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'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
a_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Union[str, Any] , *__lowercase : str , **__lowercase : Tuple ) -> None:
warnings.warn(
'''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use PoolFormerImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase ) | 152 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =AlbertConfig.from_json_file(UpperCamelCase__ )
print(f"Building PyTorch model from configuration: {config}" )
SCREAMING_SNAKE_CASE__ : Any =AlbertForPreTraining(UpperCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict(), UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
a_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path) | 152 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 279 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''biogpt'''
def __init__(self , __magic_name__=4_2384 , __magic_name__=1024 , __magic_name__=24 , __magic_name__=16 , __magic_name__=4096 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=1024 , __magic_name__=0.02 , __magic_name__=1e-12 , __magic_name__=True , __magic_name__=True , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=1 , __magic_name__=0 , __magic_name__=2 , **__magic_name__ , ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : Optional[int] = hidden_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : List[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Optional[int] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : str = scale_embedding
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = layerdrop
snake_case_ : Optional[Any] = activation_dropout
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
| 279 | 1 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase__ = '#'
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] ) ->None:
'''simple docstring'''
_UpperCAmelCase : dict = {}
def lowerCAmelCase__ ( self : int , lowerCamelCase__ : str ) ->None:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = self._trie
for char in text:
if char not in trie:
_UpperCAmelCase : Any = {}
_UpperCAmelCase : Optional[int] = trie[char]
_UpperCAmelCase : Dict = True
def lowerCAmelCase__ ( self : List[str] , lowerCamelCase__ : str ) ->tuple | list:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = self._trie
for char in prefix:
if char in trie:
_UpperCAmelCase : Any = trie[char]
else:
return []
return self._elements(lowerCamelCase__ )
def lowerCAmelCase__ ( self : str , lowerCamelCase__ : dict ) ->tuple:
'''simple docstring'''
_UpperCAmelCase : Any = []
for c, v in d.items():
_UpperCAmelCase : Optional[int] = [''' '''] if c == END else [(c + s) for s in self._elements(lowerCamelCase__ )]
result.extend(lowerCamelCase__ )
return tuple(lowerCamelCase__ )
lowerCamelCase__ = Trie()
lowerCamelCase__ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : str = trie.find_word(__lowerCAmelCase )
return tuple(string + word for word in suffixes )
def __lowerCAmelCase ():
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 234 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict = logging.get_logger(__name__)
_A : Union[str, Any] = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Any = "vit_msn"
def __init__( self : Optional[Any] , A : Dict=7_6_8 , A : Union[str, Any]=1_2 , A : Optional[Any]=1_2 , A : List[Any]=3_0_7_2 , A : List[str]="gelu" , A : Optional[int]=0.0 , A : int=0.0 , A : int=0.02 , A : Tuple=1e-06 , A : int=2_2_4 , A : Union[str, Any]=1_6 , A : Dict=3 , A : Optional[Any]=True , **A : Optional[Any] , ) ->Dict:
super().__init__(**A )
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Dict = num_hidden_layers
lowerCamelCase__ : str = num_attention_heads
lowerCamelCase__ : Tuple = intermediate_size
lowerCamelCase__ : str = hidden_act
lowerCamelCase__ : Optional[int] = hidden_dropout_prob
lowerCamelCase__ : Any = attention_probs_dropout_prob
lowerCamelCase__ : List[str] = initializer_range
lowerCamelCase__ : Optional[int] = layer_norm_eps
lowerCamelCase__ : Any = image_size
lowerCamelCase__ : Any = patch_size
lowerCamelCase__ : Union[str, Any] = num_channels
lowerCamelCase__ : Tuple = qkv_bias
| 142 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[Any] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : str = "openai-gpt"
lowerCAmelCase_ : Optional[int] = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , a__=40_478 , a__=512 , a__=768 , a__=12 , a__=12 , a__="gelu" , a__=0.1 , a__=0.1 , a__=0.1 , a__=1e-5 , a__=0.0_2 , a__="cls_index" , a__=True , a__=None , a__=True , a__=0.1 , **a__ , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = vocab_size
snake_case_ = n_positions
snake_case_ = n_embd
snake_case_ = n_layer
snake_case_ = n_head
snake_case_ = afn
snake_case_ = resid_pdrop
snake_case_ = embd_pdrop
snake_case_ = attn_pdrop
snake_case_ = layer_norm_epsilon
snake_case_ = initializer_range
snake_case_ = summary_type
snake_case_ = summary_use_proj
snake_case_ = summary_activation
snake_case_ = summary_first_dropout
snake_case_ = summary_proj_to_labels
super().__init__(**a__ )
| 92 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_SCREAMING_SNAKE_CASE : Any = False
class _snake_case ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
snake_case_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
image=a__ , generator=a__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" , ).images
snake_case_ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ = np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 92 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
A__ : Tuple =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__ : Union[str, Any] =cvtColor(img, COLOR_BGR2GRAY)
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = cn.convert_to_negative(lowerCAmelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
with Image.open("""digital_image_processing/image_data/lena_small.jpg""" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(lowerCAmelCase , 1_10 ) ).startswith(
"""<PIL.Image.Image image mode=RGB size=100x100 at""" )
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = imread("""digital_image_processing/image_data/lena_small.jpg""" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_lowerCAmelCase = canny.canny(lowerCAmelCase )
# assert canny array for at least one True
assert canny_array.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
assert gg.gaussian_filter(lowerCAmelCase , 5 , sigma=0.9 ).all()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
_lowerCAmelCase = conv.img_convolve(lowerCAmelCase , lowerCAmelCase ).astype(lowerCAmelCase )
assert res.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
assert med.median_filter(lowerCAmelCase , 3 ).any()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = sob.sobel_filter(lowerCAmelCase )
assert grad.any() and theta.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = sp.make_sepia(lowerCAmelCase , 20 )
assert sepia.all()
def UpperCamelCase__ ( lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" ):
"""simple docstring"""
_lowerCAmelCase = bs.Burkes(imread(lowerCAmelCase , 1 ) , 1_20 )
burkes.process()
assert burkes.output_img.any()
def UpperCamelCase__ ( lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" , ):
"""simple docstring"""
_lowerCAmelCase = rs.NearestNeighbour(imread(lowerCAmelCase , 1 ) , 4_00 , 2_00 )
nn.process()
assert nn.output.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = """digital_image_processing/image_data/lena.jpg"""
