01 )
assert miller_rabin(17_31_60_17 )
# 25_326_001
assert not miller_rabin(30_78_38_66_41 )
assert miller_rabin(30_78_38_66_53 )
# 3_215_031_751
assert not miller_rabin(1_71_30_45_57_48_01 )
assert miller_rabin(1_71_30_45_57_48_19 )
# 2_152_302_898_747
assert not miller_rabin(2_77_97_99_72_83_07 )
assert miller_rabin(2_77_97_99_72_83_27 )
# 3_474_749_660_383
assert not miller_rabin(1_13_85_00_23_90_94_41 )
assert miller_rabin(1_13_85_00_23_90_95_27 )
# 341_550_071_728_321
assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ = 16
A_ = 32
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 16 ,__UpperCamelCase = "bert-base-cased" ) -> List[Any]:
lowerCamelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = load_dataset('glue' ,'mrpc' )
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase_ = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCamelCase_ = datasets.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,load_from_cache_file=__UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase_ = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(__UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__UpperCamelCase ,padding='max_length' ,max_length=1_28 ,return_tensors='pt' )
return tokenizer.pad(__UpperCamelCase ,padding='longest' ,return_tensors='pt' )
# Instantiate dataloaders.
lowerCamelCase_ = DataLoader(
tokenized_datasets['train'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
lowerCamelCase_ = DataLoader(
tokenized_datasets['validation'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
model.eval()
lowerCamelCase_ = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__UpperCamelCase ) - 1:
lowerCamelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCamelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__UpperCamelCase ,references=__UpperCamelCase ,)
lowerCamelCase_ = metric.compute()
return eval_metric["accuracy"]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[str]:
# Initialize accelerator
lowerCamelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase_ = config['lr']
lowerCamelCase_ = int(config['num_epochs'] )
lowerCamelCase_ = int(config['seed'] )
lowerCamelCase_ = int(config['batch_size'] )
lowerCamelCase_ = args.model_name_or_path
set_seed(__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = get_dataloaders(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,return_dict=__UpperCamelCase )
# Instantiate optimizer
lowerCamelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase_ = optimizer_cls(params=model.parameters() ,lr=__UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowerCamelCase_ = 1
lowerCamelCase_ = (len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase_ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase ,num_warmup_steps=0 ,num_training_steps=__UpperCamelCase ,)
else:
lowerCamelCase_ = DummyScheduler(__UpperCamelCase ,total_num_steps=__UpperCamelCase ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase_ = 0
lowerCamelCase_ = evaluate.load('glue' ,'mrpc' )
lowerCamelCase_ = num_epochs
if args.partial_train_epoch is not None:
lowerCamelCase_ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCamelCase_ = args.resume_from_checkpoint.split('epoch_' )[1]
lowerCamelCase_ = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCamelCase_ = int(__UpperCamelCase ) + 1
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
accelerator.print('resumed checkpoint performance:' ,__UpperCamelCase )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' ,lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' ,optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir ,f'''state_{starting_epoch-1}.json''' ) ,'r' ) as f:
lowerCamelCase_ = json.load(__UpperCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCamelCase_ = {}
for epoch in range(__UpperCamelCase ,__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.loss
lowerCamelCase_ = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCamelCase_ = f'''epoch_{epoch}'''
lowerCamelCase_ = os.path.join(args.output_dir ,__UpperCamelCase )
accelerator.save_state(__UpperCamelCase )
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = accuracy
lowerCamelCase_ = lr_scheduler.get_lr()[0]
lowerCamelCase_ = optimizer.param_groups[0]['lr']
lowerCamelCase_ = epoch
lowerCamelCase_ = overall_step
accelerator.print(f'''epoch {epoch}:''' ,__UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,f'''state_{epoch}.json''' ) ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> str:
lowerCamelCase_ = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' ,type=__UpperCamelCase ,default='bert-base-cased' ,help='Path to pretrained model or model identifier from huggingface.co/models.' ,required=__UpperCamelCase ,)
parser.add_argument(
'--output_dir' ,type=__UpperCamelCase ,default='.' ,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,)
parser.add_argument(
'--resume_from_checkpoint' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If the training should continue from a checkpoint folder.' ,)
parser.add_argument(
'--partial_train_epoch' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If passed, the training will stop after this number of epochs.' ,)
parser.add_argument(
'--num_epochs' ,type=__UpperCamelCase ,default=2 ,help='Number of train epochs.' ,)
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = None
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 2
@register_to_config
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = 100 , SCREAMING_SNAKE_CASE_ = 1.007 , SCREAMING_SNAKE_CASE_ = 80 , SCREAMING_SNAKE_CASE_ = 0.05 , SCREAMING_SNAKE_CASE_ = 50 , ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = sigma_max
# setable values
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None # sigma(t_i)
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = num_inference_steps
lowerCamelCase_ = np.arange(0 , self.num_inference_steps )[::-1].copy()
lowerCamelCase_ = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
lowerCamelCase_ = torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.floataa , device=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[torch.FloatTensor, float]:
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
lowerCamelCase_ = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
lowerCamelCase_ = 0
# sample eps ~ N(0, S_noise^2 * I)
lowerCamelCase_ = self.config.s_noise * randn_tensor(sample.shape , generator=SCREAMING_SNAKE_CASE_ ).to(sample.device )
lowerCamelCase_ = sigma + gamma * sigma
lowerCamelCase_ = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , ) -> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
lowerCamelCase_ = sample_hat + sigma_hat * model_output
lowerCamelCase_ = (sample_hat - pred_original_sample) / sigma_hat
lowerCamelCase_ = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , ) -> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
lowerCamelCase_ = sample_prev + sigma_prev * model_output
lowerCamelCase_ = (sample_prev - pred_original_sample) / sigma_prev
lowerCamelCase_ = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
raise NotImplementedError()
| 42 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> np.ndarray:
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
lowerCamelCase_ = ksize + 1
lowerCamelCase_ = np.zeros((ksize, ksize) ,dtype=np.floataa )
# each value
for y in range(__UpperCamelCase ):
for x in range(__UpperCamelCase ):
# distance from center
lowerCamelCase_ = x - ksize // 2
lowerCamelCase_ = y - ksize // 2
# degree to radiant
lowerCamelCase_ = theta / 1_80 * np.pi
lowerCamelCase_ = np.cos(_theta )
lowerCamelCase_ = np.sin(_theta )
# get kernel x
lowerCamelCase_ = cos_theta * px + sin_theta * py
# get kernel y
lowerCamelCase_ = -sin_theta * px + cos_theta * py
# fill kernel
lowerCamelCase_ = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
A_ = imread("../image_data/lena.jpg")
# turn image in gray scale value
A_ = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
A_ = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
A_ = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
A_ = out / out.max() * 255
A_ = out.astype(np.uinta)
imshow("Original", gray)
imshow("Gabor filter with 20x20 mask and 6 directions", out)
waitKey(0)
| 42 | 1 |
'''simple docstring'''
A_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
A_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def _UpperCamelCase ( __UpperCamelCase ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def _UpperCamelCase ( __UpperCamelCase ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def _UpperCamelCase ( ) -> None:
lowerCamelCase_ = 'Morse code here!'
print(__UpperCamelCase )
lowerCamelCase_ = encrypt(__UpperCamelCase )
print(__UpperCamelCase )
lowerCamelCase_ = decrypt(__UpperCamelCase )
print(__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['transformers', 'torch', 'note_seq']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 42 | 1 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _UpperCamelCase ( ) -> List[Any]:
lowerCamelCase_ = HfArgumentParser(__UpperCamelCase )
lowerCamelCase_ = parser.parse_args_into_dataclasses()[0]
lowerCamelCase_ = TensorFlowBenchmark(args=__UpperCamelCase )
try:
lowerCamelCase_ = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCamelCase_ = 'Arg --no_{0} is no longer used, please use --no-{0} instead.'
lowerCamelCase_ = ' '.join(str(__UpperCamelCase ).split(' ' )[:-1] )
lowerCamelCase_ = ''
lowerCamelCase_ = eval(str(__UpperCamelCase ).split(' ' )[-1] )
lowerCamelCase_ = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
lowerCamelCase_ = full_error_msg + begin_error_msg + str(__UpperCamelCase )
raise ValueError(__UpperCamelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
lowerCamelCase_ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
lowerCamelCase_ = DDPMScheduler()
lowerCamelCase_ = AudioDiffusionPipeline(vqvae=SCREAMING_SNAKE_CASE_ , unet=self.dummy_unet , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 , return_dict=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = self.dummy_vqvae_and_unet
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(raw_audio=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , start_step=5 , steps=10 )
lowerCamelCase_ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = self.dummy_unet_condition
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=SCREAMING_SNAKE_CASE_ , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = torch.rand((1, 1, 10) )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , encoding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = torch_device
lowerCamelCase_ = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 42 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = params
lowerCamelCase_ = np.array(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = np.array([len(SCREAMING_SNAKE_CASE_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Any:
'''simple docstring'''
return len(self.lengths )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = self.params.max_model_input_size
lowerCamelCase_ = self.lengths > max_len
logger.info(f'''Splitting {sum(SCREAMING_SNAKE_CASE_ )} too long sequences.''' )
def divide_chunks(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return [l[i : i + n] for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )]
lowerCamelCase_ = []
lowerCamelCase_ = []
if self.params.mlm:
lowerCamelCase_ ,lowerCamelCase_ = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
else:
lowerCamelCase_ ,lowerCamelCase_ = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
lowerCamelCase_ = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
lowerCamelCase_ = np.insert(SCREAMING_SNAKE_CASE_ , 0 , SCREAMING_SNAKE_CASE_ )
if sub_s[-1] != sep_id:
lowerCamelCase_ = np.insert(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
assert len(SCREAMING_SNAKE_CASE_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(SCREAMING_SNAKE_CASE_ )
new_tok_ids.extend(SCREAMING_SNAKE_CASE_ )
new_lengths.extend([len(SCREAMING_SNAKE_CASE_ ) for l in sub_seqs] )
lowerCamelCase_ = np.array(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = np.array(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = len(self )
lowerCamelCase_ = self.lengths > 11
lowerCamelCase_ = self.token_ids[indices]
lowerCamelCase_ = self.lengths[indices]
lowerCamelCase_ = len(self )
logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
lowerCamelCase_ = self.params.special_tok_ids['unk_token']
lowerCamelCase_ = len(self )
lowerCamelCase_ = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
lowerCamelCase_ = (unk_occs / self.lengths) < 0.5
lowerCamelCase_ = self.token_ids[indices]
lowerCamelCase_ = self.lengths[indices]
lowerCamelCase_ = len(self )
logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
if not self.params.is_master:
return
logger.info(f'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = [t[0] for t in batch]
lowerCamelCase_ = [t[1] for t in batch]
assert len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ )
# Max for paddings
lowerCamelCase_ = max(SCREAMING_SNAKE_CASE_ )
# Pad token ids
if self.params.mlm:
lowerCamelCase_ = self.params.special_tok_ids['pad_token']
else:
lowerCamelCase_ = self.params.special_tok_ids['unk_token']
lowerCamelCase_ = [list(t.astype(SCREAMING_SNAKE_CASE_ ) ) + [pad_idx] * (max_seq_len_ - len(SCREAMING_SNAKE_CASE_ )) for t in token_ids]
assert len(tk_ ) == len(SCREAMING_SNAKE_CASE_ )
assert all(len(SCREAMING_SNAKE_CASE_ ) == max_seq_len_ for t in tk_ )
lowerCamelCase_ = torch.tensor(tk_ ) # (bs, max_seq_len_)
lowerCamelCase_ = torch.tensor(SCREAMING_SNAKE_CASE_ ) # (bs)
return tk_t, lg_t
| 42 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _UpperCamelCase ( __UpperCamelCase = 8 ) -> str:
lowerCamelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(__UpperCamelCase )
lowerCamelCase_ = i // 3
lowerCamelCase_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowerCamelCase_ = (
chars_incl
+ random(__UpperCamelCase ,quotient + remainder )
+ random(__UpperCamelCase ,__UpperCamelCase )
+ random(__UpperCamelCase ,__UpperCamelCase )
)
lowerCamelCase_ = list(__UpperCamelCase )
shuffle(__UpperCamelCase )
return "".join(__UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Tuple:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 8 ) -> bool:
if len(__UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowerCamelCase_ = any(char in ascii_uppercase for char in password )
lowerCamelCase_ = any(char in ascii_lowercase for char in password )
lowerCamelCase_ = any(char in digits for char in password )
lowerCamelCase_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _UpperCamelCase ( ) -> Optional[int]:
lowerCamelCase_ = int(input('Please indicate the max length of your password: ' ).strip() )
lowerCamelCase_ = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' ,password_generator(__UpperCamelCase ) )
print(
'Alternative Password generated:' ,alternative_password_generator(__UpperCamelCase ,__UpperCamelCase ) ,)
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeTop2Router",
"NllbMoeSparseMLP",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowerCamelCase_ = test_metrics
@require_cpu
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
debug_launcher(self.test_metrics.main )
@require_single_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
self.test_metrics.main()
@require_multi_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices.''' )
lowerCamelCase_ = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=os.environ.copy() )
| 42 | 1 |
'''simple docstring'''
import os
import string
import sys
A_ = 1 << 8
A_ = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
A_ = KEYMAP["up"]
A_ = KEYMAP["left"]
if sys.platform == "win32":
A_ = []
A_ = {
B"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
B"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
B"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
B"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
B"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
B"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
B"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
B"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
A_ = ord(str(i))
def _UpperCamelCase ( ) -> int:
if os.name == "nt":
import msvcrt
lowerCamelCase_ = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(__UpperCamelCase ) == 0:
# Read the keystroke
lowerCamelCase_ = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowerCamelCase_ = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowerCamelCase_ = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(__UpperCamelCase )
if ord(__UpperCamelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
lowerCamelCase_ = chr(KEYMAP['esc'] )
except KeyError:
lowerCamelCase_ = cha[1]
else:
lowerCamelCase_ = ch.decode(__UpperCamelCase )
else:
lowerCamelCase_ = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowerCamelCase_ = sys.stdin.fileno()
lowerCamelCase_ = termios.tcgetattr(__UpperCamelCase )
try:
tty.setraw(__UpperCamelCase )
lowerCamelCase_ = sys.stdin.read(1 )
finally:
termios.tcsetattr(__UpperCamelCase ,termios.TCSADRAIN ,__UpperCamelCase )
return ch
def _UpperCamelCase ( ) -> str:
lowerCamelCase_ = get_raw_chars()
if ord(__UpperCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(__UpperCamelCase ) == KEYMAP["esc"]:
lowerCamelCase_ = get_raw_chars()
if ord(__UpperCamelCase ) == KEYMAP["mod_int"]:
lowerCamelCase_ = get_raw_chars()
if ord(__UpperCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(__UpperCamelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 42 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
if hor == 1_28:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 64, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
lowerCamelCase_ = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
lowerCamelCase_ = model.state_dict()
lowerCamelCase_ = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 6_55_36,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> Tuple:
lowerCamelCase_ = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 1_28, 2_56),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 6_55_36,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
lowerCamelCase_ = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
lowerCamelCase_ = model
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin' )
with open('hub/hopper-medium-v2/value_function/config.json' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 42 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ = "cpu" , SCREAMING_SNAKE_CASE_ = "openai/clip-vit-large-patch14" ) -> None:
'''simple docstring'''
lowerCamelCase_ = device
lowerCamelCase_ = CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [0.48_145_466, 0.4_578_275, 0.40_821_073]
lowerCamelCase_ = [0.26_862_954, 0.26_130_258, 0.27_577_711]
lowerCamelCase_ = torchvision.transforms.Normalize(self.image_mean , self.image_std )
lowerCamelCase_ = torchvision.transforms.Resize(224 )
lowerCamelCase_ = torchvision.transforms.CenterCrop(224 )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.resize(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.center_crop(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.normalize(SCREAMING_SNAKE_CASE_ )
return images
def __call__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(text=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.preprocess_img(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0.01 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="image" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , ) -> None:
'''simple docstring'''
super().__init__()
lowerCamelCase_ = None
lowerCamelCase_ = device if device else get_device()
if vqgan:
lowerCamelCase_ = vqgan
else:
lowerCamelCase_ = load_vqgan(self.device , conf_path=SCREAMING_SNAKE_CASE_ , ckpt_path=SCREAMING_SNAKE_CASE_ )
self.vqgan.eval()
if clip:
lowerCamelCase_ = clip
else:
lowerCamelCase_ = CLIPModel.from_pretrained('openai/clip-vit-base-patch32' )
self.clip.to(self.device )
lowerCamelCase_ = ProcessorGradientFlow(device=self.device )
lowerCamelCase_ = iterations
lowerCamelCase_ = lr
lowerCamelCase_ = log
lowerCamelCase_ = make_grid
lowerCamelCase_ = return_val
lowerCamelCase_ = quantize
lowerCamelCase_ = self.vqgan.decoder.z_shape
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=True ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = []
if output_path is None:
lowerCamelCase_ = './animation.gif'
if input_path is None:
lowerCamelCase_ = self.save_path
lowerCamelCase_ = sorted(glob(input_path + '/*' ) )
if not len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
' function?)' )
if len(SCREAMING_SNAKE_CASE_ ) == 1:
print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)' )
lowerCamelCase_ = total_duration / len(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [frame_duration] * len(SCREAMING_SNAKE_CASE_ )
if extend_frames:
lowerCamelCase_ = 1.5
lowerCamelCase_ = 3
for file_name in paths:
if file_name.endswith('.png' ):
images.append(imageio.imread(SCREAMING_SNAKE_CASE_ ) )
imageio.mimsave(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , duration=SCREAMING_SNAKE_CASE_ )
print(f'''gif saved to {output_path}''' )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
if not (path or img):
raise ValueError('Input either path or tensor' )
if img is not None:
raise NotImplementedError
lowerCamelCase_ = preprocess(Image.open(SCREAMING_SNAKE_CASE_ ) , target_image_size=256 ).to(self.device )
lowerCamelCase_ = preprocess_vqgan(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,*lowerCamelCase_ = self.vqgan.encode(SCREAMING_SNAKE_CASE_ )
return z
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.latent.detach().requires_grad_()
lowerCamelCase_ = base_latent + transform_vector
if self.quantize:
lowerCamelCase_ ,*lowerCamelCase_ = self.vqgan.quantize(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = trans_latent
return self.vqgan.decode(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.clip_preprocessor(text=SCREAMING_SNAKE_CASE_ , images=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.clip(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = clip_outputs.logits_per_image
if weights is not None:
lowerCamelCase_ = similarity_logits * weights
return similarity_logits.sum()
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self._get_clip_similarity(pos_prompts['prompts'] , SCREAMING_SNAKE_CASE_ , weights=(1 / pos_prompts['weights']) )
if neg_prompts:
lowerCamelCase_ = self._get_clip_similarity(neg_prompts['prompts'] , SCREAMING_SNAKE_CASE_ , weights=neg_prompts['weights'] )
else:
lowerCamelCase_ = torch.tensor([1] , device=self.device )
lowerCamelCase_ = -torch.log(SCREAMING_SNAKE_CASE_ ) + torch.log(SCREAMING_SNAKE_CASE_ )
return loss
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = torch.randn_like(self.latent , requires_grad=SCREAMING_SNAKE_CASE_ , device=self.device )
lowerCamelCase_ = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
lowerCamelCase_ = self._add_vector(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = loop_post_process(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self._get_CLIP_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print('CLIP loss' , SCREAMING_SNAKE_CASE_ )
if self.log:
wandb.log({'CLIP Loss': clip_loss} )
clip_loss.backward(retain_graph=SCREAMING_SNAKE_CASE_ )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
wandb.init(reinit=SCREAMING_SNAKE_CASE_ , project='face-editor' )
wandb.config.update({'Positive Prompts': positive_prompts} )
wandb.config.update({'Negative Prompts': negative_prompts} )
wandb.config.update({'lr': self.lr, 'iterations': self.iterations} )
if image_path:
lowerCamelCase_ = Image.open(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = image.resize((256, 256) )
wandb.log('Original Image' , wandb.Image(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
if not prompts:
return []
lowerCamelCase_ = []
lowerCamelCase_ = []
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [prompt.strip() for prompt in prompts.split('|' )]
for prompt in prompts:
if isinstance(SCREAMING_SNAKE_CASE_ , (tuple, list) ):
lowerCamelCase_ = prompt[0]
lowerCamelCase_ = float(prompt[1] )
elif ":" in prompt:
lowerCamelCase_ ,lowerCamelCase_ = prompt.split(':' )
lowerCamelCase_ = float(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = prompt
lowerCamelCase_ = 1.0
processed_prompts.append(SCREAMING_SNAKE_CASE_ )
weights.append(SCREAMING_SNAKE_CASE_ )
return {
"prompts": processed_prompts,
"weights": torch.tensor(SCREAMING_SNAKE_CASE_ , device=self.device ),
}
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , ) -> Union[str, Any]:
'''simple docstring'''
if image_path:
lowerCamelCase_ = self._get_latent(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert pos_prompts, "You must provide at least one positive prompt."