# Reading the image and converting it to grayscale.
_lowerCAmelCase = imread(lowerCAmelCase , 0 )
# Test for get_neighbors_pixel function() return not None
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = image[x_coordinate][y_coordinate]
_lowerCAmelCase = lbp.get_neighbors_pixel(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_lowerCAmelCase = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_lowerCAmelCase = lbp.local_binary_value(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
assert lbp_image.any()
| 70 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case : Dict = 4
snake_case : str = (1 << p) - 1
for _ in range(p - 2 ):
snake_case : Any = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| 203 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCamelCase( unittest.TestCase ):
def __init__( self, lowerCamelCase, lowerCamelCase=7, lowerCamelCase=3, lowerCamelCase=18, lowerCamelCase=30, lowerCamelCase=4_00, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=True, ) -> str:
"""simple docstring"""
_lowercase : Tuple = size if size is not None else {'height': 18, 'width': 18}
_lowercase : Tuple = parent
_lowercase : Dict = batch_size
_lowercase : Any = num_channels
_lowercase : int = image_size
_lowercase : List[str] = min_resolution
_lowercase : str = max_resolution
_lowercase : str = do_resize
_lowercase : Optional[int] = size
_lowercase : Any = apply_ocr
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCamelCase( _a, unittest.TestCase ):
lowercase_ : List[str] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : Any = LayoutLMvaImageProcessingTester(self)
@property
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : List[str] = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCamelCase, 'do_resize'))
self.assertTrue(hasattr(lowerCamelCase, 'size'))
self.assertTrue(hasattr(lowerCamelCase, 'apply_ocr'))
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : int = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {'height': 18, 'width': 18})
_lowercase : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {'height': 42, 'width': 42})
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
pass
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : str = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_lowercase : Any = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, Image.Image)
# Test not batched input
_lowercase : Dict = image_processing(image_inputs[0], return_tensors='pt')
self.assertEqual(
encoding.pixel_values.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
self.assertIsInstance(encoding.words, lowerCamelCase)
self.assertIsInstance(encoding.boxes, lowerCamelCase)
# Test batched
_lowercase : str = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Optional[int] = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_lowercase : List[str] = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, numpify=lowerCamelCase)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, np.ndarray)
# Test not batched input
_lowercase : List[Any] = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
# Test batched
_lowercase : Union[str, Any] = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
_lowercase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_lowercase : Optional[Any] = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, torchify=lowerCamelCase)
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, torch.Tensor)
# Test not batched input
_lowercase : Tuple = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
# Test batched
_lowercase : List[Any] = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
), )
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Optional[Any] = LayoutLMvaImageProcessor()
from datasets import load_dataset
_lowercase : Optional[Any] = load_dataset('hf-internal-testing/fixtures_docvqa', split='test')
_lowercase : Optional[Any] = Image.open(ds[0]['file']).convert('RGB')
_lowercase : Optional[Any] = image_processing(lowerCamelCase, return_tensors='pt')
self.assertEqual(encoding.pixel_values.shape, (1, 3, 2_24, 2_24))
self.assertEqual(len(encoding.words), len(encoding.boxes))
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
_lowercase : List[str] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
_lowercase : Any = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words, lowerCamelCase)
self.assertListEqual(encoding.boxes, lowerCamelCase)
# with apply_OCR = False
_lowercase : List[str] = LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase)
_lowercase : Union[str, Any] = image_processing(lowerCamelCase, return_tensors='pt')
self.assertEqual(encoding.pixel_values.shape, (1, 3, 2_24, 2_24))
| 84 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _lowerCamelCase:
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0)
_lowercase : Dict = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
_lowercase : List[str] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
_lowercase : Optional[int] = UNetaDConditionModel(
sample_size=32, layers_per_block=1, block_out_channels=[32, 64], down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
], mid_block_type='UNetMidBlock2DSimpleCrossAttn', up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'], in_channels=3, out_channels=6, cross_attention_dim=32, encoder_hid_dim=32, attention_head_dim=8, addition_embed_type='text', addition_embed_type_num_heads=2, cross_attention_norm='group_norm', resnet_time_scale_shift='scale_shift', act_fn='gelu', )
unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
torch.manual_seed(0)
_lowercase : Dict = DDPMScheduler(
num_train_timesteps=10_00, beta_schedule='squaredcos_cap_v2', beta_start=0.0_0_0_1, beta_end=0.0_2, thresholding=lowerCamelCase, dynamic_thresholding_ratio=0.9_5, sample_max_value=1.0, prediction_type='epsilon', variance_type='learned_range', )
torch.manual_seed(0)
_lowercase : List[Any] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
torch.manual_seed(0)
_lowercase : List[str] = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
_lowercase : int = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
_lowercase : List[str] = UNetaDConditionModel(
sample_size=32, layers_per_block=[1, 2], block_out_channels=[32, 64], down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
], mid_block_type='UNetMidBlock2DSimpleCrossAttn', up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'], in_channels=6, out_channels=6, cross_attention_dim=32, encoder_hid_dim=32, attention_head_dim=8, addition_embed_type='text', addition_embed_type_num_heads=2, cross_attention_norm='group_norm', resnet_time_scale_shift='scale_shift', act_fn='gelu', class_embed_type='timestep', mid_block_scale_factor=1.4_1_4, time_embedding_act_fn='gelu', time_embedding_dim=32, )
unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
torch.manual_seed(0)
_lowercase : Optional[int] = DDPMScheduler(
num_train_timesteps=10_00, beta_schedule='squaredcos_cap_v2', beta_start=0.0_0_0_1, beta_end=0.0_2, thresholding=lowerCamelCase, dynamic_thresholding_ratio=0.9_5, sample_max_value=1.0, prediction_type='epsilon', variance_type='learned_range', )