lowerCamelCase_ = self.process_prompts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.process_prompts(SCREAMING_SNAKE_CASE_ )
if save_final and save_path is None:
lowerCamelCase_ = os.path.join('./outputs/' , '_'.join(pos_prompts['prompts'] ) )
if not os.path.exists(SCREAMING_SNAKE_CASE_ ):
os.makedirs(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = save_path + '_' + get_timestamp()
os.makedirs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = save_path
lowerCamelCase_ = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('Original Image' )
show_pil(custom_to_pil(SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase_ = loop_post_process(SCREAMING_SNAKE_CASE_ )
for iter, transformed_img in enumerate(self._optimize_CLIP(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ):
if show_intermediate:
show_pil(SCREAMING_SNAKE_CASE_ )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , f'''iter_{iter:03d}.png''' ) )
if self.log:
wandb.log({'Image': wandb.Image(SCREAMING_SNAKE_CASE_ )} )
if show_final:
show_pil(SCREAMING_SNAKE_CASE_ )
if save_final:
transformed_img.save(os.path.join(self.save_path , f'''iter_{iter:03d}_final.png''' ) )
| 42 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A_ = 250_004
A_ = 250_020
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MBartTokenizer
SCREAMING_SNAKE_CASE_ = MBartTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/mbart-large-en-ro'
SCREAMING_SNAKE_CASE_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE_ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 250020 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 42 | 1 |
'''simple docstring'''
import string
from math import logaa
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> int:
lowerCamelCase_ = document.translate(
str.maketrans('' ,'' ,string.punctuation ) ).replace('\n' ,'' )
lowerCamelCase_ = document_without_punctuation.split(' ' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> tuple[int, int]:
lowerCamelCase_ = corpus.lower().translate(
str.maketrans('' ,'' ,string.punctuation ) ) # strip all punctuation and replace it with ''
lowerCamelCase_ = corpus_without_punctuation.split('\n' )
lowerCamelCase_ = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__UpperCamelCase ))
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=False ) -> float:
if smoothing:
if n == 0:
raise ValueError('log10(0) is undefined.' )
return round(1 + logaa(n / (1 + df) ) ,3 )
if df == 0:
raise ZeroDivisionError('df must be > 0' )
elif n == 0:
raise ValueError('log10(0) is undefined.' )
return round(logaa(n / df ) ,3 )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> float:
return round(tf * idf ,3 )
| 42 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'ylacombe/bark-small'
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = 'en_speaker_1'
lowerCamelCase_ = 'This is a test string'
lowerCamelCase_ = 'speaker_embeddings_path.json'
lowerCamelCase_ = 'speaker_embeddings'
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowerCamelCase_ = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCamelCase_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowerCamelCase_ = 35
lowerCamelCase_ = 2
lowerCamelCase_ = 8
lowerCamelCase_ = {
'semantic_prompt': np.ones(SCREAMING_SNAKE_CASE_ ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase_ = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string )
lowerCamelCase_ = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 42 | 1 |
'''simple docstring'''
import math
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> float:
if (
not isinstance(__UpperCamelCase ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and 1.' )
return apparent_power * power_factor
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> float:
if (
not isinstance(__UpperCamelCase ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and 1.' )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
A_ = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
A_ = {
"allenai/longformer-base-4096": 4_096,
"allenai/longformer-large-4096": 4_096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = (
list(range(ord('!' ) ,ord('~' ) + 1 ) ) + list(range(ord('¡' ) ,ord('¬' ) + 1 ) ) + list(range(ord('®' ) ,ord('ÿ' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase ,__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as vocab_handle:
lowerCamelCase_ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ ,lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCamelCase_ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ' '.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = word
return word
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(' ' ) )
return bpe_tokens
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = ''.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '\n' )
lowerCamelCase_ = 0
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase_ = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE_ ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ' ' + text
return (text, kwargs)
| 42 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
if height >= 1:
move_tower(height - 1 ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
move_disk(__UpperCamelCase ,__UpperCamelCase )
move_tower(height - 1 ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Tuple:
print('moving disk from' ,__UpperCamelCase ,'to' ,__UpperCamelCase )
def _UpperCamelCase ( ) -> Tuple:
lowerCamelCase_ = int(input('Height of hanoi: ' ).strip() )
move_tower(__UpperCamelCase ,'A' ,'B' ,'C' )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A_ = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
A_ = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
A_ = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = RealmTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="[UNK]" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="[PAD]" , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[MASK]" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , do_lower_case=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , tokenize_chinese_chars=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , SCREAMING_SNAKE_CASE_ ) != do_lower_case
or normalizer_state.get('strip_accents' , SCREAMING_SNAKE_CASE_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , normalizer_state.pop('type' ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = do_lower_case
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = PaddingStrategy.MAX_LENGTH
lowerCamelCase_ = text
lowerCamelCase_ = kwargs.pop('text_pair' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = kwargs.pop('return_tensors' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(SCREAMING_SNAKE_CASE_ ):
if batch_text_pair is not None:
lowerCamelCase_ = batch_text_pair[idx]
else:
lowerCamelCase_ = None
lowerCamelCase_ = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoded_candidates.get('input_ids' )
lowerCamelCase_ = encoded_candidates.get('attention_mask' )
lowerCamelCase_ = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(SCREAMING_SNAKE_CASE_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(SCREAMING_SNAKE_CASE_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {key: item for key, item in output_data.items() if len(SCREAMING_SNAKE_CASE_ ) != 0}
return BatchEncoding(SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = len(__UpperCamelCase )
# We need to create solution object to save path.
lowerCamelCase_ = [[0 for _ in range(__UpperCamelCase )] for _ in range(__UpperCamelCase )]
lowerCamelCase_ = run_maze(__UpperCamelCase ,0 ,0 ,__UpperCamelCase )
if solved:
print('\n'.join(str(__UpperCamelCase ) for row in solutions ) )
else:
print('No solution exists!' )
return solved
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> bool:
lowerCamelCase_ = len(__UpperCamelCase )
# Final check point.
if i == j == (size - 1):
lowerCamelCase_ = 1
return True
lowerCamelCase_ = (not i < 0) and (not j < 0) # Check lower bounds
lowerCamelCase_ = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
lowerCamelCase_ = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
lowerCamelCase_ = 1
# check for directions
if (
run_maze(__UpperCamelCase ,i + 1 ,__UpperCamelCase ,__UpperCamelCase )
or run_maze(__UpperCamelCase ,__UpperCamelCase ,j + 1 ,__UpperCamelCase )
or run_maze(__UpperCamelCase ,i - 1 ,__UpperCamelCase ,__UpperCamelCase )
or run_maze(__UpperCamelCase ,__UpperCamelCase ,j - 1 ,__UpperCamelCase )
):
return True
lowerCamelCase_ = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = str(__UpperCamelCase )
return len(__UpperCamelCase ) == 9 and set(__UpperCamelCase ) == set('123456789' )
def _UpperCamelCase ( ) -> int | None:
for base_num in range(99_99 ,49_99 ,-1 ):
lowerCamelCase_ = 10_00_02 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
for base_num in range(3_33 ,99 ,-1 ):
lowerCamelCase_ = 1_00_20_03 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,) -> List[Any]:
if attention_mask is None:
lowerCamelCase_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCamelCase_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCamelCase_ = torch.ones(config.encoder_layers ,config.encoder_attention_heads ,device=__UpperCamelCase )
if decoder_head_mask is None:
lowerCamelCase_ = torch.ones(config.decoder_layers ,config.decoder_attention_heads ,device=__UpperCamelCase )
if cross_attn_head_mask is None:
lowerCamelCase_ = torch.ones(config.decoder_layers ,config.decoder_attention_heads ,device=__UpperCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="relu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=20 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = encoder_layerdrop
lowerCamelCase_ = decoder_layerdrop
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = eos_token_id
lowerCamelCase_ = pad_token_id
lowerCamelCase_ = bos_token_id
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = self.eos_token_id # Eos Token
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCamelCase_ = input_ids.clamp(self.pad_token_id + 1 )
lowerCamelCase_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCamelCase_ = self.get_config()
lowerCamelCase_ = prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return config, inputs_dict
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = MaMaaaModel(config=SCREAMING_SNAKE_CASE_ ).get_decoder().to(SCREAMING_SNAKE_CASE_ ).eval()
lowerCamelCase_ = inputs_dict['input_ids']
lowerCamelCase_ = inputs_dict['attention_mask']
lowerCamelCase_ = inputs_dict['head_mask']
# first forward pass
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , head_mask=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
lowerCamelCase_ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCamelCase_ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )['last_hidden_state']
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ )[
'last_hidden_state'
]
# select random slice
lowerCamelCase_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCamelCase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-2 ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = MaMaaaModel(config=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ).eval()
lowerCamelCase_ = model(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = outputs.encoder_last_hidden_state
lowerCamelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = model.get_encoder()
encoder.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = model.get_decoder()
decoder.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE_ = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = MaMaaaModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = model_class.from_pretrained(SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
self.assertEqual(info['missing_keys'] , [] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCamelCase_ = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if not self.is_encoder_decoder:
lowerCamelCase_ = inputs['input_ids']
del inputs["input_ids"]
else:
lowerCamelCase_ = inputs['input_ids']
lowerCamelCase_ = inputs.get('decoder_input_ids' , SCREAMING_SNAKE_CASE_ )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCamelCase_ = wte(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = wte(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = wte(SCREAMING_SNAKE_CASE_ )
with torch.no_grad():
model(**SCREAMING_SNAKE_CASE_ )[0]
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
lowerCamelCase_ = input_dict['input_ids']
lowerCamelCase_ = input_ids.ne(1 ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ ).eval().to(SCREAMING_SNAKE_CASE_ )
if torch_device == "cuda":
model.half()
model.generate(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
model.generate(num_beams=4 , do_sample=SCREAMING_SNAKE_CASE_ , early_stopping=SCREAMING_SNAKE_CASE_ , num_return_sequences=3 )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
return torch.tensor(__UpperCamelCase ,dtype=torch.long ,device=__UpperCamelCase )
A_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
lowerCamelCase_ = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
lowerCamelCase_ = prepare_mam_aaa_inputs_dict(model.config , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with torch.no_grad():
lowerCamelCase_ = model(**SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
# change to expected output here
lowerCamelCase_ = torch.tensor(
[[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]] , device=SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(SCREAMING_SNAKE_CASE_ )
# change to intended input
lowerCamelCase_ = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
lowerCamelCase_ = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
lowerCamelCase_ = prepare_mam_aaa_inputs_dict(model.config , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with torch.no_grad():
lowerCamelCase_ = model(**SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
# change to expected output here
lowerCamelCase_ = torch.tensor(
[[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]] , device=SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
lowerCamelCase_ = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = model.generate(
input_ids=dct['input_ids'].to(SCREAMING_SNAKE_CASE_ ) , attention_mask=dct['attention_mask'].to(SCREAMING_SNAKE_CASE_ ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
lowerCamelCase_ = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
lowerCamelCase_ = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
assert generated == expected_en
| 42 |
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = spanish_id.replace('-' ,'' ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
lowerCamelCase_ = int(spanish_id_clean[0:8] )
lowerCamelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(__UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'trocr'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'decoder_attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'decoder_layers',
}
def __init__( self , SCREAMING_SNAKE_CASE_=50265 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , **SCREAMING_SNAKE_CASE_ , ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = d_model
lowerCamelCase_ = decoder_layers
lowerCamelCase_ = decoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = activation_function
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = init_std
lowerCamelCase_ = decoder_layerdrop
lowerCamelCase_ = use_cache
lowerCamelCase_ = scale_embedding
lowerCamelCase_ = use_learned_position_embeddings
lowerCamelCase_ = layernorm_embedding
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
| 42 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 | 1 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def _UpperCamelCase ( *__UpperCamelCase ) -> Dict:
with open(__UpperCamelCase ,'r' ) as fh:
fcntl.flock(__UpperCamelCase ,fcntl.LOCK_EX )
try:
print(*__UpperCamelCase )
finally:
fcntl.flock(__UpperCamelCase ,fcntl.LOCK_UN )
A_ = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
A_ = torch.device("cuda", local_rank)
A_ = socket.gethostname()
A_ = f'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
A_ = dist.get_rank()
A_ = dist.get_world_size()
printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(f'''{gpu} is broken''')
raise
| 42 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = "auto" ) -> List[str]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = 7.5 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
# get prompt text embeddings
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
lowerCamelCase_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase_ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowerCamelCase_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = text_embeddings.shape
lowerCamelCase_ = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ = 42
if negative_prompt is None:
lowerCamelCase_ = ['']
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = text_input_ids.shape[-1]
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ = uncond_embeddings.shape[1]
lowerCamelCase_ = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowerCamelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase_ = latents_reference.to(self.device )
lowerCamelCase_ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowerCamelCase_ = (latents_shape[3] - latents_shape_reference[3]) // 2
lowerCamelCase_ = (latents_shape[2] - latents_shape_reference[2]) // 2
lowerCamelCase_ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowerCamelCase_ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowerCamelCase_ = 0 if dx < 0 else dx
lowerCamelCase_ = 0 if dy < 0 else dy
lowerCamelCase_ = max(-dx , 0 )
lowerCamelCase_ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowerCamelCase_ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase_ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 1 / 0.18_215 * latents
lowerCamelCase_ = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowerCamelCase_ = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) , return_tensors='pt' ).to(
self.device )
lowerCamelCase_ ,lowerCamelCase_ = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowerCamelCase_ = None
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ , nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def _UpperCamelCase ( __UpperCamelCase ) -> List[Any]:
lowerCamelCase_ ,lowerCamelCase_ = image.size
lowerCamelCase_ ,lowerCamelCase_ = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
lowerCamelCase_ = image.resize((w, h) ,resample=PIL_INTERPOLATION['lanczos'] )
lowerCamelCase_ = np.array(__UpperCamelCase ).astype(np.floataa ) / 255.0
lowerCamelCase_ = image[None].transpose(0 ,3 ,1 ,2 )
lowerCamelCase_ = torch.from_numpy(__UpperCamelCase )
return 2.0 * image - 1.0
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 100 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
lowerCamelCase_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
lowerCamelCase_ = image.shape[0]
else:
raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
lowerCamelCase_ = preprocess(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
lowerCamelCase_ = (batch_size, self.unet.config.in_channels // 2, height, width)
lowerCamelCase_ = next(self.unet.parameters() ).dtype
lowerCamelCase_ = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = image.to(device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ , device=self.device )
lowerCamelCase_ = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for t in self.progress_bar(SCREAMING_SNAKE_CASE_ ):
# concat latents and low resolution image in the channel dimension.
lowerCamelCase_ = torch.cat([latents, image] , dim=1 )
lowerCamelCase_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).sample
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# decode the image latents with the VQVAE
lowerCamelCase_ = self.vqvae.decode(SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = torch.clamp(SCREAMING_SNAKE_CASE_ , -1.0 , 1.0 )
lowerCamelCase_ = image / 2 + 0.5
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 42 |
'''simple docstring'''
import pprint
import requests
A_ = "https://zenquotes.io/api"
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
A_ = random_quotes()
pprint.pprint(response)
| 42 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=33 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ) -> int:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = EsmModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = EsmForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = EsmForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = ()
SCREAMING_SNAKE_CASE_ = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = EsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase_ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = EsmModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowerCamelCase_ = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowerCamelCase_ = create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE_ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.empty(2 , 4 , 30 )
lowerCamelCase_ = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowerCamelCase_ = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowerCamelCase_ = embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE_ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = 33
lowerCamelCase_ = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
# compare the actual values for a slice.
lowerCamelCase_ = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A_ = {
"configuration_owlvit": [
"OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"OwlViTConfig",
"OwlViTOnnxConfig",
"OwlViTTextConfig",
"OwlViTVisionConfig",
],
"processing_owlvit": ["OwlViTProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["OwlViTFeatureExtractor"]
A_ = ["OwlViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OwlViTModel",
"OwlViTPreTrainedModel",
"OwlViTTextModel",
"OwlViTVisionModel",
"OwlViTForObjectDetection",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 42 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> list:
if len(__UpperCamelCase ) <= 1:
return [tuple(__UpperCamelCase )]
lowerCamelCase_ = []
def generate(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = [0] * n
res.append(tuple(__UpperCamelCase ) )
lowerCamelCase_ = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
lowerCamelCase_ ,lowerCamelCase_ = arr[i], arr[0]
else:
lowerCamelCase_ ,lowerCamelCase_ = arr[i], arr[c[i]]
res.append(tuple(__UpperCamelCase ) )
c[i] += 1
lowerCamelCase_ = 0
else:
lowerCamelCase_ = 0
i += 1
generate(len(__UpperCamelCase ) ,__UpperCamelCase )
return res
if __name__ == "__main__":
A_ = input("Enter numbers separated by a comma:\n").strip()
A_ = [int(item) for item in user_input.split(",")]
print(heaps(arr))
| 42 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'xlm-prophetnet'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 30522 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2 , **SCREAMING_SNAKE_CASE_ , ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = num_encoder_layers
lowerCamelCase_ = num_encoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = num_decoder_layers
lowerCamelCase_ = num_decoder_attention_heads
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = init_std # Normal(0, this parameter)
lowerCamelCase_ = activation_function
# parameters for xlmprophetnet
lowerCamelCase_ = ngram
lowerCamelCase_ = num_buckets
lowerCamelCase_ = relative_max_distance
lowerCamelCase_ = disable_ngram_loss
lowerCamelCase_ = eps
# 3 Types of Dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = dropout
lowerCamelCase_ = use_cache
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , add_cross_attention=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 42 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'timm_backbone'
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> str:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = backbone
lowerCamelCase_ = num_channels
lowerCamelCase_ = features_only
lowerCamelCase_ = use_pretrained_backbone
lowerCamelCase_ = True
lowerCamelCase_ = out_indices if out_indices is not None else (-1,)
| 42 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = x
lowerCamelCase_ = y
for step in range(__UpperCamelCase ): # noqa: B007
lowerCamelCase_ = a * a - b * b + x
lowerCamelCase_ = 2 * a * b + y
lowerCamelCase_ = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55 ) for i in colorsys.hsv_to_rgb(__UpperCamelCase ,1 ,1 ) )
def _UpperCamelCase ( __UpperCamelCase = 8_00 ,__UpperCamelCase = 6_00 ,__UpperCamelCase = -0.6 ,__UpperCamelCase = 0 ,__UpperCamelCase = 3.2 ,__UpperCamelCase = 50 ,__UpperCamelCase = True ,) -> Image.Image:
lowerCamelCase_ = Image.new('RGB' ,(image_width, image_height) )
lowerCamelCase_ = img.load()
# loop through the image-coordinates
for image_x in range(__UpperCamelCase ):
for image_y in range(__UpperCamelCase ):
# determine the figure-coordinates based on the image-coordinates
lowerCamelCase_ = figure_width / image_width * image_height
lowerCamelCase_ = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowerCamelCase_ = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowerCamelCase_ = get_distance(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowerCamelCase_ = get_color_coded_rgb(__UpperCamelCase )
else:
lowerCamelCase_ = get_black_and_white_rgb(__UpperCamelCase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
A_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 42 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> set:
lowerCamelCase_ = set()
# edges = list of graph's edges
lowerCamelCase_ = get_edges(__UpperCamelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCamelCase_ ,lowerCamelCase_ = edges.pop()
chosen_vertices.add(__UpperCamelCase )
chosen_vertices.add(__UpperCamelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(__UpperCamelCase )
return chosen_vertices
def _UpperCamelCase ( __UpperCamelCase ) -> set:
lowerCamelCase_ = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 42 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase_ = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase_ = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase_ = outgoing_gradient**2 + 4
lowerCamelCase_ = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase_ = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase_ = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase_ = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase_ = x_minus if isclose(__UpperCamelCase ,__UpperCamelCase ) else x_plus
lowerCamelCase_ = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _UpperCamelCase ( __UpperCamelCase = 1.4 ,__UpperCamelCase = -9.6 ) -> int:
lowerCamelCase_ = 0
lowerCamelCase_ = first_x_coord
lowerCamelCase_ = first_y_coord
lowerCamelCase_ = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = next_point(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 42 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
raise ValueError(
'Warning: upper bound of deterministic test is exceeded. '
'Pass allow_probable=True to allow probabilistic test. '
'A return value of True indicates a probable prime.' )
# array bounds provided by analysis
lowerCamelCase_ = [
20_47,
1_37_36_53,
25_32_60_01,
32_15_03_17_51,
2_15_23_02_89_87_47,
3_47_47_49_66_03_83,
3_41_55_00_71_72_83_21,
1,
3_82_51_23_05_65_46_41_30_51,
1,
1,
31_86_65_85_78_34_03_11_51_16_74_61,
3_31_70_44_06_46_79_88_73_85_96_19_81,
]
lowerCamelCase_ = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__UpperCamelCase ,1 ):
if n < _p:
# then we have our last prime to check
lowerCamelCase_ = primes[:idx]
break
lowerCamelCase_ ,lowerCamelCase_ = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
lowerCamelCase_ = False
for r in range(__UpperCamelCase ):
lowerCamelCase_ = pow(__UpperCamelCase ,d * 2**r ,__UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
lowerCamelCase_ = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def _UpperCamelCase ( ) -> None:
assert not miller_rabin(5_61 )
assert miller_rabin(5_63 )
# 2047
assert not miller_rabin(83_82_01 )
assert miller_rabin(83_82_07 )
# 1_373_653
assert not miller_rabin(17_31_60_01 )
assert miller_rabin(17_31_60_17 )
# 25_326_001
assert not miller_rabin(30_78_38_66_41 )
assert miller_rabin(30_78_38_66_53 )
# 3_215_031_751
assert not miller_rabin(1_71_30_45_57_48_01 )
assert miller_rabin(1_71_30_45_57_48_19 )
# 2_152_302_898_747
assert not miller_rabin(2_77_97_99_72_83_07 )
assert miller_rabin(2_77_97_99_72_83_27 )
# 3_474_749_660_383
assert not miller_rabin(1_13_85_00_23_90_94_41 )
assert miller_rabin(1_13_85_00_23_90_95_27 )
# 341_550_071_728_321
assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 42 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = None
# Automatically constructed
SCREAMING_SNAKE_CASE_ = "dict"
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = field(default='Translation' , init=UpperCAmelCase__ , repr=UpperCAmelCase__ )
def __call__( self ) -> Optional[Any]:
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def UpperCamelCase( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Value
return {k: Value('string' ) for k in sorted(self.languages )}
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
# Automatically constructed
SCREAMING_SNAKE_CASE_ = "dict"
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = field(default='TranslationVariableLanguages' , init=UpperCAmelCase__ , repr=UpperCAmelCase__ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = sorted(set(self.languages ) ) if self.languages else None
lowerCamelCase_ = len(self.languages ) if self.languages else None
def __call__( self ) -> Dict:
'''simple docstring'''
return pa.struct({'language': pa.list_(pa.string() ), 'translation': pa.list_(pa.string() )} )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = set(self.languages )
if self.languages and set(SCREAMING_SNAKE_CASE_ ) - lang_set:
raise ValueError(
f'''Some languages in example ({", ".join(sorted(set(SCREAMING_SNAKE_CASE_ ) - lang_set ) )}) are not in valid set ({", ".join(SCREAMING_SNAKE_CASE_ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
lowerCamelCase_ = []
for lang, text in translation_dict.items():
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
lowerCamelCase_ ,lowerCamelCase_ = zip(*sorted(SCREAMING_SNAKE_CASE_ ) )
return {"language": languages, "translation": translations}
def UpperCamelCase( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value('string' ) ),
"translation": Sequence(Value('string' ) ),
}
| 42 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ = 16
A_ = 32
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 16 ,__UpperCamelCase = "bert-base-cased" ) -> List[Any]:
lowerCamelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = load_dataset('glue' ,'mrpc' )
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase_ = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCamelCase_ = datasets.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,load_from_cache_file=__UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase_ = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(__UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__UpperCamelCase ,padding='max_length' ,max_length=1_28 ,return_tensors='pt' )
return tokenizer.pad(__UpperCamelCase ,padding='longest' ,return_tensors='pt' )
# Instantiate dataloaders.
lowerCamelCase_ = DataLoader(
tokenized_datasets['train'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
lowerCamelCase_ = DataLoader(
tokenized_datasets['validation'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
model.eval()
lowerCamelCase_ = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__UpperCamelCase ) - 1:
lowerCamelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCamelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__UpperCamelCase ,references=__UpperCamelCase ,)
lowerCamelCase_ = metric.compute()
return eval_metric["accuracy"]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[str]:
# Initialize accelerator
lowerCamelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase_ = config['lr']
lowerCamelCase_ = int(config['num_epochs'] )
lowerCamelCase_ = int(config['seed'] )
lowerCamelCase_ = int(config['batch_size'] )
lowerCamelCase_ = args.model_name_or_path
set_seed(__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = get_dataloaders(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,return_dict=__UpperCamelCase )
# Instantiate optimizer
lowerCamelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase_ = optimizer_cls(params=model.parameters() ,lr=__UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowerCamelCase_ = 1
lowerCamelCase_ = (len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase_ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase ,num_warmup_steps=0 ,num_training_steps=__UpperCamelCase ,)
else:
lowerCamelCase_ = DummyScheduler(__UpperCamelCase ,total_num_steps=__UpperCamelCase ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase_ = 0
lowerCamelCase_ = evaluate.load('glue' ,'mrpc' )
lowerCamelCase_ = num_epochs
if args.partial_train_epoch is not None:
lowerCamelCase_ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCamelCase_ = args.resume_from_checkpoint.split('epoch_' )[1]
lowerCamelCase_ = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCamelCase_ = int(__UpperCamelCase ) + 1
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
accelerator.print('resumed checkpoint performance:' ,__UpperCamelCase )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' ,lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' ,optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir ,f'''state_{starting_epoch-1}.json''' ) ,'r' ) as f:
lowerCamelCase_ = json.load(__UpperCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCamelCase_ = {}
for epoch in range(__UpperCamelCase ,__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.loss
lowerCamelCase_ = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCamelCase_ = f'''epoch_{epoch}'''
lowerCamelCase_ = os.path.join(args.output_dir ,__UpperCamelCase )
accelerator.save_state(__UpperCamelCase )
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = accuracy
lowerCamelCase_ = lr_scheduler.get_lr()[0]
lowerCamelCase_ = optimizer.param_groups[0]['lr']
lowerCamelCase_ = epoch
lowerCamelCase_ = overall_step
accelerator.print(f'''epoch {epoch}:''' ,__UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,f'''state_{epoch}.json''' ) ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> str:
lowerCamelCase_ = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' ,type=__UpperCamelCase ,default='bert-base-cased' ,help='Path to pretrained model or model identifier from huggingface.co/models.' ,required=__UpperCamelCase ,)
parser.add_argument(
'--output_dir' ,type=__UpperCamelCase ,default='.' ,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,)
parser.add_argument(
'--resume_from_checkpoint' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If the training should continue from a checkpoint folder.' ,)
parser.add_argument(
'--partial_train_epoch' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If passed, the training will stop after this number of epochs.' ,)
parser.add_argument(
'--num_epochs' ,type=__UpperCamelCase ,default=2 ,help='Number of train epochs.' ,)
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase_ = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase_ = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase_ = outgoing_gradient**2 + 4
lowerCamelCase_ = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase_ = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase_ = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase_ = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase_ = x_minus if isclose(__UpperCamelCase ,__UpperCamelCase ) else x_plus
lowerCamelCase_ = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _UpperCamelCase ( __UpperCamelCase = 1.4 ,__UpperCamelCase = -9.6 ) -> int:
lowerCamelCase_ = 0
lowerCamelCase_ = first_x_coord
lowerCamelCase_ = first_y_coord
lowerCamelCase_ = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = next_point(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> np.ndarray:
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
lowerCamelCase_ = ksize + 1
lowerCamelCase_ = np.zeros((ksize, ksize) ,dtype=np.floataa )
# each value
for y in range(__UpperCamelCase ):
for x in range(__UpperCamelCase ):
# distance from center
lowerCamelCase_ = x - ksize // 2
lowerCamelCase_ = y - ksize // 2
# degree to radiant
lowerCamelCase_ = theta / 1_80 * np.pi
lowerCamelCase_ = np.cos(_theta )
lowerCamelCase_ = np.sin(_theta )
# get kernel x
lowerCamelCase_ = cos_theta * px + sin_theta * py
# get kernel y
lowerCamelCase_ = -sin_theta * px + cos_theta * py
# fill kernel
lowerCamelCase_ = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
A_ = imread("../image_data/lena.jpg")
# turn image in gray scale value
A_ = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
A_ = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
A_ = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
A_ = out / out.max() * 255
A_ = out.astype(np.uinta)
imshow("Original", gray)
imshow("Gabor filter with 20x20 mask and 6 directions", out)
waitKey(0)
| 42 | 1 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ) -> str:
'''simple docstring'''
lowerCamelCase_ = start
lowerCamelCase_ = end
lowerCamelCase_ = val
lowerCamelCase_ = (start + end) // 2
lowerCamelCase_ = left
lowerCamelCase_ = right
def __repr__( self ) -> Tuple:
'''simple docstring'''
return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = collection
lowerCamelCase_ = function
if self.collection:
lowerCamelCase_ = self._build_tree(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
self._update_tree(self.root , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
return self._query_range(self.root , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if start == end:
return SegmentTreeNode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.collection[start] )
lowerCamelCase_ = (start + end) // 2
lowerCamelCase_ = self._build_tree(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self._build_tree(mid + 1 , SCREAMING_SNAKE_CASE_ )
return SegmentTreeNode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.fn(left.val , right.val ) , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
if node.start == i and node.end == i:
lowerCamelCase_ = val
return
if i <= node.mid:
self._update_tree(node.left , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
self._update_tree(node.right , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.fn(node.left.val , node.right.val )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , SCREAMING_SNAKE_CASE_ , node.mid ) , self._query_range(node.right , node.mid + 1 , SCREAMING_SNAKE_CASE_ ) , )
else:
# range in right child tree
return self._query_range(node.right , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if self.root is not None:
lowerCamelCase_ = Queue()
queue.put(self.root )
while not queue.empty():
lowerCamelCase_ = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("*" * 50)
A_ = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 42 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['transformers', 'torch', 'note_seq']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 42 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ('foo.json',)] )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = AutoConfig.from_pretrained('gpt2' )
lowerCamelCase_ = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = GenerationConfig()
lowerCamelCase_ = {
'max_new_tokens': 1024,
'foo': 'bar',
}
lowerCamelCase_ = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = generation_config.update(**SCREAMING_SNAKE_CASE_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(SCREAMING_SNAKE_CASE_ , {'foo': 'bar'} )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = GenerationConfig()
lowerCamelCase_ = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
lowerCamelCase_ = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_ )
assert not hasattr(SCREAMING_SNAKE_CASE_ , 'foo' ) # no new kwargs should be initialized if from config
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(default_config.num_beams , 1 )
lowerCamelCase_ = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def UpperCamelCase( cls ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE_ )
@classmethod
def UpperCamelCase( cls ) -> Tuple:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id='test-generation-config' , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id='valid_org/test-generation-config-org' , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
| 42 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
lowerCamelCase_ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
lowerCamelCase_ = DDPMScheduler()
lowerCamelCase_ = AudioDiffusionPipeline(vqvae=SCREAMING_SNAKE_CASE_ , unet=self.dummy_unet , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 , return_dict=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = self.dummy_vqvae_and_unet
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(raw_audio=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , start_step=5 , steps=10 )
lowerCamelCase_ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = self.dummy_unet_condition
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=SCREAMING_SNAKE_CASE_ , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = torch.rand((1, 1, 10) )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , encoding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = torch_device
lowerCamelCase_ = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 42 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=False ,) -> Any:
output_path.parent.mkdir(parents=__UpperCamelCase ,exist_ok=__UpperCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
__UpperCamelCase ,__UpperCamelCase ,f=output_path.as_posix() ,input_names=__UpperCamelCase ,output_names=__UpperCamelCase ,dynamic_axes=__UpperCamelCase ,do_constant_folding=__UpperCamelCase ,use_external_data_format=__UpperCamelCase ,enable_onnx_checker=__UpperCamelCase ,opset_version=__UpperCamelCase ,)
else:
export(
__UpperCamelCase ,__UpperCamelCase ,f=output_path.as_posix() ,input_names=__UpperCamelCase ,output_names=__UpperCamelCase ,dynamic_axes=__UpperCamelCase ,do_constant_folding=__UpperCamelCase ,opset_version=__UpperCamelCase ,)
@torch.no_grad()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = False ) -> Dict:
lowerCamelCase_ = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowerCamelCase_ = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
lowerCamelCase_ = 'cpu'
lowerCamelCase_ = Path(__UpperCamelCase )
# VAE DECODER
lowerCamelCase_ = AutoencoderKL.from_pretrained(model_path + '/vae' )
lowerCamelCase_ = vae_decoder.config.latent_channels
# forward only through the decoder part
lowerCamelCase_ = vae_decoder.decode
onnx_export(
__UpperCamelCase ,model_args=(
torch.randn(1 ,__UpperCamelCase ,25 ,25 ).to(device=__UpperCamelCase ,dtype=__UpperCamelCase ),
False,
) ,output_path=output_path / 'vae_decoder' / 'model.onnx' ,ordered_input_names=['latent_sample', 'return_dict'] ,output_names=['sample'] ,dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} ,opset=__UpperCamelCase ,)
del vae_decoder
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
A_ = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX")
| 42 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _UpperCamelCase ( __UpperCamelCase = 8 ) -> str:
lowerCamelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(__UpperCamelCase )
lowerCamelCase_ = i // 3
lowerCamelCase_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowerCamelCase_ = (
chars_incl
+ random(__UpperCamelCase ,quotient + remainder )
+ random(__UpperCamelCase ,__UpperCamelCase )
+ random(__UpperCamelCase ,__UpperCamelCase )
)
lowerCamelCase_ = list(__UpperCamelCase )
shuffle(__UpperCamelCase )
return "".join(__UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Tuple:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 8 ) -> bool:
if len(__UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowerCamelCase_ = any(char in ascii_uppercase for char in password )
lowerCamelCase_ = any(char in ascii_lowercase for char in password )
lowerCamelCase_ = any(char in digits for char in password )
lowerCamelCase_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _UpperCamelCase ( ) -> Optional[int]:
lowerCamelCase_ = int(input('Please indicate the max length of your password: ' ).strip() )
lowerCamelCase_ = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' ,password_generator(__UpperCamelCase ) )
print(
'Alternative Password generated:' ,alternative_password_generator(__UpperCamelCase ,__UpperCamelCase ) ,)
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> float:
'''simple docstring'''
return 0.0
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> tuple[int | float, int | float]:
lowerCamelCase_ = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCamelCase_ = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> None:
lowerCamelCase_ = 5_12
lowerCamelCase_ = [1] + [0] * (size - 1)
lowerCamelCase_ = [filter_type.process(__UpperCamelCase ) for item in inputs]
lowerCamelCase_ = [0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ = np.abs(np.fft.fft(__UpperCamelCase ) )
lowerCamelCase_ = 20 * np.logaa(__UpperCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 ,samplerate / 2 - 1 )
plt.xlabel('Frequency (Hz)' )
plt.xscale('log' )
# Display within reasonable bounds
lowerCamelCase_ = get_bounds(__UpperCamelCase ,__UpperCamelCase )
plt.ylim(max([-80, bounds[0]] ) ,min([80, bounds[1]] ) )
plt.ylabel('Gain (dB)' )
plt.plot(__UpperCamelCase )
plt.show()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> None:
lowerCamelCase_ = 5_12
lowerCamelCase_ = [1] + [0] * (size - 1)
lowerCamelCase_ = [filter_type.process(__UpperCamelCase ) for item in inputs]
lowerCamelCase_ = [0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ = np.angle(np.fft.fft(__UpperCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 ,samplerate / 2 - 1 )
plt.xlabel('Frequency (Hz)' )
plt.xscale('log' )
plt.ylim(-2 * pi ,2 * pi )
plt.ylabel('Phase shift (Radians)' )
plt.plot(np.unwrap(__UpperCamelCase ,-2 * pi ) )
plt.show()
| 42 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowerCamelCase_ = test_metrics
@require_cpu
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
debug_launcher(self.test_metrics.main )
@require_single_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
self.test_metrics.main()
@require_multi_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices.''' )
lowerCamelCase_ = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=os.environ.copy() )
| 42 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base' )
lowerCamelCase_ = AutoTokenizer.from_pretrained('xlm-roberta-base' )
lowerCamelCase_ = 'The dog is cute and lives in the garden house'
lowerCamelCase_ = jnp.array([tokenizer.encode(SCREAMING_SNAKE_CASE_ )] )
lowerCamelCase_ = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
lowerCamelCase_ = jnp.array(
[[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )['last_hidden_state']
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
| 42 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
if hor == 1_28:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 64, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
lowerCamelCase_ = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
lowerCamelCase_ = model.state_dict()
lowerCamelCase_ = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 6_55_36,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> Tuple:
lowerCamelCase_ = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 1_28, 2_56),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 6_55_36,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
lowerCamelCase_ = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
lowerCamelCase_ = model
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin' )
with open('hub/hopper-medium-v2/value_function/config.json' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A_ = 250_004
A_ = 250_020
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MBartTokenizer
SCREAMING_SNAKE_CASE_ = MBartTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/mbart-large-en-ro'
SCREAMING_SNAKE_CASE_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE_ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 250020 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 42 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
A_ = "\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n >>> pipe_prior.to(\"cuda\")\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n >>> pipe.to(\"cuda\")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save(\"cat.png\")\n ```\n"
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=8 ) -> List[Any]:
lowerCamelCase_ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowerCamelCase_ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(
unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , movq=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
if latents is None:
lowerCamelCase_ = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowerCamelCase_ = latents.to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = latents * scheduler.init_noise_sigma
return latents
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_=0 ) -> Optional[int]:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
lowerCamelCase_ = torch.device(f'''cuda:{gpu_id}''' )
lowerCamelCase_ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_=0 ) -> Optional[Any]:
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
lowerCamelCase_ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=SCREAMING_SNAKE_CASE_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowerCamelCase_ = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowerCamelCase_ ,lowerCamelCase_ = cpu_offload_with_hook(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prev_module_hook=SCREAMING_SNAKE_CASE_ )
# We'll offload the last model manually.
lowerCamelCase_ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(SCREAMING_SNAKE_CASE_ , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(SCREAMING_SNAKE_CASE_ )
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 100 , SCREAMING_SNAKE_CASE_ = 4.0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self._execution_device
lowerCamelCase_ = guidance_scale > 1.0
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = torch.cat(SCREAMING_SNAKE_CASE_ , dim=0 )
lowerCamelCase_ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = torch.cat(SCREAMING_SNAKE_CASE_ , dim=0 )
if do_classifier_free_guidance:
lowerCamelCase_ = image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ , dim=0 )
lowerCamelCase_ = negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ , dim=0 )
lowerCamelCase_ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=SCREAMING_SNAKE_CASE_ )
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.scheduler.timesteps
lowerCamelCase_ = self.unet.config.in_channels
lowerCamelCase_ ,lowerCamelCase_ = downscale_height_and_width(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.movq_scale_factor )
# create initial latent
lowerCamelCase_ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = {'image_embeds': image_embeds}
lowerCamelCase_ = self.unet(
sample=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , added_cond_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
if do_classifier_free_guidance:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.split(latents.shape[1] , dim=1 )
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ ,lowerCamelCase_ = variance_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowerCamelCase_ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , )[0]
# post-processing
lowerCamelCase_ = self.movq.decode(SCREAMING_SNAKE_CASE_ , force_not_quantize=SCREAMING_SNAKE_CASE_ )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowerCamelCase_ = image * 0.5 + 0.5
lowerCamelCase_ = image.clamp(0 , 1 )
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 42 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'ylacombe/bark-small'
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = 'en_speaker_1'
lowerCamelCase_ = 'This is a test string'
lowerCamelCase_ = 'speaker_embeddings_path.json'
lowerCamelCase_ = 'speaker_embeddings'
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowerCamelCase_ = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCamelCase_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowerCamelCase_ = 35
lowerCamelCase_ = 2
lowerCamelCase_ = 8
lowerCamelCase_ = {
'semantic_prompt': np.ones(SCREAMING_SNAKE_CASE_ ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase_ = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string )
lowerCamelCase_ = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 42 | 1 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = [10, 20, 30, 40, 50, 60]
lowerCamelCase_ = [2, 4, 6, 8, 10, 12]
lowerCamelCase_ = 100
self.assertEqual(kp.calc_profit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , 210 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.assertRaisesRegex(SCREAMING_SNAKE_CASE_ , 'max_weight must greater than zero.' )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
self.assertRaisesRegex(SCREAMING_SNAKE_CASE_ , 'Weight can not be negative.' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertRaisesRegex(SCREAMING_SNAKE_CASE_ , 'Profit can not be negative.' )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
self.assertRaisesRegex(SCREAMING_SNAKE_CASE_ , 'max_weight must greater than zero.' )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
self.assertRaisesRegex(
SCREAMING_SNAKE_CASE_ , 'The length of profit and weight must be same.' )
if __name__ == "__main__":
unittest.main()
| 42 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
A_ = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
A_ = {
"allenai/longformer-base-4096": 4_096,
"allenai/longformer-large-4096": 4_096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = (
list(range(ord('!' ) ,ord('~' ) + 1 ) ) + list(range(ord('¡' ) ,ord('¬' ) + 1 ) ) + list(range(ord('®' ) ,ord('ÿ' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase ,__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as vocab_handle:
lowerCamelCase_ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ ,lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCamelCase_ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ' '.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = word
return word
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(' ' ) )
return bpe_tokens
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = ''.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '\n' )
lowerCamelCase_ = 0
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase_ = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE_ ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ' ' + text
return (text, kwargs)
| 42 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
A_ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
A_ = "cuda" if torch.cuda.is_available() else "cpu"
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase=1_00 ,__UpperCamelCase=" " ) -> List[str]:
lowerCamelCase_ = text.split(__UpperCamelCase )
return [character.join(text[i : i + n] ).strip() for i in range(0 ,len(__UpperCamelCase ) ,__UpperCamelCase )]
def _UpperCamelCase ( __UpperCamelCase ) -> dict:
lowerCamelCase_ ,lowerCamelCase_ = [], []
for title, text in zip(documents['title'] ,documents['text'] ):
if text is not None:
for passage in split_text(__UpperCamelCase ):
titles.append(title if title is not None else '' )
texts.append(__UpperCamelCase )
return {"title": titles, "text": texts}
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> dict:
lowerCamelCase_ = ctx_tokenizer(
documents['title'] ,documents['text'] ,truncation=__UpperCamelCase ,padding='longest' ,return_tensors='pt' )['input_ids']
lowerCamelCase_ = ctx_encoder(input_ids.to(device=__UpperCamelCase ) ,return_dict=__UpperCamelCase ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,) -> Union[str, Any]:
######################################
logger.info('Step 1 - Create the dataset' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowerCamelCase_ = load_dataset(
'csv' ,data_files=[rag_example_args.csv_path] ,split='train' ,delimiter='\t' ,column_names=['title', 'text'] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowerCamelCase_ = dataset.map(__UpperCamelCase ,batched=__UpperCamelCase ,num_proc=processing_args.num_proc )
# And compute the embeddings
lowerCamelCase_ = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__UpperCamelCase )
lowerCamelCase_ = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowerCamelCase_ = Features(
{'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} ) # optional, save as float32 instead of float64 to save space
lowerCamelCase_ = dataset.map(
partial(__UpperCamelCase ,ctx_encoder=__UpperCamelCase ,ctx_tokenizer=__UpperCamelCase ) ,batched=__UpperCamelCase ,batch_size=processing_args.batch_size ,features=__UpperCamelCase ,)
# And finally save your dataset
lowerCamelCase_ = os.path.join(rag_example_args.output_dir ,'my_knowledge_dataset' )
dataset.save_to_disk(__UpperCamelCase )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('Step 2 - Index the dataset' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowerCamelCase_ = faiss.IndexHNSWFlat(index_hnsw_args.d ,index_hnsw_args.m ,faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('embeddings' ,custom_index=__UpperCamelCase )
# And save the index
lowerCamelCase_ = os.path.join(rag_example_args.output_dir ,'my_knowledge_dataset_hnsw_index.faiss' )
dataset.get_index('embeddings' ).save(__UpperCamelCase )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = field(
default=str(Path(UpperCAmelCase__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
SCREAMING_SNAKE_CASE_ = field(
default=UpperCAmelCase__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
SCREAMING_SNAKE_CASE_ = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
SCREAMING_SNAKE_CASE_ = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
SCREAMING_SNAKE_CASE_ = field(
default=str(Path(UpperCAmelCase__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = field(
default=UpperCAmelCase__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
SCREAMING_SNAKE_CASE_ = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = field(
default=7_68 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=1_28 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
A_ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
A_ , A_ , A_ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
A_ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 42 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A_ = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
A_ = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
A_ = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = RealmTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="[UNK]" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="[PAD]" , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[MASK]" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , do_lower_case=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , tokenize_chinese_chars=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , SCREAMING_SNAKE_CASE_ ) != do_lower_case
or normalizer_state.get('strip_accents' , SCREAMING_SNAKE_CASE_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , normalizer_state.pop('type' ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = do_lower_case
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = PaddingStrategy.MAX_LENGTH
lowerCamelCase_ = text
lowerCamelCase_ = kwargs.pop('text_pair' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = kwargs.pop('return_tensors' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(SCREAMING_SNAKE_CASE_ ):
if batch_text_pair is not None:
lowerCamelCase_ = batch_text_pair[idx]
else:
lowerCamelCase_ = None
lowerCamelCase_ = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoded_candidates.get('input_ids' )
lowerCamelCase_ = encoded_candidates.get('attention_mask' )
lowerCamelCase_ = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(SCREAMING_SNAKE_CASE_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(SCREAMING_SNAKE_CASE_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {key: item for key, item in output_data.items() if len(SCREAMING_SNAKE_CASE_ ) != 0}
return BatchEncoding(SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = AudioLDMPipeline
SCREAMING_SNAKE_CASE_ = TEXT_TO_AUDIO_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_AUDIO_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=(32, 64) , class_embed_type='simple_projection' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase_ = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
lowerCamelCase_ = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta' , model_max_length=77 )
lowerCamelCase_ = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'vocoder': vocoder,
}
return components
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ) -> Optional[Any]:
'''simple docstring'''
if str(SCREAMING_SNAKE_CASE_ ).startswith('mps' ):
lowerCamelCase_ = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'prompt': 'A hammer hitting a wooden surface',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
}
return inputs
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
lowerCamelCase_ = audio[:10]
lowerCamelCase_ = np.array(
[-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 3 * [inputs['prompt']]
# forward
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 3 * [inputs.pop('prompt' )]
lowerCamelCase_ = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = text_inputs['input_ids'].to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
lowerCamelCase_ = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
lowerCamelCase_ = prompt_embeds
# forward
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 3 * ['this is a negative prompt']
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = 3 * [inputs['prompt']]
# forward
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 3 * [inputs.pop('prompt' )]
lowerCamelCase_ = []
for p in [prompt, negative_prompt]:
lowerCamelCase_ = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = text_inputs['input_ids'].to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
lowerCamelCase_ = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = embeds
# forward
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 'egg cracking'
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
lowerCamelCase_ = audio[:10]
lowerCamelCase_ = np.array(
[-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 'A hammer hitting a wooden surface'
# test num_waveforms_per_prompt=1 (default)
lowerCamelCase_ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
lowerCamelCase_ = 2
lowerCamelCase_ = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
lowerCamelCase_ = 2
lowerCamelCase_ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
lowerCamelCase_ = 2
lowerCamelCase_ = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.vocoder.config.sampling_rate
lowerCamelCase_ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
lowerCamelCase_ = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ['hey']
lowerCamelCase_ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
lowerCamelCase_ = output.audios.shape
assert audio_shape == (1, 256)
lowerCamelCase_ = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
lowerCamelCase_ = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
lowerCamelCase_ = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ) -> int:
'''simple docstring'''
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
lowerCamelCase_ = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'prompt': 'A hammer hitting a wooden surface',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 2.5,
}
return inputs
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 25
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 81920
lowerCamelCase_ = audio[77230:77240]
lowerCamelCase_ = np.array(
[-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
lowerCamelCase_ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
lowerCamelCase_ = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
lowerCamelCase_ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_inputs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 81920
lowerCamelCase_ = audio[27780:27790]
lowerCamelCase_ = np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
lowerCamelCase_ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2
| 42 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = str(__UpperCamelCase )
return len(__UpperCamelCase ) == 9 and set(__UpperCamelCase ) == set('123456789' )
def _UpperCamelCase ( ) -> int | None:
for base_num in range(99_99 ,49_99 ,-1 ):
lowerCamelCase_ = 10_00_02 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
for base_num in range(3_33 ,99 ,-1 ):
lowerCamelCase_ = 1_00_20_03 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
A_ = "\nimport os\n"
A_ = "\ndef foo():\n import os\n return False\n"
A_ = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
A_ = "\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n"
A_ = "\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n"
A_ = "\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n"
A_ = "\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n"
A_ = "\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n"
A_ = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n"
A_ = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n"
A_ = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('case' ,__UpperCamelCase )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = os.path.join(__UpperCamelCase ,'test_file.py' )
with open(__UpperCamelCase ,'w' ) as _tmp_file:
_tmp_file.write(__UpperCamelCase )
lowerCamelCase_ = get_imports(__UpperCamelCase )
assert parsed_imports == ["os"]
| 42 |
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = spanish_id.replace('-' ,'' ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
lowerCamelCase_ = int(spanish_id_clean[0:8] )
lowerCamelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(__UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ProphetNetTokenizer
SCREAMING_SNAKE_CASE_ = False
def UpperCamelCase( self ) -> int:
'''simple docstring'''
super().setUp()
lowerCamelCase_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = 'unwanted, running'
return input_text, output_text
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class(self.vocab_file )
lowerCamelCase_ = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
lowerCamelCase_ = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = i
lowerCamelCase_ = WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
@require_torch
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
lowerCamelCase_ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase_ = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(batch.input_ids.numpy()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
@slow
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
lowerCamelCase_ = tokenizer.encode('sequence builders' , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode('multi-sequence build' , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 42 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 | 1 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _UpperCamelCase ( __UpperCamelCase ) -> Optional[Any]:
return EnvironmentCommand()
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def UpperCamelCase( SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = parser.add_parser('env' )
download_parser.set_defaults(func=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = huggingface_hub.__version__
lowerCamelCase_ = 'not installed'
lowerCamelCase_ = 'NA'
if is_torch_available():
import torch
lowerCamelCase_ = torch.__version__
lowerCamelCase_ = torch.cuda.is_available()
lowerCamelCase_ = 'not installed'
if is_transformers_available():
import transformers
lowerCamelCase_ = transformers.__version__
lowerCamelCase_ = 'not installed'
if is_accelerate_available():
import accelerate
lowerCamelCase_ = accelerate.__version__
lowerCamelCase_ = 'not installed'
if is_xformers_available():
import xformers
lowerCamelCase_ = xformers.__version__
lowerCamelCase_ = {
'`diffusers` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'PyTorch version (GPU?)': f'''{pt_version} ({pt_cuda_available})''',
'Huggingface_hub version': hub_version,