torch.manual_seed(0)
_lowercase : str = DDPMScheduler(
num_train_timesteps=10_00, beta_schedule='squaredcos_cap_v2', beta_start=0.0_0_0_1, beta_end=0.0_2, )
torch.manual_seed(0)
_lowercase : Union[str, Any] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : List[Any] = self.get_dummy_components()
_lowercase : List[str] = self.pipeline_class(**lowerCamelCase)
pipe.to(lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : int = self.get_dummy_inputs(lowerCamelCase)
_lowercase : int = inputs['prompt']
_lowercase : Dict = inputs['generator']
_lowercase : Optional[int] = inputs['num_inference_steps']
_lowercase : str = inputs['output_type']
if "image" in inputs:
_lowercase : List[Any] = inputs['image']
else:
_lowercase : List[Any] = None
if "mask_image" in inputs:
_lowercase : Union[str, Any] = inputs['mask_image']
else:
_lowercase : Dict = None
if "original_image" in inputs:
_lowercase : Any = inputs['original_image']
else:
_lowercase : Tuple = None
_lowercase , _lowercase : str = pipe.encode_prompt(lowerCamelCase)
# inputs with prompt converted to embeddings
_lowercase : Any = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
_lowercase : int = image
if mask_image is not None:
_lowercase : str = mask_image
if original_image is not None:
_lowercase : Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase, lowerCamelCase, lowerCamelCase)
_lowercase : Dict = pipe(**lowerCamelCase)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase)
_lowercase : Any = self.pipeline_class.from_pretrained(lowerCamelCase)
pipe_loaded.to(lowerCamelCase)
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase, lowerCamelCase) is None, F'''`{optional_component}` did not stay set to None after loading.''', )
_lowercase : Dict = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Optional[Any] = inputs['generator']
_lowercase : Any = inputs['num_inference_steps']
_lowercase : List[Any] = inputs['output_type']
# inputs with prompt converted to embeddings
_lowercase : Optional[int] = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
_lowercase : str = image
if mask_image is not None:
_lowercase : Optional[int] = mask_image
if original_image is not None:
_lowercase : int = original_image
_lowercase : str = pipe_loaded(**lowerCamelCase)[0]
_lowercase : List[Any] = np.abs(to_np(lowerCamelCase) - to_np(lowerCamelCase)).max()
self.assertLess(lowerCamelCase, 1E-4)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Optional[Any] = self.get_dummy_components()
_lowercase : Any = self.pipeline_class(**lowerCamelCase)
pipe.to(lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : List[str] = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Tuple = pipe(**lowerCamelCase)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase)
_lowercase : List[str] = self.pipeline_class.from_pretrained(lowerCamelCase)
pipe_loaded.to(lowerCamelCase)
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
_lowercase : int = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Tuple = pipe_loaded(**lowerCamelCase)[0]
_lowercase : str = np.abs(to_np(lowerCamelCase) - to_np(lowerCamelCase)).max()
self.assertLess(lowerCamelCase, 1E-4)
| 84 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =UnCLIPImageVariationPipeline
lowerCamelCase__ =IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
lowerCamelCase__ =IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase__ =[
'generator',
'return_dict',
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
lowerCamelCase__ =False
@property
def __UpperCamelCase ( self : int ) -> List[Any]:
"""simple docstring"""
return 32
@property
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
return 32
@property
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
return self.time_input_dim
@property
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return 100
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(a )
@property
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(a )
@property
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = {
"clip_embeddings_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"cross_attention_dim": self.cross_attention_dim,
}
SCREAMING_SNAKE_CASE : Dict = UnCLIPTextProjModel(**a )
return model
@property
def __UpperCamelCase ( self : int ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = {
"sample_size": 32,
# RGB in channels
"in_channels": 3,
# Out channels is double in channels because predicts mean and variance
"out_channels": 6,
"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,
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": "identity",
}
SCREAMING_SNAKE_CASE : int = UNetaDConditionModel(**a )
return model
@property
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def __UpperCamelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def __UpperCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(1 )
SCREAMING_SNAKE_CASE : Tuple = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def __UpperCamelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.dummy_decoder
SCREAMING_SNAKE_CASE : List[str] = self.dummy_text_proj
SCREAMING_SNAKE_CASE : int = self.dummy_text_encoder
SCREAMING_SNAKE_CASE : int = self.dummy_tokenizer
SCREAMING_SNAKE_CASE : str = self.dummy_super_res_first
SCREAMING_SNAKE_CASE : List[Any] = self.dummy_super_res_last
SCREAMING_SNAKE_CASE : str = UnCLIPScheduler(
variance_type="learned_range" , prediction_type="epsilon" , num_train_timesteps=1000 , )
SCREAMING_SNAKE_CASE : str = UnCLIPScheduler(
variance_type="fixed_small_log" , prediction_type="epsilon" , num_train_timesteps=1000 , )
SCREAMING_SNAKE_CASE : List[str] = CLIPImageProcessor(crop_size=32 , size=32 )
SCREAMING_SNAKE_CASE : Dict = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def __UpperCamelCase ( self : Any , a : str , a : Union[str, Any]=0 , a : Tuple=True ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(a ) ).to(a )
if str(a ).startswith("mps" ):
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(a )
else:
SCREAMING_SNAKE_CASE : int = torch.Generator(device=a ).manual_seed(a )
if pil_image:
SCREAMING_SNAKE_CASE : Dict = input_image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = input_image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
SCREAMING_SNAKE_CASE : Tuple = DiffusionPipeline.numpy_to_pil(a )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "cpu"
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : int = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Dict = pipe(**a )
SCREAMING_SNAKE_CASE : Any = output.images
SCREAMING_SNAKE_CASE : int = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : int = pipe(
**a , return_dict=a , )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = "cpu"
SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : str = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(**a )
SCREAMING_SNAKE_CASE : Optional[Any] = output.images
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Dict = pipe(
**a , return_dict=a , )[0]
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = "cpu"
SCREAMING_SNAKE_CASE : List[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : str = [
pipeline_inputs["image"],
pipeline_inputs["image"],
]
SCREAMING_SNAKE_CASE : Dict = pipe(**a )
SCREAMING_SNAKE_CASE : Optional[int] = output.images
SCREAMING_SNAKE_CASE : int = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : str = [
tuple_pipeline_inputs["image"],
tuple_pipeline_inputs["image"],
]
SCREAMING_SNAKE_CASE : List[str] = pipe(
**a , return_dict=a , )[0]
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
SCREAMING_SNAKE_CASE : List[Any] = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = torch.device("cpu" )
class _UpperCamelCase :
'''simple docstring'''
lowerCamelCase__ =1
SCREAMING_SNAKE_CASE : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Optional[int] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : str = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : str = torch.Generator(device=a ).manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = pipe.decoder.dtype
SCREAMING_SNAKE_CASE : List[str] = 1
SCREAMING_SNAKE_CASE : List[str] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
SCREAMING_SNAKE_CASE : List[Any] = pipe.prepare_latents(
a , dtype=a , device=a , generator=a , latents=a , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE : List[str] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
SCREAMING_SNAKE_CASE : int = pipe.prepare_latents(
a , dtype=a , device=a , generator=a , latents=a , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE : str = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe(
**a , decoder_latents=a , super_res_latents=a ).images
SCREAMING_SNAKE_CASE : str = self.get_dummy_inputs(a , pil_image=a )
# Don't pass image, instead pass embedding
SCREAMING_SNAKE_CASE : List[str] = pipeline_inputs.pop("image" )
SCREAMING_SNAKE_CASE : str = pipe.image_encoder(a ).image_embeds
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(
**a , decoder_latents=a , super_res_latents=a , image_embeddings=a , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1e-4
@skip_mps
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = torch_device == "cpu"
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
SCREAMING_SNAKE_CASE : List[Any] = 1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=a , expected_max_diff=a )
@skip_mps
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = torch_device == "cpu"
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
self._test_inference_batch_single_identical(
test_max_difference=a , relax_max_difference=a , additional_params_copy_to_batched_inputs=a , )
def __UpperCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
SCREAMING_SNAKE_CASE : List[str] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=a , additional_params_copy_to_batched_inputs=a , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=a )
@skip_mps
def __UpperCamelCase ( self : Tuple ) -> Any:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )
SCREAMING_SNAKE_CASE : str = UnCLIPImageVariationPipeline.from_pretrained(
"kakaobrain/karlo-v1-alpha-image-variations" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : Tuple = pipeline.to(a )
pipeline.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
SCREAMING_SNAKE_CASE : str = pipeline(
a , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(a , a , 15 ) | 76 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , _a) or not isinstance(_a , _a):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
SCREAMING_SNAKE_CASE : int = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
SCREAMING_SNAKE_CASE : List[Any] = float(factorial(_a))
coefficient /= factorial(_a) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75)) | 76 | 1 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCAmelCase : int = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : int = ["""pixel_values"""]
def __init__( self , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = PILImageResampling.BICUBIC , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = True , lowerCAmelCase__ = 1 / 2_5_5 , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = True , **lowerCAmelCase__ , ) -> None:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
a__ : Dict =size if size is not None else {"shortest_edge": 2_2_4}
a__ : int =get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
a__ : List[str] =crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
a__ : str =get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ , param_name="crop_size" )
a__ : Union[str, Any] =do_resize
a__ : Tuple =size
a__ : List[str] =resample
a__ : int =do_center_crop
a__ : Any =crop_size
a__ : Tuple =do_rescale
a__ : Optional[int] =rescale_factor
a__ : str =do_normalize
a__ : Optional[Any] =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
a__ : List[str] =image_std if image_std is not None else OPENAI_CLIP_STD
a__ : str =do_convert_rgb
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = PILImageResampling.BICUBIC , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> np.ndarray:
'''simple docstring'''
a__ : List[Any] =get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
a__ : Dict =get_resize_output_image_size(lowerCAmelCase__ , size=size["shortest_edge"] , default_to_square=lowerCAmelCase__ )
return resize(lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> np.ndarray:
'''simple docstring'''
a__ : str =get_size_dict(lowerCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCAmelCase__ , size=(size["height"], size["width"]) , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> Dict:
'''simple docstring'''
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = ChannelDimension.FIRST , **lowerCAmelCase__ , ) -> PIL.Image.Image:
'''simple docstring'''
a__ : List[str] =do_resize if do_resize is not None else self.do_resize
a__ : Optional[Any] =size if size is not None else self.size
a__ : int =get_size_dict(lowerCAmelCase__ , param_name="size" , default_to_square=lowerCAmelCase__ )
a__ : str =resample if resample is not None else self.resample
a__ : Tuple =do_center_crop if do_center_crop is not None else self.do_center_crop
a__ : Optional[Any] =crop_size if crop_size is not None else self.crop_size
a__ : Any =get_size_dict(lowerCAmelCase__ , param_name="crop_size" , default_to_square=lowerCAmelCase__ )
a__ : Tuple =do_rescale if do_rescale is not None else self.do_rescale
a__ : Optional[int] =rescale_factor if rescale_factor is not None else self.rescale_factor
a__ : Union[str, Any] =do_normalize if do_normalize is not None else self.do_normalize
a__ : int =image_mean if image_mean is not None else self.image_mean
a__ : List[Any] =image_std if image_std is not None else self.image_std
a__ : Tuple =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
a__ : Optional[int] =make_list_of_images(lowerCAmelCase__ )
if not valid_images(lowerCAmelCase__ ):
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:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
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." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
a__ : List[str] =[convert_to_rgb(lowerCAmelCase__ ) for image in images]