'Transformers version': transformers_version,
'Accelerate version': accelerate_version,
'xFormers version': xformers_version,
'Using GPU in script?': '<fill in>',
'Using distributed or parallel set-up in script?': '<fill in>',
}
print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n' )
print(self.format_dict(SCREAMING_SNAKE_CASE_ ) )
return info
@staticmethod
def UpperCamelCase( SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 42 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = "auto" ) -> List[str]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = 7.5 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
# get prompt text embeddings
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
lowerCamelCase_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase_ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowerCamelCase_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = text_embeddings.shape
lowerCamelCase_ = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ = 42
if negative_prompt is None:
lowerCamelCase_ = ['']
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = text_input_ids.shape[-1]
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ = uncond_embeddings.shape[1]
lowerCamelCase_ = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowerCamelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase_ = latents_reference.to(self.device )
lowerCamelCase_ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowerCamelCase_ = (latents_shape[3] - latents_shape_reference[3]) // 2
lowerCamelCase_ = (latents_shape[2] - latents_shape_reference[2]) // 2
lowerCamelCase_ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowerCamelCase_ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowerCamelCase_ = 0 if dx < 0 else dx
lowerCamelCase_ = 0 if dy < 0 else dy
lowerCamelCase_ = max(-dx , 0 )
lowerCamelCase_ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowerCamelCase_ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase_ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 1 / 0.18_215 * latents
lowerCamelCase_ = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowerCamelCase_ = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) , return_tensors='pt' ).to(
self.device )
lowerCamelCase_ ,lowerCamelCase_ = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowerCamelCase_ = None
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ , nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class UpperCAmelCase :
'''simple docstring'''
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
return None
class UpperCAmelCase :
'''simple docstring'''
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return None
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(SCREAMING_SNAKE_CASE_ , 'tf' , 12 , **SCREAMING_SNAKE_CASE_ )
@require_torch
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(SCREAMING_SNAKE_CASE_ , 'pt' , 12 , **SCREAMING_SNAKE_CASE_ )
@require_torch
@slow
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
from transformers import BertModel
lowerCamelCase_ = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(SCREAMING_SNAKE_CASE_ ) )
vocab_file.flush()
lowerCamelCase_ = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCamelCase_ = BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE_ ) ) )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
self._test_export(SCREAMING_SNAKE_CASE_ , 'pt' , 12 , SCREAMING_SNAKE_CASE_ )
@require_tf
@slow
def UpperCamelCase( self ) -> int:
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase_ = self._test_export(SCREAMING_SNAKE_CASE_ , 'tf' , 12 , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = quantize(Path(SCREAMING_SNAKE_CASE_ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase_ = self._test_export(SCREAMING_SNAKE_CASE_ , 'pt' , 12 , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = quantize(SCREAMING_SNAKE_CASE_ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCamelCase_ = Path(SCREAMING_SNAKE_CASE_ ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
return path
except Exception as e:
self.fail(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_tokenizers
@slow
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
from transformers import BertModel
lowerCamelCase_ = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase_ = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 'pt' )
@require_tf
@require_tokenizers
@slow
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
from transformers import TFBertModel
lowerCamelCase_ = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase_ = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 'tf' )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = FeatureExtractionPipeline(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = infer_shapes(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Assert all variables are present
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , SCREAMING_SNAKE_CASE_ )
self.assertSequenceEqual(variable_names[3:] , SCREAMING_SNAKE_CASE_ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: 'batch', 1: 'sequence'} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['output_0'] , {0: 'batch', 1: 'sequence'} )
self.assertDictEqual(shapes['output_1'] , {0: 'batch'} )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = ['input_ids', 'attention_mask', 'token_type_ids']
lowerCamelCase_ = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
lowerCamelCase_ ,lowerCamelCase_ = ensure_valid_input(FuncContiguousArgs() , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(SCREAMING_SNAKE_CASE_ ) , set(SCREAMING_SNAKE_CASE_ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(SCREAMING_SNAKE_CASE_ , (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCamelCase_ ,lowerCamelCase_ = ensure_valid_input(FuncNonContiguousArgs() , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1 )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['input_ids'] )
self.assertEqual(ordered_input_names[0] , 'input_ids' )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ) , '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx' , generated.as_posix() )
| 42 |
'''simple docstring'''
import pprint
import requests
A_ = "https://zenquotes.io/api"
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
A_ = random_quotes()
pprint.pprint(response)
| 42 | 1 |
'''simple docstring'''
import math
class UpperCAmelCase :
'''simple docstring'''
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ = 0.0
lowerCamelCase_ = 0.0
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[list[int | float]]:
'''simple docstring'''
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def _UpperCamelCase ( ) -> None:
# Training Examples ( m, n )
lowerCamelCase_ = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
lowerCamelCase_ = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
lowerCamelCase_ = SelfOrganizingMap()
lowerCamelCase_ = 3
lowerCamelCase_ = 0.5
for _ in range(__UpperCamelCase ):
for j in range(len(__UpperCamelCase ) ):
# training sample
lowerCamelCase_ = training_samples[j]
# Compute the winning vector
lowerCamelCase_ = self_organizing_map.get_winner(__UpperCamelCase ,__UpperCamelCase )
# Update the winning vector
lowerCamelCase_ = self_organizing_map.update(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# classify test sample
lowerCamelCase_ = [0, 0, 0, 1]
lowerCamelCase_ = self_organizing_map.get_winner(__UpperCamelCase ,__UpperCamelCase )
# results
print(f'''Clusters that the test sample belongs to : {winner}''' )
print(f'''Weights that have been trained : {weights}''' )
# running the main() function
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=33 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ) -> int:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = EsmModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = EsmForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = EsmForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = ()
SCREAMING_SNAKE_CASE_ = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = EsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase_ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = EsmModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowerCamelCase_ = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowerCamelCase_ = create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE_ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.empty(2 , 4 , 30 )
lowerCamelCase_ = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowerCamelCase_ = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowerCamelCase_ = embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE_ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = 33
lowerCamelCase_ = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
# compare the actual values for a slice.
lowerCamelCase_ = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCamelCase ( __UpperCamelCase ) -> Tuple:
return np.maximum(0 ,__UpperCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 42 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
A_ = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> None:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = size if size is not None else {'shortest_edge': 384}
lowerCamelCase_ = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = do_resize
lowerCamelCase_ = size
# Default value set here for backwards compatibility where the value in config is None
lowerCamelCase_ = crop_pct if crop_pct is not None else 224 / 256
lowerCamelCase_ = resample
lowerCamelCase_ = do_rescale
lowerCamelCase_ = rescale_factor
lowerCamelCase_ = do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCamelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> np.ndarray:
'''simple docstring'''
lowerCamelCase_ = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''' )
lowerCamelCase_ = size['shortest_edge']
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
lowerCamelCase_ = int(shortest_edge / crop_pct )
lowerCamelCase_ = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=SCREAMING_SNAKE_CASE_ , size=(shortest_edge, shortest_edge) , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
SCREAMING_SNAKE_CASE_ , size=(shortest_edge, shortest_edge) , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> np.ndarray:
'''simple docstring'''
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ) -> PIL.Image.Image:
'''simple docstring'''
lowerCamelCase_ = do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ = crop_pct if crop_pct is not None else self.crop_pct
lowerCamelCase_ = resample if resample is not None else self.resample
lowerCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ = image_std if image_std is not None else self.image_std
lowerCamelCase_ = size if size is not None else self.size
lowerCamelCase_ = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError('crop_pct must be specified if size < 384.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowerCamelCase_ = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
lowerCamelCase_ = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , crop_pct=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
lowerCamelCase_ = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
lowerCamelCase_ = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
lowerCamelCase_ = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
lowerCamelCase_ = {'pixel_values': images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
| 42 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'xlm-prophetnet'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 30522 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2 , **SCREAMING_SNAKE_CASE_ , ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = num_encoder_layers
lowerCamelCase_ = num_encoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = num_decoder_layers
lowerCamelCase_ = num_decoder_attention_heads
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = init_std # Normal(0, this parameter)
lowerCamelCase_ = activation_function
# parameters for xlmprophetnet
lowerCamelCase_ = ngram
lowerCamelCase_ = num_buckets
lowerCamelCase_ = relative_max_distance
lowerCamelCase_ = disable_ngram_loss
lowerCamelCase_ = eps
# 3 Types of Dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = dropout
lowerCamelCase_ = use_cache
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , add_cross_attention=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 42 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
A_ = 128_022
A_ = 128_028
@require_sentencepiece
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MaMaaaTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
super().setUp()
lowerCamelCase_ = ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = Path(self.tmpdirname )
save_json(SCREAMING_SNAKE_CASE_ , save_dir / VOCAB_FILES_NAMES['vocab_file'] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(SCREAMING_SNAKE_CASE_ , save_dir / VOCAB_FILES_NAMES['spm_file'] )
lowerCamelCase_ = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = '</s>'
lowerCamelCase_ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '</s>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '<s>' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('Skip this test while all models are still to be uploaded.' )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [2, 3, 4, 5, 6] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
lowerCamelCase_ = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , 'This is a test' )
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE_ , model_name='facebook/m2m100_418M' , revision='c168bae485c864188cf9aa0e4108b0b6934dc91e' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/m2m100_418M'
SCREAMING_SNAKE_CASE_ = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
SCREAMING_SNAKE_CASE_ = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
SCREAMING_SNAKE_CASE_ = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def UpperCamelCase( cls ) -> int:
'''simple docstring'''
lowerCamelCase_ = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en' , tgt_lang='fr' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('ar' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('en' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('ro' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('mr' ) , 128063 )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.get_vocab()
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['<unk>'] , 3 )
self.assertIn(self.tokenizer.get_lang_token('en' ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = 'en'
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
# fmt: off
lowerCamelCase_ = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MaMaaaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.lang_token_to_id , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = 'en'
lowerCamelCase_ = 'fr'
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowerCamelCase_ = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'mr'
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('mr' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowerCamelCase_ = 'zh'
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('zh' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'mr'
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('mr' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowerCamelCase_ = 'zh'
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('zh' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs('A test' , return_tensors='pt' , src_lang='en' , tgt_lang='ar' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# en_XX, A, test, EOS
'input_ids': [[128022, 58, 4183, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 128006,
} , )
| 42 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = x
lowerCamelCase_ = y
for step in range(__UpperCamelCase ): # noqa: B007
lowerCamelCase_ = a * a - b * b + x
lowerCamelCase_ = 2 * a * b + y
lowerCamelCase_ = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55 ) for i in colorsys.hsv_to_rgb(__UpperCamelCase ,1 ,1 ) )
def _UpperCamelCase ( __UpperCamelCase = 8_00 ,__UpperCamelCase = 6_00 ,__UpperCamelCase = -0.6 ,__UpperCamelCase = 0 ,__UpperCamelCase = 3.2 ,__UpperCamelCase = 50 ,__UpperCamelCase = True ,) -> Image.Image:
lowerCamelCase_ = Image.new('RGB' ,(image_width, image_height) )
lowerCamelCase_ = img.load()
# loop through the image-coordinates
for image_x in range(__UpperCamelCase ):
for image_y in range(__UpperCamelCase ):
# determine the figure-coordinates based on the image-coordinates
lowerCamelCase_ = figure_width / image_width * image_height
lowerCamelCase_ = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowerCamelCase_ = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowerCamelCase_ = get_distance(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowerCamelCase_ = get_color_coded_rgb(__UpperCamelCase )
else:
lowerCamelCase_ = get_black_and_white_rgb(__UpperCamelCase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
A_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 42 | 1 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> Union[str, Any]:
with open(os.path.dirname(__UpperCamelCase ) + '/grid.txt' ) as f:
lowerCamelCase_ = [] # noqa: E741
for _ in range(20 ):
l.append([int(__UpperCamelCase ) for x in f.readline().split()] )
lowerCamelCase_ = 0
# right
for i in range(20 ):
for j in range(17 ):
lowerCamelCase_ = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
lowerCamelCase_ = temp
# down
for i in range(17 ):
for j in range(20 ):
lowerCamelCase_ = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
lowerCamelCase_ = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
lowerCamelCase_ = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
lowerCamelCase_ = temp
# diagonal 2
for i in range(17 ):
for j in range(3 ,20 ):
lowerCamelCase_ = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
lowerCamelCase_ = temp
return maximum
if __name__ == "__main__":
print(solution())
| 42 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase_ = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase_ = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase_ = outgoing_gradient**2 + 4
lowerCamelCase_ = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase_ = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase_ = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase_ = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase_ = x_minus if isclose(__UpperCamelCase ,__UpperCamelCase ) else x_plus
lowerCamelCase_ = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _UpperCamelCase ( __UpperCamelCase = 1.4 ,__UpperCamelCase = -9.6 ) -> int:
lowerCamelCase_ = 0
lowerCamelCase_ = first_x_coord
lowerCamelCase_ = first_y_coord
lowerCamelCase_ = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = next_point(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'ylacombe/bark-small'
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = 'en_speaker_1'
lowerCamelCase_ = 'This is a test string'
lowerCamelCase_ = 'speaker_embeddings_path.json'
lowerCamelCase_ = 'speaker_embeddings'
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowerCamelCase_ = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCamelCase_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowerCamelCase_ = 35
lowerCamelCase_ = 2
lowerCamelCase_ = 8
lowerCamelCase_ = {
'semantic_prompt': np.ones(SCREAMING_SNAKE_CASE_ ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase_ = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string )
lowerCamelCase_ = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 42 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
raise ValueError(
'Warning: upper bound of deterministic test is exceeded. '
'Pass allow_probable=True to allow probabilistic test. '
'A return value of True indicates a probable prime.' )
# array bounds provided by analysis
lowerCamelCase_ = [
20_47,
1_37_36_53,
25_32_60_01,
32_15_03_17_51,
2_15_23_02_89_87_47,
3_47_47_49_66_03_83,
3_41_55_00_71_72_83_21,
1,
3_82_51_23_05_65_46_41_30_51,
1,
1,
31_86_65_85_78_34_03_11_51_16_74_61,
3_31_70_44_06_46_79_88_73_85_96_19_81,
]
lowerCamelCase_ = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__UpperCamelCase ,1 ):
if n < _p:
# then we have our last prime to check
lowerCamelCase_ = primes[:idx]
break
lowerCamelCase_ ,lowerCamelCase_ = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
lowerCamelCase_ = False
for r in range(__UpperCamelCase ):
lowerCamelCase_ = pow(__UpperCamelCase ,d * 2**r ,__UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
lowerCamelCase_ = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def _UpperCamelCase ( ) -> None:
assert not miller_rabin(5_61 )
assert miller_rabin(5_63 )
# 2047
assert not miller_rabin(83_82_01 )
assert miller_rabin(83_82_07 )
# 1_373_653
assert not miller_rabin(17_31_60_01 )
assert miller_rabin(17_31_60_17 )
# 25_326_001
assert not miller_rabin(30_78_38_66_41 )
assert miller_rabin(30_78_38_66_53 )
# 3_215_031_751
assert not miller_rabin(1_71_30_45_57_48_01 )
assert miller_rabin(1_71_30_45_57_48_19 )
# 2_152_302_898_747
assert not miller_rabin(2_77_97_99_72_83_07 )
assert miller_rabin(2_77_97_99_72_83_27 )
# 3_474_749_660_383
assert not miller_rabin(1_13_85_00_23_90_94_41 )
assert miller_rabin(1_13_85_00_23_90_95_27 )
# 341_550_071_728_321
assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 42 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=10_24 ) -> str:
lowerCamelCase_ ,lowerCamelCase_ = [], []
lowerCamelCase_ = list(zip(__UpperCamelCase ,__UpperCamelCase ) )
lowerCamelCase_ ,lowerCamelCase_ = sorted_examples[0]
def is_too_big(__UpperCamelCase ):
return tok(__UpperCamelCase ,return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
lowerCamelCase_ = new_src + ' ' + src
lowerCamelCase_ = new_tgt + ' ' + tgt
if is_too_big(__UpperCamelCase ) or is_too_big(__UpperCamelCase ): # cant fit, finalize example
finished_src.append(__UpperCamelCase )
finished_tgt.append(__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = src, tgt
else: # can fit, keep adding
lowerCamelCase_ ,lowerCamelCase_ = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__UpperCamelCase )
finished_tgt.append(__UpperCamelCase )
return finished_src, finished_tgt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = Path(__UpperCamelCase )
save_path.mkdir(exist_ok=__UpperCamelCase )
for split in ["train"]:
lowerCamelCase_ ,lowerCamelCase_ = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
lowerCamelCase_ = [x.rstrip() for x in Path(__UpperCamelCase ).open().readlines()]
lowerCamelCase_ = [x.rstrip() for x in Path(__UpperCamelCase ).open().readlines()]
lowerCamelCase_ ,lowerCamelCase_ = pack_examples(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
print(f'''packed {split} split from {len(__UpperCamelCase )} examples -> {len(__UpperCamelCase )}.''' )
Path(save_path / f'''{split}.source''' ).open('w' ).write('\n'.join(__UpperCamelCase ) )
Path(save_path / f'''{split}.target''' ).open('w' ).write('\n'.join(__UpperCamelCase ) )
for split in ["val", "test"]:
lowerCamelCase_ ,lowerCamelCase_ = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
shutil.copyfile(__UpperCamelCase ,save_path / f'''{split}.source''' )
shutil.copyfile(__UpperCamelCase ,save_path / f'''{split}.target''' )
def _UpperCamelCase ( ) -> List[str]:
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('--tok_name' ,type=__UpperCamelCase ,help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len' ,type=__UpperCamelCase ,default=1_28 )
parser.add_argument('--data_dir' ,type=__UpperCamelCase )
parser.add_argument('--save_path' ,type=__UpperCamelCase )
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__UpperCamelCase ,Path(args.data_dir ) ,args.max_seq_len ,args.save_path )
if __name__ == "__main__":
packer_cli()
| 42 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ = 16
A_ = 32
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 16 ,__UpperCamelCase = "bert-base-cased" ) -> List[Any]:
lowerCamelCase_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = load_dataset('glue' ,'mrpc' )
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase_ = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCamelCase_ = datasets.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,load_from_cache_file=__UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase_ = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(__UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__UpperCamelCase ,padding='max_length' ,max_length=1_28 ,return_tensors='pt' )
return tokenizer.pad(__UpperCamelCase ,padding='longest' ,return_tensors='pt' )