# All transformations expect numpy arrays.
a__ : List[str] =[to_numpy_array(lowerCAmelCase__ ) for image in images]
if do_resize:
a__ : List[Any] =[self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_center_crop:
a__ : Tuple =[self.center_crop(image=lowerCAmelCase__ , size=lowerCAmelCase__ ) for image in images]
if do_rescale:
a__ : List[Any] =[self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
a__ : Optional[Any] =[self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
a__ : Union[str, Any] =[to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
a__ : List[str] ={"pixel_values": images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
| 148 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = (PNDMScheduler,)
_lowercase : str = (("""num_inference_steps""", 50),)
def _lowercase ( self , **lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Dict ={
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowercase ( self , lowerCAmelCase__=0 , **lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
a__ : Optional[int] =dict(self.forward_default_kwargs )
a__ : Tuple =kwargs.pop("num_inference_steps" , lowerCAmelCase__ )
a__ : List[str] =self.dummy_sample
a__ : List[str] =0.1 * sample
a__ : str =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a__ : int =self.get_scheduler_config(**lowerCAmelCase__ )
a__ : Union[str, Any] =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
a__ : Any =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
a__ : List[Any] =scheduler_class.from_pretrained(lowerCAmelCase__ )
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
a__ : str =dummy_past_residuals[:]
a__ : Any =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Dict =new_scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a__ : Optional[int] =scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Union[str, Any] =new_scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _lowercase ( self ) -> int:
'''simple docstring'''
pass
def _lowercase ( self , lowerCAmelCase__=0 , **lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[int] =dict(self.forward_default_kwargs )
a__ : List[str] =kwargs.pop("num_inference_steps" , lowerCAmelCase__ )
a__ : List[str] =self.dummy_sample
a__ : int =0.1 * sample
a__ : Tuple =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a__ : Dict =self.get_scheduler_config()
a__ : List[str] =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
a__ : Dict =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
a__ : Dict =scheduler_class.from_pretrained(lowerCAmelCase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residual (must be after setting timesteps)
a__ : Optional[int] =dummy_past_residuals[:]
a__ : Optional[Any] =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : List[Any] =new_scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a__ : List[str] =scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Any =new_scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _lowercase ( self , **lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Union[str, Any] =self.scheduler_classes[0]
a__ : Optional[Any] =self.get_scheduler_config(**lowerCAmelCase__ )
a__ : Any =scheduler_class(**lowerCAmelCase__ )
a__ : int =1_0
a__ : Union[str, Any] =self.dummy_model()
a__ : Optional[int] =self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase__ )
for i, t in enumerate(scheduler.prk_timesteps ):
a__ : List[Any] =model(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : Optional[Any] =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
a__ : int =model(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : int =scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
return sample
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : str =dict(self.forward_default_kwargs )
a__ : Tuple =kwargs.pop("num_inference_steps" , lowerCAmelCase__ )
for scheduler_class in self.scheduler_classes:
a__ : Union[str, Any] =self.get_scheduler_config()
a__ : List[str] =scheduler_class(**lowerCAmelCase__ )
a__ : List[Any] =self.dummy_sample
a__ : Dict =0.1 * sample
if num_inference_steps is not None and hasattr(lowerCAmelCase__ , "set_timesteps" ):
scheduler.set_timesteps(lowerCAmelCase__ )
elif num_inference_steps is not None and not hasattr(lowerCAmelCase__ , "set_timesteps" ):
a__ : int =num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a__ : Tuple =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
a__ : str =dummy_past_residuals[:]
a__ : List[Any] =scheduler.step_prk(lowerCAmelCase__ , 0 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : int =scheduler.step_prk(lowerCAmelCase__ , 1 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
a__ : List[str] =scheduler.step_plms(lowerCAmelCase__ , 0 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
a__ : Dict =scheduler.step_plms(lowerCAmelCase__ , 1 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCAmelCase__ )
a__ : Optional[Any] =self.scheduler_classes[0]
a__ : Tuple =self.get_scheduler_config(steps_offset=1 )
a__ : Optional[Any] =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(1_0 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] ) , )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
for beta_start, beta_end in zip([0.00_01, 0.0_01] , [0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowercase ( self ) -> Dict:
'''simple docstring'''
for t in [1, 5, 1_0]:
self.check_over_forward(time_step=lowerCAmelCase__ )
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 1_0] , [1_0, 5_0, 1_0_0] ):
self.check_over_forward(num_inference_steps=lowerCAmelCase__ )
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Dict =2_7
for scheduler_class in self.scheduler_classes:
a__ : Tuple =self.dummy_sample
a__ : Dict =0.1 * sample
a__ : Dict =self.get_scheduler_config()
a__ : int =scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
a__ : Any =scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaises(lowerCAmelCase__ ):
a__ : List[Any] =self.scheduler_classes[0]
a__ : Dict =self.get_scheduler_config()
a__ : Tuple =scheduler_class(**lowerCAmelCase__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] =self.full_loop()
a__ : str =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Optional[Any] =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1E-2
assert abs(result_mean.item() - 0.25_80 ) < 1E-3
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : str =self.full_loop(prediction_type="v_prediction" )
a__ : int =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Optional[int] =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1E-2
assert abs(result_mean.item() - 0.08_78 ) < 1E-3
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ : Tuple =self.full_loop(set_alpha_to_one=lowerCAmelCase__ , beta_start=0.01 )
a__ : str =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Dict =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1E-2
assert abs(result_mean.item() - 0.29_95 ) < 1E-3
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : Dict =self.full_loop(set_alpha_to_one=lowerCAmelCase__ , beta_start=0.01 )
a__ : Union[str, Any] =torch.sum(torch.abs(lowerCAmelCase__ ) )
a__ : Union[str, Any] =torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1E-2
assert abs(result_mean.item() - 0.24_34 ) < 1E-3
| 148 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = """deberta-v2"""
def __init__( self : str , _UpperCAmelCase : Tuple=12_81_00 , _UpperCAmelCase : str=15_36 , _UpperCAmelCase : List[Any]=24 , _UpperCAmelCase : List[Any]=24 , _UpperCAmelCase : List[str]=61_44 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Any=5_12 , _UpperCAmelCase : List[Any]=0 , _UpperCAmelCase : Optional[Any]=0.02 , _UpperCAmelCase : str=1E-7 , _UpperCAmelCase : Any=False , _UpperCAmelCase : Tuple=-1 , _UpperCAmelCase : str=0 , _UpperCAmelCase : int=True , _UpperCAmelCase : str=None , _UpperCAmelCase : Union[str, Any]=0 , _UpperCAmelCase : Union[str, Any]="gelu" , **_UpperCAmelCase : str , ):
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = relative_attention
UpperCAmelCase__ = max_relative_positions
UpperCAmelCase__ = pad_token_id
UpperCAmelCase__ = position_biased_input
# Backwards compatibility
if type(_UpperCAmelCase ) == str:
UpperCAmelCase__ = [x.strip() for x in pos_att_type.lower().split("""|""" )]
UpperCAmelCase__ = pos_att_type
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = kwargs.get("""pooler_hidden_size""" , _UpperCAmelCase )
UpperCAmelCase__ = pooler_dropout
UpperCAmelCase__ = pooler_hidden_act
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
if self.task == "multiple-choice":
UpperCAmelCase__ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCAmelCase__ = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
return 12
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional["TensorType"] = None , _UpperCAmelCase : int = 3 , _UpperCAmelCase : int = 40 , _UpperCAmelCase : int = 40 , _UpperCAmelCase : "PreTrainedTokenizerBase" = None , ):
"""simple docstring"""
UpperCAmelCase__ = super().generate_dummy_inputs(preprocessor=_UpperCAmelCase , framework=_UpperCAmelCase )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 346 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ):
'''simple docstring'''
UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase__ = n // 2
return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(2_0))
else:
try:
UpperCAmelCase_ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number.')
| 346 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 352 |
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 319 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _a ( UpperCamelCase__ ):
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
class _a ( nn.Module ):
_lowercase : int
_lowercase : Tuple[int] = (16, 32, 96, 256)
_lowercase : jnp.dtype = jnp.floataa
def lowerCamelCase_ ( self: List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase__ = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase__ = self.block_out_channels[i]
lowercase__ = self.block_out_channels[i + 1]
lowercase__ = nn.Conv(
UpperCamelCase_ , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(UpperCamelCase_ )
lowercase__ = nn.Conv(
UpperCamelCase_ , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(UpperCamelCase_ )
lowercase__ = blocks
lowercase__ = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self: Optional[int] , UpperCamelCase_: Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.conv_in(UpperCamelCase_ )
lowercase__ = nn.silu(UpperCamelCase_ )
for block in self.blocks:
lowercase__ = block(UpperCamelCase_ )
lowercase__ = nn.silu(UpperCamelCase_ )
lowercase__ = self.conv_out(UpperCamelCase_ )
return embedding
@flax_register_to_config
class _a ( nn.Module , UpperCamelCase__ , UpperCamelCase__ ):
_lowercase : int = 32
_lowercase : int = 4
_lowercase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_lowercase : Union[bool, Tuple[bool]] = False
_lowercase : Tuple[int] = (320, 640, 1280, 1280)
_lowercase : int = 2
_lowercase : Union[int, Tuple[int]] = 8
_lowercase : Optional[Union[int, Tuple[int]]] = None
_lowercase : int = 1280
_lowercase : float = 0.0
_lowercase : bool = False
_lowercase : jnp.dtype = jnp.floataa
_lowercase : bool = True
_lowercase : int = 0
_lowercase : str = "rgb"
_lowercase : Tuple[int] = (16, 32, 96, 256)
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: jax.random.KeyArray ) -> FrozenDict:
"""simple docstring"""
lowercase__ = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase__ = jnp.zeros(UpperCamelCase_ , dtype=jnp.floataa )
lowercase__ = jnp.ones((1,) , dtype=jnp.intaa )
lowercase__ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase__ = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase__ = jnp.zeros(UpperCamelCase_ , dtype=jnp.floataa )
lowercase__ , lowercase__ = jax.random.split(UpperCamelCase_ )
lowercase__ = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )["params"]
def lowerCamelCase_ ( self: Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = self.block_out_channels
lowercase__ = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase__ = self.num_attention_heads or self.attention_head_dim
# input
lowercase__ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase__ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase__ = FlaxTimestepEmbedding(UpperCamelCase_ , dtype=self.dtype )
lowercase__ = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase__ = self.only_cross_attention
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
lowercase__ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
lowercase__ = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase__ = []
lowercase__ = []
lowercase__ = block_out_channels[0]
lowercase__ = nn.Conv(
UpperCamelCase_ , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(UpperCamelCase_ )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase__ = output_channel
lowercase__ = block_out_channels[i]
lowercase__ = i == len(UpperCamelCase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase__ = FlaxCrossAttnDownBlockaD(
in_channels=UpperCamelCase_ , out_channels=UpperCamelCase_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase__ = FlaxDownBlockaD(
in_channels=UpperCamelCase_ , out_channels=UpperCamelCase_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(UpperCamelCase_ )
for _ in range(self.layers_per_block ):
lowercase__ = nn.Conv(
UpperCamelCase_ , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(UpperCamelCase_ )
if not is_final_block:
lowercase__ = nn.Conv(
UpperCamelCase_ , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(UpperCamelCase_ )
lowercase__ = down_blocks
lowercase__ = controlnet_down_blocks
# mid
lowercase__ = block_out_channels[-1]
lowercase__ = FlaxUNetMidBlockaDCrossAttn(
in_channels=UpperCamelCase_ , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase__ = nn.Conv(
UpperCamelCase_ , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self: Dict , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Any , UpperCamelCase_: Optional[Any] , UpperCamelCase_: List[Any] , UpperCamelCase_: float = 1.0 , UpperCamelCase_: bool = True , UpperCamelCase_: bool = False , ) -> Union[FlaxControlNetOutput, Tuple]:
"""simple docstring"""
lowercase__ = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase__ = jnp.flip(UpperCamelCase_ , axis=1 )
# 1. time
if not isinstance(UpperCamelCase_ , jnp.ndarray ):
lowercase__ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(UpperCamelCase_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase__ = timesteps.astype(dtype=jnp.floataa )
lowercase__ = jnp.expand_dims(UpperCamelCase_ , 0 )
lowercase__ = self.time_proj(UpperCamelCase_ )
lowercase__ = self.time_embedding(UpperCamelCase_ )
# 2. pre-process
lowercase__ = jnp.transpose(UpperCamelCase_ , (0, 2, 3, 1) )
lowercase__ = self.conv_in(UpperCamelCase_ )
lowercase__ = jnp.transpose(UpperCamelCase_ , (0, 2, 3, 1) )
lowercase__ = self.controlnet_cond_embedding(UpperCamelCase_ )
sample += controlnet_cond
# 3. down
lowercase__ = (sample,)
for down_block in self.down_blocks:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
lowercase__ , lowercase__ = down_block(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , deterministic=not train )
else:
lowercase__ , lowercase__ = down_block(UpperCamelCase_ , UpperCamelCase_ , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase__ = self.mid_block(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , deterministic=not train )
# 5. contronet blocks
lowercase__ = ()
for down_block_res_sample, controlnet_block in zip(UpperCamelCase_ , self.controlnet_down_blocks ):
lowercase__ = controlnet_block(UpperCamelCase_ )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase__ = controlnet_down_block_res_samples
lowercase__ = self.controlnet_mid_block(UpperCamelCase_ )
# 6. scaling
lowercase__ = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=UpperCamelCase_ , mid_block_res_sample=UpperCamelCase_ )
| 110 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
try:
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as flax_state_f:
lowercase__ = from_bytes(SCREAMING_SNAKE_CASE , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(SCREAMING_SNAKE_CASE ) as f:
if f.read().startswith('''version''' ):
raise OSError(