# Instantiate dataloaders.
lowerCamelCase_ = DataLoader(
tokenized_datasets['train'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
lowerCamelCase_ = DataLoader(
tokenized_datasets['validation'] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
model.eval()
lowerCamelCase_ = 0
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCamelCase_ ,lowerCamelCase_ = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__UpperCamelCase ) - 1:
lowerCamelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCamelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__UpperCamelCase ,references=__UpperCamelCase ,)
lowerCamelCase_ = metric.compute()
return eval_metric["accuracy"]
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[str]:
# Initialize accelerator
lowerCamelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase_ = config['lr']
lowerCamelCase_ = int(config['num_epochs'] )
lowerCamelCase_ = int(config['seed'] )
lowerCamelCase_ = int(config['batch_size'] )
lowerCamelCase_ = args.model_name_or_path
set_seed(__UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = get_dataloaders(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,return_dict=__UpperCamelCase )
# Instantiate optimizer
lowerCamelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase_ = optimizer_cls(params=model.parameters() ,lr=__UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowerCamelCase_ = 1
lowerCamelCase_ = (len(__UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase_ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase ,num_warmup_steps=0 ,num_training_steps=__UpperCamelCase ,)
else:
lowerCamelCase_ = DummyScheduler(__UpperCamelCase ,total_num_steps=__UpperCamelCase ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = accelerator.prepare(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase_ = 0
lowerCamelCase_ = evaluate.load('glue' ,'mrpc' )
lowerCamelCase_ = num_epochs
if args.partial_train_epoch is not None:
lowerCamelCase_ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCamelCase_ = args.resume_from_checkpoint.split('epoch_' )[1]
lowerCamelCase_ = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCamelCase_ = int(__UpperCamelCase ) + 1
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
accelerator.print('resumed checkpoint performance:' ,__UpperCamelCase )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' ,lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' ,optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir ,f'''state_{starting_epoch-1}.json''' ) ,'r' ) as f:
lowerCamelCase_ = json.load(__UpperCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCamelCase_ = {}
for epoch in range(__UpperCamelCase ,__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
lowerCamelCase_ = model(**__UpperCamelCase )
lowerCamelCase_ = outputs.loss
lowerCamelCase_ = loss / gradient_accumulation_steps
accelerator.backward(__UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCamelCase_ = f'''epoch_{epoch}'''
lowerCamelCase_ = os.path.join(args.output_dir ,__UpperCamelCase )
accelerator.save_state(__UpperCamelCase )
lowerCamelCase_ = evaluation_loop(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = accuracy
lowerCamelCase_ = lr_scheduler.get_lr()[0]
lowerCamelCase_ = optimizer.param_groups[0]['lr']
lowerCamelCase_ = epoch
lowerCamelCase_ = overall_step
accelerator.print(f'''epoch {epoch}:''' ,__UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,f'''state_{epoch}.json''' ) ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> str:
lowerCamelCase_ = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' ,type=__UpperCamelCase ,default='bert-base-cased' ,help='Path to pretrained model or model identifier from huggingface.co/models.' ,required=__UpperCamelCase ,)
parser.add_argument(
'--output_dir' ,type=__UpperCamelCase ,default='.' ,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,)
parser.add_argument(
'--resume_from_checkpoint' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If the training should continue from a checkpoint folder.' ,)
parser.add_argument(
'--partial_train_epoch' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='If passed, the training will stop after this number of epochs.' ,)
parser.add_argument(
'--num_epochs' ,type=__UpperCamelCase ,default=2 ,help='Number of train epochs.' ,)
lowerCamelCase_ = parser.parse_args()
lowerCamelCase_ = {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A_ = 250_004
A_ = 250_020
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MBartTokenizer
SCREAMING_SNAKE_CASE_ = MBartTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/mbart-large-en-ro'
SCREAMING_SNAKE_CASE_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE_ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 250020 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 42 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> np.ndarray:
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
lowerCamelCase_ = ksize + 1
lowerCamelCase_ = np.zeros((ksize, ksize) ,dtype=np.floataa )
# each value
for y in range(__UpperCamelCase ):
for x in range(__UpperCamelCase ):
# distance from center
lowerCamelCase_ = x - ksize // 2
lowerCamelCase_ = y - ksize // 2
# degree to radiant
lowerCamelCase_ = theta / 1_80 * np.pi
lowerCamelCase_ = np.cos(_theta )
lowerCamelCase_ = np.sin(_theta )
# get kernel x
lowerCamelCase_ = cos_theta * px + sin_theta * py
# get kernel y
lowerCamelCase_ = -sin_theta * px + cos_theta * py
# fill kernel
lowerCamelCase_ = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
A_ = imread("../image_data/lena.jpg")
# turn image in gray scale value
A_ = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
A_ = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
A_ = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
A_ = out / out.max() * 255
A_ = out.astype(np.uinta)
imshow("Original", gray)
imshow("Gabor filter with 20x20 mask and 6 directions", out)
waitKey(0)
| 42 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ = logging.get_logger(__name__)
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> str:
lowerCamelCase_ = WavaVecaForSequenceClassification.from_pretrained(__UpperCamelCase ,config=__UpperCamelCase )
lowerCamelCase_ = downstream_dict['projector.weight']
lowerCamelCase_ = downstream_dict['projector.bias']
lowerCamelCase_ = downstream_dict['model.post_net.linear.weight']
lowerCamelCase_ = downstream_dict['model.post_net.linear.bias']
return model
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
lowerCamelCase_ = WavaVecaForAudioFrameClassification.from_pretrained(__UpperCamelCase ,config=__UpperCamelCase )
lowerCamelCase_ = downstream_dict['model.linear.weight']
lowerCamelCase_ = downstream_dict['model.linear.bias']
return model
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
lowerCamelCase_ = WavaVecaForXVector.from_pretrained(__UpperCamelCase ,config=__UpperCamelCase )
lowerCamelCase_ = downstream_dict['connector.weight']
lowerCamelCase_ = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowerCamelCase_ = downstream_dict[
f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
lowerCamelCase_ = downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
lowerCamelCase_ = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
lowerCamelCase_ = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
lowerCamelCase_ = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
lowerCamelCase_ = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
lowerCamelCase_ = downstream_dict['objective.W']
return model
@torch.no_grad()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Dict:
lowerCamelCase_ = torch.load(__UpperCamelCase ,map_location='cpu' )
lowerCamelCase_ = checkpoint['Downstream']
lowerCamelCase_ = WavaVecaConfig.from_pretrained(__UpperCamelCase )
lowerCamelCase_ = WavaVecaFeatureExtractor.from_pretrained(
__UpperCamelCase ,return_attention_mask=__UpperCamelCase ,do_normalize=__UpperCamelCase )
lowerCamelCase_ = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
lowerCamelCase_ = convert_classification(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
elif arch.endswith('ForAudioFrameClassification' ):
lowerCamelCase_ = convert_diarization(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
elif arch.endswith('ForXVector' ):
lowerCamelCase_ = convert_xvector(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
else:
raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
lowerCamelCase_ = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(__UpperCamelCase )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
A_ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 42 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['transformers', 'torch', 'note_seq']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"processing_clipseg": ["CLIPSegProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
"CLIPSegModel",
"CLIPSegPreTrainedModel",
"CLIPSegTextModel",
"CLIPSegVisionModel",
"CLIPSegForImageSegmentation",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=10 , )
return model
@property
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
lowerCamelCase_ = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
lowerCamelCase_ = DDPMScheduler()
lowerCamelCase_ = AudioDiffusionPipeline(vqvae=SCREAMING_SNAKE_CASE_ , unet=self.dummy_unet , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , steps=4 , return_dict=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = self.dummy_vqvae_and_unet
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(raw_audio=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , start_step=5 , steps=10 )
lowerCamelCase_ = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
lowerCamelCase_ = self.dummy_unet_condition
lowerCamelCase_ = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=SCREAMING_SNAKE_CASE_ , mel=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
np.random.seed(0 )
lowerCamelCase_ = torch.rand((1, 1, 10) )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ , encoding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.images[0]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = torch_device
lowerCamelCase_ = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
lowerCamelCase_ = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(42 )
lowerCamelCase_ = pipe(generator=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = output.audios[0]
lowerCamelCase_ = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
lowerCamelCase_ = np.frombuffer(image.tobytes() , dtype='uint8' )[:10]
lowerCamelCase_ = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["XLNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["XLNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLNetForMultipleChoice",
"XLNetForQuestionAnswering",
"XLNetForQuestionAnsweringSimple",
"XLNetForSequenceClassification",
"XLNetForTokenClassification",
"XLNetLMHeadModel",
"XLNetModel",
"XLNetPreTrainedModel",
"load_tf_weights_in_xlnet",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLNetForMultipleChoice",
"TFXLNetForQuestionAnsweringSimple",
"TFXLNetForSequenceClassification",
"TFXLNetForTokenClassification",
"TFXLNetLMHeadModel",
"TFXLNetMainLayer",
"TFXLNetModel",
"TFXLNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _UpperCamelCase ( __UpperCamelCase = 8 ) -> str:
lowerCamelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(__UpperCamelCase )
lowerCamelCase_ = i // 3
lowerCamelCase_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowerCamelCase_ = (
chars_incl
+ random(__UpperCamelCase ,quotient + remainder )
+ random(__UpperCamelCase ,__UpperCamelCase )
+ random(__UpperCamelCase ,__UpperCamelCase )
)
lowerCamelCase_ = list(__UpperCamelCase )
shuffle(__UpperCamelCase )
return "".join(__UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Tuple:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> str:
pass # Put your code here...
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase = 8 ) -> bool:
if len(__UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowerCamelCase_ = any(char in ascii_uppercase for char in password )
lowerCamelCase_ = any(char in ascii_lowercase for char in password )
lowerCamelCase_ = any(char in digits for char in password )
lowerCamelCase_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _UpperCamelCase ( ) -> Optional[int]:
lowerCamelCase_ = int(input('Please indicate the max length of your password: ' ).strip() )
lowerCamelCase_ = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' ,password_generator(__UpperCamelCase ) )
print(
'Alternative Password generated:' ,alternative_password_generator(__UpperCamelCase ,__UpperCamelCase ) ,)
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 42 | 1 |
'''simple docstring'''
from collections import deque
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
lowerCamelCase_ = process_name # process name
lowerCamelCase_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
lowerCamelCase_ = arrival_time
lowerCamelCase_ = burst_time # remaining burst time
lowerCamelCase_ = 0 # total time of the process wait in ready queue
lowerCamelCase_ = 0 # time from arrival time to completion time
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> None:
'''simple docstring'''
lowerCamelCase_ = number_of_queues
# time slice of queues that round robin algorithm applied
lowerCamelCase_ = time_slices
# unfinished process is in this ready_queue
lowerCamelCase_ = queue
# current time
lowerCamelCase_ = current_time
# finished process is in this sequence queue
lowerCamelCase_ = deque()
def UpperCamelCase( self ) -> list[str]:
'''simple docstring'''
lowerCamelCase_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> list[int]:
'''simple docstring'''
lowerCamelCase_ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> list[int]:
'''simple docstring'''
lowerCamelCase_ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> list[int]:
'''simple docstring'''
lowerCamelCase_ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> list[int]:
'''simple docstring'''
return [q.burst_time for q in queue]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> deque[Process]:
'''simple docstring'''
lowerCamelCase_ = deque() # sequence deque of finished process
while len(SCREAMING_SNAKE_CASE_ ) != 0:
lowerCamelCase_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(SCREAMING_SNAKE_CASE_ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
lowerCamelCase_ = 0
# set the process's turnaround time because it is finished
lowerCamelCase_ = self.current_time - cp.arrival_time
# set the completion time
lowerCamelCase_ = self.current_time
# add the process to queue that has finished queue
finished.append(SCREAMING_SNAKE_CASE_ )
self.finish_queue.extend(SCREAMING_SNAKE_CASE_ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> tuple[deque[Process], deque[Process]]:
'''simple docstring'''
lowerCamelCase_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(SCREAMING_SNAKE_CASE_ ) ):
lowerCamelCase_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(SCREAMING_SNAKE_CASE_ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
lowerCamelCase_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(SCREAMING_SNAKE_CASE_ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
lowerCamelCase_ = 0
# set the finish time
lowerCamelCase_ = self.current_time
# update the process' turnaround time because it is finished
lowerCamelCase_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(SCREAMING_SNAKE_CASE_ )
self.finish_queue.extend(SCREAMING_SNAKE_CASE_ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def UpperCamelCase( self ) -> deque[Process]:
'''simple docstring'''
for i in range(self.number_of_queues - 1 ):
lowerCamelCase_ ,lowerCamelCase_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A_ = Process("P1", 0, 53)
A_ = Process("P2", 0, 17)
A_ = Process("P3", 0, 68)
A_ = Process("P4", 0, 24)
A_ = 3
A_ = [17, 25]
A_ = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A_ = Process("P1", 0, 53)
A_ = Process("P2", 0, 17)
A_ = Process("P3", 0, 68)
A_ = Process("P4", 0, 24)
A_ = 3
A_ = [17, 25]
A_ = deque([Pa, Pa, Pa, Pa])
A_ = MLFQ(number_of_queues, time_slices, queue, 0)
A_ = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
f'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
f'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 42 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowerCamelCase_ = test_metrics
@require_cpu
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
debug_launcher(self.test_metrics.main )
@require_single_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
self.test_metrics.main()
@require_multi_gpu
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
print(f'''Found {torch.cuda.device_count()} devices.''' )
lowerCamelCase_ = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=os.environ.copy() )
| 42 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
A_ = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
A_ = {
"allenai/longformer-base-4096": 4_096,
"allenai/longformer-large-4096": 4_096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = (
list(range(ord('!' ) ,ord('~' ) + 1 ) ) + list(range(ord('¡' ) ,ord('¬' ) + 1 ) ) + list(range(ord('®' ) ,ord('ÿ' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase ,__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as vocab_handle:
lowerCamelCase_ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ ,lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCamelCase_ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ' '.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = word
return word
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(' ' ) )
return bpe_tokens
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = ''.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '\n' )
lowerCamelCase_ = 0
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase_ = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE_ ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ' ' + text
return (text, kwargs)
| 42 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
if hor == 1_28:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
lowerCamelCase_ = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
lowerCamelCase_ = (32, 64, 1_28, 2_56)
lowerCamelCase_ = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
lowerCamelCase_ = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
lowerCamelCase_ = model.state_dict()
lowerCamelCase_ = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 6_55_36,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
def _UpperCamelCase ( ) -> Tuple:
lowerCamelCase_ = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 1_28, 2_56),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 6_55_36,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
lowerCamelCase_ = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
lowerCamelCase_ = model
lowerCamelCase_ = UNetaDModel(**__UpperCamelCase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
lowerCamelCase_ = dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase_ = state_dict.pop(__UpperCamelCase )
hf_value_function.load_state_dict(__UpperCamelCase )
torch.save(hf_value_function.state_dict() ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin' )
with open('hub/hopper-medium-v2/value_function/config.json' ,'w' ) as f:
json.dump(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 42 | 1 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
A_ = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _UpperCamelCase ( __UpperCamelCase ) -> int:
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
if args.student_type == "roberta":
lowerCamelCase_ = False
elif args.student_type == "gpt2":
lowerCamelCase_ = False
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Any:
if args.student_type == "roberta":
lowerCamelCase_ = False
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = argparse.ArgumentParser(description='Training' )
parser.add_argument('--force' ,action='store_true' ,help='Overwrite dump_path if it already exists.' )
parser.add_argument(
'--dump_path' ,type=__UpperCamelCase ,required=__UpperCamelCase ,help='The output directory (log, checkpoints, parameters, etc.)' )
parser.add_argument(
'--data_file' ,type=__UpperCamelCase ,required=__UpperCamelCase ,help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' ,)
parser.add_argument(
'--student_type' ,type=__UpperCamelCase ,choices=['distilbert', 'roberta', 'gpt2'] ,required=__UpperCamelCase ,help='The student type (DistilBERT, RoBERTa).' ,)
parser.add_argument('--student_config' ,type=__UpperCamelCase ,required=__UpperCamelCase ,help='Path to the student configuration.' )
parser.add_argument(
'--student_pretrained_weights' ,default=__UpperCamelCase ,type=__UpperCamelCase ,help='Load student initialization checkpoint.' )
parser.add_argument(
'--teacher_type' ,choices=['bert', 'roberta', 'gpt2'] ,required=__UpperCamelCase ,help='Teacher type (BERT, RoBERTa).' )
parser.add_argument('--teacher_name' ,type=__UpperCamelCase ,required=__UpperCamelCase ,help='The teacher model.' )
parser.add_argument('--temperature' ,default=2.0 ,type=__UpperCamelCase ,help='Temperature for the softmax temperature.' )
parser.add_argument(
'--alpha_ce' ,default=0.5 ,type=__UpperCamelCase ,help='Linear weight for the distillation loss. Must be >=0.' )
parser.add_argument(
'--alpha_mlm' ,default=0.0 ,type=__UpperCamelCase ,help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' ,)
parser.add_argument('--alpha_clm' ,default=0.5 ,type=__UpperCamelCase ,help='Linear weight for the CLM loss. Must be >=0.' )
parser.add_argument('--alpha_mse' ,default=0.0 ,type=__UpperCamelCase ,help='Linear weight of the MSE loss. Must be >=0.' )
parser.add_argument(
'--alpha_cos' ,default=0.0 ,type=__UpperCamelCase ,help='Linear weight of the cosine embedding loss. Must be >=0.' )
parser.add_argument(
'--mlm' ,action='store_true' ,help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
parser.add_argument(
'--mlm_mask_prop' ,default=0.15 ,type=__UpperCamelCase ,help='Proportion of tokens for which we need to make a prediction.' ,)
parser.add_argument('--word_mask' ,default=0.8 ,type=__UpperCamelCase ,help='Proportion of tokens to mask out.' )
parser.add_argument('--word_keep' ,default=0.1 ,type=__UpperCamelCase ,help='Proportion of tokens to keep.' )
parser.add_argument('--word_rand' ,default=0.1 ,type=__UpperCamelCase ,help='Proportion of tokens to randomly replace.' )
parser.add_argument(
'--mlm_smoothing' ,default=0.7 ,type=__UpperCamelCase ,help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' ,)
parser.add_argument('--token_counts' ,type=__UpperCamelCase ,help='The token counts in the data_file for MLM.' )
parser.add_argument(
'--restrict_ce_to_mask' ,action='store_true' ,help='If true, compute the distillation loss only the [MLM] prediction distribution.' ,)
parser.add_argument(
'--freeze_pos_embs' ,action='store_true' ,help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' ,)
parser.add_argument(
'--freeze_token_type_embds' ,action='store_true' ,help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' ,)
parser.add_argument('--n_epoch' ,type=__UpperCamelCase ,default=3 ,help='Number of pass on the whole dataset.' )
parser.add_argument('--batch_size' ,type=__UpperCamelCase ,default=5 ,help='Batch size (for each process).' )
parser.add_argument(
'--group_by_size' ,action='store_false' ,help='If true, group sequences that have similar length into the same batch. Default is true.' ,)
parser.add_argument(
'--gradient_accumulation_steps' ,type=__UpperCamelCase ,default=50 ,help='Gradient accumulation for larger training batches.' ,)
parser.add_argument('--warmup_prop' ,default=0.05 ,type=__UpperCamelCase ,help='Linear warmup proportion.' )
parser.add_argument('--weight_decay' ,default=0.0 ,type=__UpperCamelCase ,help='Weight decay if we apply some.' )
parser.add_argument('--learning_rate' ,default=5e-4 ,type=__UpperCamelCase ,help='The initial learning rate for Adam.' )
parser.add_argument('--adam_epsilon' ,default=1e-6 ,type=__UpperCamelCase ,help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' ,default=5.0 ,type=__UpperCamelCase ,help='Max gradient norm.' )
parser.add_argument('--initializer_range' ,default=0.02 ,type=__UpperCamelCase ,help='Random initialization range.' )
parser.add_argument(
'--fp16' ,action='store_true' ,help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' ,)
parser.add_argument(
'--fp16_opt_level' ,type=__UpperCamelCase ,default='O1' ,help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) ,)
parser.add_argument('--n_gpu' ,type=__UpperCamelCase ,default=1 ,help='Number of GPUs in the node.' )
parser.add_argument('--local_rank' ,type=__UpperCamelCase ,default=-1 ,help='Distributed training - Local rank' )
parser.add_argument('--seed' ,type=__UpperCamelCase ,default=56 ,help='Random seed' )
parser.add_argument('--log_interval' ,type=__UpperCamelCase ,default=5_00 ,help='Tensorboard logging interval.' )
parser.add_argument('--checkpoint_interval' ,type=__UpperCamelCase ,default=40_00 ,help='Checkpoint interval.' )
lowerCamelCase_ = parser.parse_args()
sanity_checks(__UpperCamelCase )
# ARGS #
init_gpu_params(__UpperCamelCase )
set_seed(__UpperCamelCase )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
' itUse `--force` if you want to overwrite it' )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(f'''Param: {args}''' )
with open(os.path.join(args.dump_path ,'parameters.json' ) ,'w' ) as f:
json.dump(vars(__UpperCamelCase ) ,__UpperCamelCase ,indent=4 )
git_log(args.dump_path )
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = MODEL_CLASSES[args.student_type]
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowerCamelCase_ = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowerCamelCase_ = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowerCamelCase_ = tokenizer.all_special_tokens.index(__UpperCamelCase )
lowerCamelCase_ = tokenizer.all_special_ids[idx]
logger.info(f'''Special tokens {special_tok_ids}''' )
lowerCamelCase_ = special_tok_ids
lowerCamelCase_ = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'''Loading data from {args.data_file}''' )
with open(args.data_file ,'rb' ) as fp:
lowerCamelCase_ = pickle.load(__UpperCamelCase )
if args.mlm:
logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts ,'rb' ) as fp:
lowerCamelCase_ = pickle.load(__UpperCamelCase )
lowerCamelCase_ = np.maximum(__UpperCamelCase ,1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowerCamelCase_ = 0.0 # do not predict special tokens
lowerCamelCase_ = torch.from_numpy(__UpperCamelCase )
else:
lowerCamelCase_ = None
lowerCamelCase_ = LmSeqsDataset(params=__UpperCamelCase ,data=__UpperCamelCase )
logger.info('Data loader created.' )
# STUDENT #
logger.info(f'''Loading student config from {args.student_config}''' )
lowerCamelCase_ = student_config_class.from_pretrained(args.student_config )
lowerCamelCase_ = True
if args.student_pretrained_weights is not None:
logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' )
lowerCamelCase_ = student_model_class.from_pretrained(args.student_pretrained_weights ,config=__UpperCamelCase )
else:
lowerCamelCase_ = student_model_class(__UpperCamelCase )
if args.n_gpu > 0:
student.to(f'''cuda:{args.local_rank}''' )
logger.info('Student loaded.' )
# TEACHER #
lowerCamelCase_ = teacher_model_class.from_pretrained(args.teacher_name ,output_hidden_states=__UpperCamelCase )
if args.n_gpu > 0:
teacher.to(f'''cuda:{args.local_rank}''' )
logger.info(f'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__UpperCamelCase ,__UpperCamelCase )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__UpperCamelCase ,__UpperCamelCase )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowerCamelCase_ = Distiller(
params=__UpperCamelCase ,dataset=__UpperCamelCase ,token_probs=__UpperCamelCase ,student=__UpperCamelCase ,teacher=__UpperCamelCase )
distiller.train()
logger.info('Let\'s go get some drinks.' )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A_ = 250_004
A_ = 250_020
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MBartTokenizer
SCREAMING_SNAKE_CASE_ = MBartTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/mbart-large-en-ro'
SCREAMING_SNAKE_CASE_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE_ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 250020 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 42 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A_ = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientFormerConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["EfficientFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"EfficientFormerForImageClassification",
"EfficientFormerForImageClassificationWithTeacher",
"EfficientFormerModel",
"EfficientFormerPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFEfficientFormerForImageClassification",
"TFEfficientFormerForImageClassificationWithTeacher",
"TFEfficientFormerModel",
"TFEfficientFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'ylacombe/bark-small'
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = 'en_speaker_1'
lowerCamelCase_ = 'This is a test string'
lowerCamelCase_ = 'speaker_embeddings_path.json'
lowerCamelCase_ = 'speaker_embeddings'
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowerCamelCase_ = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCamelCase_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowerCamelCase_ = 35
lowerCamelCase_ = 2
lowerCamelCase_ = 8
lowerCamelCase_ = {
'semantic_prompt': np.ones(SCREAMING_SNAKE_CASE_ ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase_ = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = processor(text=self.input_string )
lowerCamelCase_ = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 42 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase = 4_00_00_00 ) -> int:
lowerCamelCase_ = [0, 1]
lowerCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowerCamelCase_ = 0
for j in range(len(__UpperCamelCase ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
A_ = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
A_ = {
"allenai/longformer-base-4096": 4_096,
"allenai/longformer-large-4096": 4_096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Any:
lowerCamelCase_ = (
list(range(ord('!' ) ,ord('~' ) + 1 ) ) + list(range(ord('¡' ) ,ord('¬' ) + 1 ) ) + list(range(ord('®' ) ,ord('ÿ' ) + 1 ) )
)
lowerCamelCase_ = bs[:]
lowerCamelCase_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
lowerCamelCase_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase ,__UpperCamelCase ) )
def _UpperCamelCase ( __UpperCamelCase ) -> List[str]:
lowerCamelCase_ = set()
lowerCamelCase_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ = char
return pairs
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as vocab_handle:
lowerCamelCase_ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {v: k for k, v in self.encoder.items()}
lowerCamelCase_ = errors # how to handle errors in decoding
lowerCamelCase_ = bytes_to_unicode()
lowerCamelCase_ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='utf-8' ) as merges_handle:
lowerCamelCase_ = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase_ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = {}
lowerCamelCase_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCamelCase_ = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCamelCase_ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_ ,lowerCamelCase_ = bigram
lowerCamelCase_ = []
lowerCamelCase_ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCamelCase_ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase_ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCamelCase_ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = ' '.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = word
return word
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(' ' ) )
return bpe_tokens
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = ''.join(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '\n' )
lowerCamelCase_ = 0
with open(SCREAMING_SNAKE_CASE_ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase_ = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE_ ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCamelCase_ = ' ' + text
return (text, kwargs)
| 42 | 1 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _UpperCamelCase ( __UpperCamelCase ) -> List[Tuple[int, ...]]:
lowerCamelCase_ = []
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase ,(list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase ,torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('Not supported' )
return shapes
@torch.jit.ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Tuple[int, ...]:
lowerCamelCase_ = []
for d in reversed(__UpperCamelCase ):
idx.append(flat_idx % d )
lowerCamelCase_ = flat_idx // d
return tuple(reversed(__UpperCamelCase ) )
@torch.jit.ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = None ,__UpperCamelCase = None ,) -> List[Tuple[slice, ...]]:
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(__UpperCamelCase ) -> None:
lowerCamelCase_ = True
for i in range(len(__UpperCamelCase ) ):
lowerCamelCase_ = -1 * (i + 1)
l[reversed_idx] &= tally
lowerCamelCase_ = l[reversed_idx]
if start_edges is None:
lowerCamelCase_ = [s == 0 for s in start]
reduce_edge_list(__UpperCamelCase )
if end_edges is None:
lowerCamelCase_ = [e == (d - 1) for e, d in zip(__UpperCamelCase ,__UpperCamelCase )]
reduce_edge_list(__UpperCamelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(__UpperCamelCase ) == 0:
return [()]
elif len(__UpperCamelCase ) == 1:
return [(slice(start[0] ,end[0] + 1 ),)]
lowerCamelCase_ = []
lowerCamelCase_ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(__UpperCamelCase ,__UpperCamelCase ):
if s == e:
path_list.append(slice(__UpperCamelCase ,s + 1 ) )
else:
break
lowerCamelCase_ = tuple(__UpperCamelCase )
lowerCamelCase_ = len(__UpperCamelCase )
# start == end, and we're done
if divergence_idx == len(__UpperCamelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
lowerCamelCase_ = start[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase ,sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] ,[d - 1 for d in dims[divergence_idx + 1 :]] ,dims[divergence_idx + 1 :] ,start_edges=start_edges[divergence_idx + 1 :] ,end_edges=[True for _ in end_edges[divergence_idx + 1 :]] ,) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
lowerCamelCase_ = end[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase ,edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] ,end[divergence_idx + 1 :] ,dims[divergence_idx + 1 :] ,start_edges=[True for _ in start_edges[divergence_idx + 1 :]] ,end_edges=end_edges[divergence_idx + 1 :] ,) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] ,end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] ,end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 ,end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
lowerCamelCase_ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 ,end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> torch.Tensor:
lowerCamelCase_ = t.shape[:no_batch_dims]
lowerCamelCase_ = list(_flat_idx_to_idx(__UpperCamelCase ,__UpperCamelCase ) )
# _get_minimal_slice_set is inclusive
lowerCamelCase_ = list(_flat_idx_to_idx(flat_end - 1 ,__UpperCamelCase ) )
# Get an ordered list of slices to perform
lowerCamelCase_ = _get_minimal_slice_set(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,)
lowerCamelCase_ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = False ,__UpperCamelCase = None ,__UpperCamelCase = False ,) -> Any:
if not (len(__UpperCamelCase ) > 0):
raise ValueError('Must provide at least one input' )
lowerCamelCase_ = [shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase )]
lowerCamelCase_ = tuple([max(__UpperCamelCase ) for s in zip(*__UpperCamelCase )] )
def _prep_inputs(__UpperCamelCase ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
lowerCamelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
lowerCamelCase_ = t.reshape(-1 ,*t.shape[no_batch_dims:] )
else:
lowerCamelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
lowerCamelCase_ = tensor_tree_map(_prep_inputs ,__UpperCamelCase )
lowerCamelCase_ = None
if _out is not None:
lowerCamelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) ,_out )
lowerCamelCase_ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
lowerCamelCase_ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(__UpperCamelCase ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
lowerCamelCase_ = 0
lowerCamelCase_ = prepped_outputs
for _ in range(__UpperCamelCase ):
# Chunk the input
if not low_mem:
lowerCamelCase_ = _select_chunk
else:
lowerCamelCase_ = partial(
_chunk_slice ,flat_start=__UpperCamelCase ,flat_end=min(__UpperCamelCase ,i + chunk_size ) ,no_batch_dims=len(__UpperCamelCase ) ,)
lowerCamelCase_ = tensor_tree_map(__UpperCamelCase ,__UpperCamelCase )
# Run the layer on the chunk
lowerCamelCase_ = layer(**__UpperCamelCase )
# Allocate space for the output
if out is None:
lowerCamelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) ,__UpperCamelCase )
# Put the chunk in its pre-allocated space
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
def assign(__UpperCamelCase ,__UpperCamelCase ) -> None:
for k, v in da.items():
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
assign(__UpperCamelCase ,da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
lowerCamelCase_ = da[k]
assign(__UpperCamelCase ,__UpperCamelCase )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
for xa, xa in zip(__UpperCamelCase ,__UpperCamelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
lowerCamelCase_ = xa
elif isinstance(__UpperCamelCase ,torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
lowerCamelCase_ = output_chunk
else:
raise ValueError('Not supported' )
i += chunk_size
lowerCamelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.view(orig_batch_dims + t.shape[1:] ) ,__UpperCamelCase )
return out
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ = 512 , ) -> int:
'''simple docstring'''
lowerCamelCase_ = max_chunk_size
lowerCamelCase_ = None
lowerCamelCase_ = None
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
logging.info('Tuning chunk size...' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
lowerCamelCase_ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
lowerCamelCase_ = [c for c in candidates if c > min_chunk_size]
lowerCamelCase_ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(SCREAMING_SNAKE_CASE_ ) -> bool:
try:
with torch.no_grad():
fn(*SCREAMING_SNAKE_CASE_ , chunk_size=SCREAMING_SNAKE_CASE_ )
return True
except RuntimeError:
return False
lowerCamelCase_ = 0
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ ) - 1
while i > min_viable_chunk_size_index:
lowerCamelCase_ = test_chunk_size(candidates[i] )
if not viable:
lowerCamelCase_ = (min_viable_chunk_size_index + i) // 2
else:
lowerCamelCase_ = i
lowerCamelCase_ = (i + len(SCREAMING_SNAKE_CASE_ ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
lowerCamelCase_ = True
for aa, aa in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert type(SCREAMING_SNAKE_CASE_ ) == type(SCREAMING_SNAKE_CASE_ )
if isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ):
consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [v for _, v in sorted(aa.items() , key=lambda SCREAMING_SNAKE_CASE_ : x[0] )]
lowerCamelCase_ = [v for _, v in sorted(aa.items() , key=lambda SCREAMING_SNAKE_CASE_ : x[0] )]
consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
consistent &= aa == aa
return consistent
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> int:
'''simple docstring'''
lowerCamelCase_ = True
lowerCamelCase_ = tree_map(lambda SCREAMING_SNAKE_CASE_ : a.shape if isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ) else a , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self._compare_arg_caches(self.cached_arg_data , SCREAMING_SNAKE_CASE_ )
else:
# Otherwise, we can reuse the precomputed value
lowerCamelCase_ = False
if not consistent:
lowerCamelCase_ = self._determine_favorable_chunk_size(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 42 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A_ = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
A_ = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
A_ = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = RealmTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="[UNK]" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="[PAD]" , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[MASK]" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , do_lower_case=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , tokenize_chinese_chars=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , SCREAMING_SNAKE_CASE_ ) != do_lower_case
or normalizer_state.get('strip_accents' , SCREAMING_SNAKE_CASE_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(SCREAMING_SNAKE_CASE_ , normalizer_state.pop('type' ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = do_lower_case
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = PaddingStrategy.MAX_LENGTH
lowerCamelCase_ = text
lowerCamelCase_ = kwargs.pop('text_pair' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = kwargs.pop('return_tensors' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(SCREAMING_SNAKE_CASE_ ):
if batch_text_pair is not None:
lowerCamelCase_ = batch_text_pair[idx]
else:
lowerCamelCase_ = None
lowerCamelCase_ = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = encoded_candidates.get('input_ids' )
lowerCamelCase_ = encoded_candidates.get('attention_mask' )
lowerCamelCase_ = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(SCREAMING_SNAKE_CASE_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(SCREAMING_SNAKE_CASE_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = {key: item for key, item in output_data.items() if len(SCREAMING_SNAKE_CASE_ ) != 0}
return BatchEncoding(SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def _UpperCamelCase ( __UpperCamelCase ) -> str:
lowerCamelCase_ = []
for line in lines:
lowerCamelCase_ = re.sub(R'#.*' ,'' ,__UpperCamelCase ) # remove comments
if line:
filtered_lines.append(__UpperCamelCase )
lowerCamelCase_ = '\n'.join(__UpperCamelCase )
# Make a hash from all this code
lowerCamelCase_ = full_str.encode('utf-8' )
return shaaaa(__UpperCamelCase ).hexdigest()
# get importable module names and hash for caching
A_ = {
"csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
A_ = {
".csv": ("csv", {}),
".tsv": ("csv", {"sep": "\t"}),
".json": ("json", {}),
".jsonl": ("json", {}),