'''You seem to have cloned a repository without having git-lfs installed. Please'''
''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
''' folder you cloned.''' )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
lowercase__ = flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE : x.dtype == jnp.bfloataa , SCREAMING_SNAKE_CASE ) ).values()
if any(SCREAMING_SNAKE_CASE ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
lowercase__ = jax.tree_util.tree_map(
lambda SCREAMING_SNAKE_CASE : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , SCREAMING_SNAKE_CASE )
lowercase__ = ''''''
lowercase__ = flatten_dict(SCREAMING_SNAKE_CASE , sep='''.''' )
lowercase__ = pt_model.state_dict()
# keep track of unexpected & missing keys
lowercase__ = []
lowercase__ = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowercase__ = flax_key_tuple.split('''.''' )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
lowercase__ = flax_key_tuple_array[:-1] + ['''weight''']
lowercase__ = jnp.transpose(SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
lowercase__ = flax_key_tuple_array[:-1] + ['''weight''']
lowercase__ = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
lowercase__ = flax_key_tuple_array[:-1] + ['''weight''']
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE ):
lowercase__ = (
flax_key_tuple_string.replace('''_0''' , '''.0''' )
.replace('''_1''' , '''.1''' )
.replace('''_2''' , '''.2''' )
.replace('''_3''' , '''.3''' )
.replace('''_4''' , '''.4''' )
.replace('''_5''' , '''.5''' )
.replace('''_6''' , '''.6''' )
.replace('''_7''' , '''.7''' )
.replace('''_8''' , '''.8''' )
.replace('''_9''' , '''.9''' )
)
lowercase__ = '''.'''.join(SCREAMING_SNAKE_CASE )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
lowercase__ = np.asarray(SCREAMING_SNAKE_CASE ) if not isinstance(SCREAMING_SNAKE_CASE , np.ndarray ) else flax_tensor
lowercase__ = torch.from_numpy(SCREAMING_SNAKE_CASE )
# remove from missing keys
missing_keys.remove(SCREAMING_SNAKE_CASE )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(SCREAMING_SNAKE_CASE )
pt_model.load_state_dict(SCREAMING_SNAKE_CASE )
# re-transform missing_keys to list
lowercase__ = list(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
if len(SCREAMING_SNAKE_CASE ) > 0:
logger.warning(
f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
''' use it for predictions and inference.''' )
return pt_model
| 110 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ,**__lowerCamelCase : Tuple ):
'''simple docstring'''
a = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__lowerCamelCase )
return config
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] ,[0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__lowerCamelCase ,beta_end=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
self.check_over_configs(thresholding=__lowerCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__lowerCamelCase ,prediction_type=__lowerCamelCase ,sample_max_value=__lowerCamelCase ,)
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.00_979 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1e-5
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCamelCase )
a = len(__lowerCamelCase )
a = self.dummy_model()
a = self.dummy_sample_deter
a = torch.manual_seed(0 )
for t in reversed(range(__lowerCamelCase ) ):
# 1. predict noise residual
a = model(__lowerCamelCase ,__lowerCamelCase )
# 2. predict previous mean of sample x_t-1
a = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,generator=__lowerCamelCase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
a = pred_prev_sample
a = torch.sum(torch.abs(__lowerCamelCase ) )
a = torch.mean(torch.abs(__lowerCamelCase ) )
assert abs(result_sum.item() - 258.9_606 ) < 1e-2
assert abs(result_mean.item() - 0.3_372 ) < 1e-3
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config(prediction_type='''v_prediction''' )
a = scheduler_class(**__lowerCamelCase )
a = len(__lowerCamelCase )
a = self.dummy_model()
a = self.dummy_sample_deter
a = torch.manual_seed(0 )
for t in reversed(range(__lowerCamelCase ) ):
# 1. predict noise residual
a = model(__lowerCamelCase ,__lowerCamelCase )
# 2. predict previous mean of sample x_t-1
a = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,generator=__lowerCamelCase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
a = pred_prev_sample
a = torch.sum(torch.abs(__lowerCamelCase ) )
a = torch.mean(torch.abs(__lowerCamelCase ) )
assert abs(result_sum.item() - 202.0_296 ) < 1e-2
assert abs(result_mean.item() - 0.2_631 ) < 1e-3
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCamelCase )
a = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__lowerCamelCase )
a = scheduler.timesteps
for i, timestep in enumerate(__lowerCamelCase ):
if i == len(__lowerCamelCase ) - 1:
a = -1
else:
a = timesteps[i + 1]
a = scheduler.previous_timestep(__lowerCamelCase )
a = prev_t.item()
self.assertEqual(__lowerCamelCase ,__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCamelCase )
a = [1_00, 87, 50, 51, 0]
with self.assertRaises(__lowerCamelCase ,msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCamelCase )
a = [1_00, 87, 50, 1, 0]
a = len(__lowerCamelCase )
with self.assertRaises(__lowerCamelCase ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__lowerCamelCase ,timesteps=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCamelCase )
a = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__lowerCamelCase ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,):
scheduler.set_timesteps(timesteps=__lowerCamelCase )
| 352 |
import argparse
import os
# New Code #
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 import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase__ : Union[str, Any] = 16
UpperCamelCase__ : Dict = 32
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ = 1_6 ) -> Tuple:
"""simple docstring"""
a = AutoTokenizer.from_pretrained('''bert-base-cased''' )
a = load_dataset('''glue''', '''mrpc''' )
def tokenize_function(snake_case_ ):
# max_length=None => use the model max length (it's actually the default)
a = tokenizer(examples['''sentence1'''], examples['''sentence2'''], truncation=snake_case_, max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
a = datasets.map(
snake_case_, batched=snake_case_, remove_columns=['''idx''', '''sentence1''', '''sentence2'''], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
a = tokenized_datasets.rename_column('''label''', '''labels''' )
def collate_fn(snake_case_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
a = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
a = 1_6
elif accelerator.mixed_precision != "no":
a = 8
else:
a = None
return tokenizer.pad(
snake_case_, padding='''longest''', max_length=snake_case_, pad_to_multiple_of=snake_case_, return_tensors='''pt''', )