".parquet": ("parquet", {}),
".arrow": ("arrow", {}),
".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_ = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
A_ = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")
_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")
| 42 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
lowerCamelCase_ = str(__UpperCamelCase )
return len(__UpperCamelCase ) == 9 and set(__UpperCamelCase ) == set('123456789' )
def _UpperCamelCase ( ) -> int | None:
for base_num in range(99_99 ,49_99 ,-1 ):
lowerCamelCase_ = 10_00_02 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
for base_num in range(3_33 ,99 ,-1 ):
lowerCamelCase_ = 1_00_20_03 * base_num
if is_9_pandigital(__UpperCamelCase ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A_ = 500_000
A_ , A_ = os.path.split(__file__)
A_ = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def _UpperCamelCase ( __UpperCamelCase ,**__UpperCamelCase ) -> int:
lowerCamelCase_ = dataset.map(**__UpperCamelCase )
@get_duration
def _UpperCamelCase ( __UpperCamelCase ,**__UpperCamelCase ) -> Any:
lowerCamelCase_ = dataset.filter(**__UpperCamelCase )
def _UpperCamelCase ( ) -> List[str]:
lowerCamelCase_ = {'num examples': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ = datasets.Features({'text': datasets.Value('string' ), 'numbers': datasets.Value('float32' )} )
lowerCamelCase_ = generate_example_dataset(
os.path.join(__UpperCamelCase ,'dataset.arrow' ) ,__UpperCamelCase ,num_examples=__UpperCamelCase )
lowerCamelCase_ = transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,use_fast=__UpperCamelCase )
def tokenize(__UpperCamelCase ):
return tokenizer(examples['text'] )
lowerCamelCase_ = map(__UpperCamelCase )
lowerCamelCase_ = map(__UpperCamelCase ,batched=__UpperCamelCase )
lowerCamelCase_ = map(__UpperCamelCase ,function=lambda __UpperCamelCase : None ,batched=__UpperCamelCase )
with dataset.formatted_as(type='numpy' ):
lowerCamelCase_ = map(__UpperCamelCase ,function=lambda __UpperCamelCase : None ,batched=__UpperCamelCase )
with dataset.formatted_as(type='pandas' ):
lowerCamelCase_ = map(__UpperCamelCase ,function=lambda __UpperCamelCase : None ,batched=__UpperCamelCase )
with dataset.formatted_as(type='torch' ,columns='numbers' ):
lowerCamelCase_ = map(__UpperCamelCase ,function=lambda __UpperCamelCase : None ,batched=__UpperCamelCase )
with dataset.formatted_as(type='tensorflow' ,columns='numbers' ):
lowerCamelCase_ = map(__UpperCamelCase ,function=lambda __UpperCamelCase : None ,batched=__UpperCamelCase )
lowerCamelCase_ = map(__UpperCamelCase ,function=__UpperCamelCase ,batched=__UpperCamelCase )
lowerCamelCase_ = filter(__UpperCamelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(__UpperCamelCase ,'wb' ) as f:
f.write(json.dumps(__UpperCamelCase ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 42 |
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = spanish_id.replace('-' ,'' ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
lowerCamelCase_ = int(spanish_id_clean[0:8] )
lowerCamelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(__UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 42 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _UpperCamelCase ( __UpperCamelCase ) -> None:
create_state_space_tree(__UpperCamelCase ,[] ,0 )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> None:
if index == len(__UpperCamelCase ):
print(__UpperCamelCase )
return
create_state_space_tree(__UpperCamelCase ,__UpperCamelCase ,index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(__UpperCamelCase ,__UpperCamelCase ,index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
A_ = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["A", "B", "C"])
generate_all_subsequences(seq)
| 42 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Dict:
'''simple docstring'''
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = "auto" ) -> List[str]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = 7.5 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ) -> List[str]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
# get prompt text embeddings
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
lowerCamelCase_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCamelCase_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
lowerCamelCase_ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowerCamelCase_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = text_embeddings.shape
lowerCamelCase_ = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ = 42
if negative_prompt is None:
lowerCamelCase_ = ['']
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='''
f''' {type(SCREAMING_SNAKE_CASE_ )}.''' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
lowerCamelCase_ = negative_prompt
lowerCamelCase_ = text_input_ids.shape[-1]
lowerCamelCase_ = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' , )
lowerCamelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCamelCase_ = uncond_embeddings.shape[1]
lowerCamelCase_ = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
lowerCamelCase_ = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowerCamelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device='cpu' , dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
lowerCamelCase_ = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase_ = latents_reference.to(self.device )
lowerCamelCase_ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowerCamelCase_ = (latents_shape[3] - latents_shape_reference[3]) // 2
lowerCamelCase_ = (latents_shape[2] - latents_shape_reference[2]) // 2
lowerCamelCase_ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowerCamelCase_ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowerCamelCase_ = 0 if dx < 0 else dx
lowerCamelCase_ = 0 if dy < 0 else dy
lowerCamelCase_ = max(-dx , 0 )
lowerCamelCase_ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowerCamelCase_ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCamelCase_ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ = {}
if accepts_eta:
lowerCamelCase_ = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred.chunk(2 )
lowerCamelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 1 / 0.18_215 * latents
lowerCamelCase_ = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowerCamelCase_ = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) , return_tensors='pt' ).to(
self.device )
lowerCamelCase_ ,lowerCamelCase_ = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowerCamelCase_ = None
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ , nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 42 | 1 |
'''simple docstring'''
from PIL import Image
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Image:
def brightness(__UpperCamelCase ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(__UpperCamelCase )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
A_ = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 42 |
'''simple docstring'''
import pprint
import requests
A_ = "https://zenquotes.io/api"
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _UpperCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
A_ = random_quotes()
pprint.pprint(response)
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase ,2 ) - pow(__UpperCamelCase ,2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase ,2 ) - pow(__UpperCamelCase ,2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase ,2 ) + pow(__UpperCamelCase ,2 ) )}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=33 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ) -> int:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = EsmModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = EsmForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = EsmForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ = ()
SCREAMING_SNAKE_CASE_ = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = EsmModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> str:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase_ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = EsmModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowerCamelCase_ = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowerCamelCase_ = create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE_ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()[0]
lowerCamelCase_ = EsmEmbeddings(config=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.empty(2 , 4 , 30 )
lowerCamelCase_ = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowerCamelCase_ = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowerCamelCase_ = embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE_ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase_ = 33
lowerCamelCase_ = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
lowerCamelCase_ = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowerCamelCase_ = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ )[0]
# compare the actual values for a slice.
lowerCamelCase_ = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 42 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'sew-d'
def __init__( self , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=("p2c", "c2p") , SCREAMING_SNAKE_CASE_="layer_norm" , SCREAMING_SNAKE_CASE_="gelu_python" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1E-7 , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_="group" , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=128 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0.05 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_="mean" , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , **SCREAMING_SNAKE_CASE_ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = hidden_size
lowerCamelCase_ = feat_extract_norm
lowerCamelCase_ = feat_extract_activation
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = conv_bias
lowerCamelCase_ = num_conv_pos_embeddings
lowerCamelCase_ = num_conv_pos_embedding_groups
lowerCamelCase_ = len(self.conv_dim )
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = squeeze_factor
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = position_buckets
lowerCamelCase_ = share_att_key
lowerCamelCase_ = relative_attention
lowerCamelCase_ = norm_rel_ebd
lowerCamelCase_ = list(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = hidden_dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = feat_proj_dropout
lowerCamelCase_ = final_dropout
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = feature_layer_norm_eps
lowerCamelCase_ = initializer_range
lowerCamelCase_ = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase_ = apply_spec_augment
lowerCamelCase_ = mask_time_prob
lowerCamelCase_ = mask_time_length
lowerCamelCase_ = mask_time_min_masks
lowerCamelCase_ = mask_feature_prob
lowerCamelCase_ = mask_feature_length
lowerCamelCase_ = mask_feature_min_masks
# ctc loss
lowerCamelCase_ = ctc_loss_reduction
lowerCamelCase_ = ctc_zero_infinity
# sequence classification
lowerCamelCase_ = use_weighted_layer_sum
lowerCamelCase_ = classifier_proj_size
@property
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 42 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = LEDTokenizer
SCREAMING_SNAKE_CASE_ = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> str:
'''simple docstring'''
super().setUp()
lowerCamelCase_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def UpperCamelCase( self ) -> str:
'''simple docstring'''
return LEDTokenizer.from_pretrained('allenai/led-base-16384' )
@cached_property
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
return LEDTokenizerFast.from_pretrained('allenai/led-base-16384' )
@require_torch
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase_ = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , max_length=len(SCREAMING_SNAKE_CASE_ ) , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE_ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE_ )
self.assertNotIn('labels' , SCREAMING_SNAKE_CASE_ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = [
'Summary of the text.',
'Another summary.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCamelCase_ = tokenizer(text_target=SCREAMING_SNAKE_CASE_ , max_length=32 , padding='max_length' , return_tensors='pt' )
self.assertEqual(32 , targets['input_ids'].shape[1] )
@require_torch
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCamelCase_ = tokenizer(
['I am a small frog' * 1024, 'I am a small frog'] , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(batch.input_ids.shape , (2, 5122) )
@require_torch
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = ['A long paragraph for summarization.']
lowerCamelCase_ = [
'Summary of the text.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = tokenizer(text_target=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = inputs['input_ids']
lowerCamelCase_ = targets['input_ids']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCamelCase_ = ['Summary of the text.', 'Another summary.']
lowerCamelCase_ = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [[0] * len(SCREAMING_SNAKE_CASE_ ) for x in encoded_output['input_ids']]
lowerCamelCase_ = tokenizer.pad(SCREAMING_SNAKE_CASE_ )
self.assertSequenceEqual(outputs['global_attention_mask'] , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 'A, <mask> AllenNLP sentence.'
lowerCamelCase_ = tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ )
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
lowerCamelCase_ = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
lowerCamelCase_ = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
SCREAMING_SNAKE_CASE_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
SCREAMING_SNAKE_CASE_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
| 42 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'xlm-prophetnet'
SCREAMING_SNAKE_CASE_ = ['past_key_values']
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 30522 , SCREAMING_SNAKE_CASE_ = 1024 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 4096 , SCREAMING_SNAKE_CASE_ = 12 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 0.1 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2 , **SCREAMING_SNAKE_CASE_ , ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = encoder_ffn_dim
lowerCamelCase_ = num_encoder_layers
lowerCamelCase_ = num_encoder_attention_heads
lowerCamelCase_ = decoder_ffn_dim
lowerCamelCase_ = num_decoder_layers
lowerCamelCase_ = num_decoder_attention_heads
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = init_std # Normal(0, this parameter)
lowerCamelCase_ = activation_function
# parameters for xlmprophetnet
lowerCamelCase_ = ngram
lowerCamelCase_ = num_buckets
lowerCamelCase_ = relative_max_distance
lowerCamelCase_ = disable_ngram_loss
lowerCamelCase_ = eps
# 3 Types of Dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = activation_dropout
lowerCamelCase_ = dropout
lowerCamelCase_ = use_cache
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , add_cross_attention=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
@property
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 42 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> bool:
lowerCamelCase_ = get_failure_array(__UpperCamelCase )
# 2) Step through text searching for pattern
lowerCamelCase_ ,lowerCamelCase_ = 0, 0 # index into text, pattern
while i < len(__UpperCamelCase ):
if pattern[j] == text[i]:
if j == (len(__UpperCamelCase ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
lowerCamelCase_ = failure[j - 1]
continue
i += 1
return False
def _UpperCamelCase ( __UpperCamelCase ) -> list[int]:
lowerCamelCase_ = [0]
lowerCamelCase_ = 0
lowerCamelCase_ = 1
while j < len(__UpperCamelCase ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
lowerCamelCase_ = failure[i - 1]
continue
j += 1
failure.append(__UpperCamelCase )
return failure
if __name__ == "__main__":
# Test 1)
A_ = "abc1abc12"
A_ = "alskfjaldsabc1abc1abc12k23adsfabcabc"
A_ = "alskfjaldsk23adsfabcabc"
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
A_ = "ABABX"
A_ = "ABABZABABYABABX"
assert kmp(pattern, text)
# Test 3)
A_ = "AAAB"
A_ = "ABAAAAAB"
assert kmp(pattern, text)
# Test 4)
A_ = "abcdabcy"
A_ = "abcxabcdabxabcdabcdabcy"
assert kmp(pattern, text)
# Test 5)
A_ = "aabaabaaa"
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 42 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = x
lowerCamelCase_ = y
for step in range(__UpperCamelCase ): # noqa: B007
lowerCamelCase_ = a * a - b * b + x
lowerCamelCase_ = 2 * a * b + y
lowerCamelCase_ = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def _UpperCamelCase ( __UpperCamelCase ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55 ) for i in colorsys.hsv_to_rgb(__UpperCamelCase ,1 ,1 ) )
def _UpperCamelCase ( __UpperCamelCase = 8_00 ,__UpperCamelCase = 6_00 ,__UpperCamelCase = -0.6 ,__UpperCamelCase = 0 ,__UpperCamelCase = 3.2 ,__UpperCamelCase = 50 ,__UpperCamelCase = True ,) -> Image.Image:
lowerCamelCase_ = Image.new('RGB' ,(image_width, image_height) )
lowerCamelCase_ = img.load()
# loop through the image-coordinates
for image_x in range(__UpperCamelCase ):
for image_y in range(__UpperCamelCase ):
# determine the figure-coordinates based on the image-coordinates
lowerCamelCase_ = figure_width / image_width * image_height
lowerCamelCase_ = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowerCamelCase_ = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowerCamelCase_ = get_distance(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowerCamelCase_ = get_color_coded_rgb(__UpperCamelCase )
else:
lowerCamelCase_ = get_black_and_white_rgb(__UpperCamelCase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
A_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 42 | 1 |
'''simple docstring'''
from collections.abc import Callable
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ = None ) -> None:
'''simple docstring'''
lowerCamelCase_ = []
# Stores indexes of each item for supporting updates and deletion.
lowerCamelCase_ = {}
# Stores current size of heap.
lowerCamelCase_ = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowerCamelCase_ = key or (lambda SCREAMING_SNAKE_CASE_ : x)
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int | None:
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int | None:
'''simple docstring'''
lowerCamelCase_ = int(2 * i + 1 )
return left if 0 < left < self.size else None
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int | None:
'''simple docstring'''
lowerCamelCase_ = int(2 * i + 2 )
return right if 0 < right < self.size else None
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowerCamelCase_ ,lowerCamelCase_ = self.arr[j], self.arr[i]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ = self._left(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self._right(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase_ = right
return valid_parent
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
lowerCamelCase_ = self._parent(SCREAMING_SNAKE_CASE_ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self._swap(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = parent, self._parent(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
lowerCamelCase_ = self._get_valid_parent(SCREAMING_SNAKE_CASE_ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
if item not in self.pos_map:
return
lowerCamelCase_ = self.pos_map[item]
lowerCamelCase_ = [item, self.key(SCREAMING_SNAKE_CASE_ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE_ )
self._heapify_down(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
if item not in self.pos_map:
return
lowerCamelCase_ = self.pos_map[item]
del self.pos_map[item]
lowerCamelCase_ = self.arr[self.size - 1]
lowerCamelCase_ = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE_ )
self._heapify_down(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
lowerCamelCase_ = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE_ )] )
else:
lowerCamelCase_ = [item, self.key(SCREAMING_SNAKE_CASE_ )]
lowerCamelCase_ = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def UpperCamelCase( self ) -> tuple | None:
'''simple docstring'''
return self.arr[0] if self.size else None
def UpperCamelCase( self ) -> tuple | None:
'''simple docstring'''
lowerCamelCase_ = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def _UpperCamelCase ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase_ = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase_ = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase_ = outgoing_gradient**2 + 4
lowerCamelCase_ = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase_ = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
lowerCamelCase_ = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase_ = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase_ = x_minus if isclose(__UpperCamelCase ,__UpperCamelCase ) else x_plus
lowerCamelCase_ = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _UpperCamelCase ( __UpperCamelCase = 1.4 ,__UpperCamelCase = -9.6 ) -> int:
lowerCamelCase_ = 0
lowerCamelCase_ = first_x_coord
lowerCamelCase_ = first_y_coord
lowerCamelCase_ = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = next_point(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 42 | 1 |
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