# Instantiate dataloaders.
a = DataLoader(
tokenized_datasets['''train'''], shuffle=snake_case_, collate_fn=snake_case_, batch_size=snake_case_ )
a = DataLoader(
tokenized_datasets['''validation'''], shuffle=snake_case_, collate_fn=snake_case_, batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase__ : int = mocked_dataloaders # noqa: F811
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> List[Any]:
"""simple docstring"""
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', snake_case_ ) == "1":
a = 2
# Initialize accelerator
a = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
a = config['''lr''']
a = int(config['''num_epochs'''] )
a = int(config['''seed'''] )
a = int(config['''batch_size'''] )
a = evaluate.load('''glue''', '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=snake_case_ )
def inner_training_loop(snake_case_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
a = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''', return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
a = model.to(accelerator.device )
# Instantiate optimizer
a = AdamW(params=model.parameters(), lr=snake_case_ )
a , a = get_dataloaders(snake_case_, snake_case_ )
# Instantiate scheduler
a = get_linear_schedule_with_warmup(
optimizer=snake_case_, num_warmup_steps=1_0_0, num_training_steps=(len(snake_case_ ) * num_epochs), )
# 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.
a , a , a , a , a = accelerator.prepare(
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
a = model(**snake_case_ )
a = outputs.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
a = model(**snake_case_ )
a = outputs.logits.argmax(dim=-1 )
a , a = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=snake_case_, references=snake_case_, )
a = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""", snake_case_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def SCREAMING_SNAKE_CASE__ ( ) -> Tuple:
"""simple docstring"""
a = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''', type=snake_case_, default=snake_case_, choices=['''no''', '''fp16''', '''bf16''', '''fp8'''], help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''', )
parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.''' )
a = parser.parse_args()
a = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(snake_case_, snake_case_ )
if __name__ == "__main__":
main()
| 330 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["MBartTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["MBartTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 10 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowercase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
a_ : int = 1_0000
a_ : Optional[List[str]] = None
a_ : Optional[datasets.Features] = None
class __lowerCamelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
a_ : Dict = ParquetConfig
def lowerCamelCase ( self : Optional[Any] ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase ( self : Any , a_ : Optional[Any] ):
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_ : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(a_ , (str, list, tuple) ):
lowerCAmelCase_ : str = data_files
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Optional[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase_ : str = [dl_manager.iter_files(a_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
lowerCAmelCase_ : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase_ : List[str] = [dl_manager.iter_files(a_ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(a_ ):
with open(a_ , "rb" ) as f:
lowerCAmelCase_ : Dict = datasets.Features.from_arrow_schema(pq.read_schema(a_ ) )
break
splits.append(datasets.SplitGenerator(name=a_ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase ( self : int , a_ : pa.Table ):
if self.info.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_ : Tuple = table_cast(a_ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase ( self : Dict , a_ : Dict ):
lowerCAmelCase_ : Optional[int] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'''' )
for file_idx, file in enumerate(itertools.chain.from_iterable(a_ ) ):
with open(a_ , "rb" ) as f:
lowerCAmelCase_ : Any = pq.ParquetFile(a_ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
lowerCAmelCase_ : List[str] = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'''{file_idx}_{batch_idx}''', self._cast_table(a_ )
except ValueError as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(a_ )}: {e}''' )
raise
| 241 | 0 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __A ( _lowercase ):
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_A = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(_lowercase , id=_lowercase )
| 359 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
__A = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 75 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A , __A , __A , __A , __A , __A , __A , __A , __A , ) -> Optional[Any]:
super().__init__()
if safety_checker is None:
logger.warning(
f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""" )
self.register_modules(
speech_model=__A , speech_processor=__A , vae=__A , text_encoder=__A , tokenizer=__A , unet=__A , scheduler=__A , feature_extractor=__A , )
def __lowerCAmelCase ( self , __A = "auto" ) -> List[str]:
if slice_size == "auto":
lowerCAmelCase_ :Any = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__A )
def __lowerCAmelCase ( self ) -> Optional[int]:
self.enable_attention_slicing(__A )
@torch.no_grad()
def __call__( self , __A , __A=1_6000 , __A = 512 , __A = 512 , __A = 50 , __A = 7.5 , __A = None , __A = 1 , __A = 0.0 , __A = None , __A = None , __A = "pil" , __A = True , __A = None , __A = 1 , **__A , ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = self.speech_processor.feature_extractor(
__A , return_tensors="""pt""" , sampling_rate=__A ).input_features.to(self.device )
lowerCAmelCase_ :List[Any] = self.speech_model.generate(__A , max_length=48_0000 )
lowerCAmelCase_ :Optional[int] = self.speech_processor.tokenizer.batch_decode(__A , skip_special_tokens=__A , normalize=__A )[
0
]
if isinstance(__A , __A ):
lowerCAmelCase_ :List[Any] = 1
elif isinstance(__A , __A ):
lowerCAmelCase_ :Dict = len(__A )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__A )}""" )
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(__A , __A ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__A )}.""" )
# get prompt text embeddings
lowerCAmelCase_ :List[Any] = self.tokenizer(
__A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
lowerCAmelCase_ :Tuple = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowerCAmelCase_ :Optional[int] = 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_ :int = text_input_ids[:, : self.tokenizer.model_max_length]
lowerCAmelCase_ :int = 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_ :Dict = text_embeddings.shape
lowerCAmelCase_ :Optional[Any] = text_embeddings.repeat(1 , __A , 1 )
lowerCAmelCase_ :str = text_embeddings.view(bs_embed * num_images_per_prompt , __A , -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_ :int = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCAmelCase_ :List[str]
if negative_prompt is None:
lowerCAmelCase_ :Union[str, Any] = [""""""] * batch_size
elif type(__A ) is not type(__A ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(__A )} !="""
f""" {type(__A )}.""" )
elif isinstance(__A , __A ):
lowerCAmelCase_ :Optional[int] = [negative_prompt]
elif batch_size != len(__A ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(__A )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
""" the batch size of `prompt`.""" )
else:
lowerCAmelCase_ :Dict = negative_prompt
lowerCAmelCase_ :List[str] = text_input_ids.shape[-1]
lowerCAmelCase_ :Dict = self.tokenizer(
__A , padding="""max_length""" , max_length=__A , truncation=__A , return_tensors="""pt""" , )
lowerCAmelCase_ :Union[str, Any] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowerCAmelCase_ :Dict = uncond_embeddings.shape[1]
lowerCAmelCase_ :Union[str, Any] = uncond_embeddings.repeat(1 , __A , 1 )
lowerCAmelCase_ :Optional[Any] = uncond_embeddings.view(batch_size * num_images_per_prompt , __A , -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_ :List[str] = 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_ :int = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowerCAmelCase_ :Optional[int] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowerCAmelCase_ :Optional[Any] = torch.randn(__A , generator=__A , device="""cpu""" , dtype=__A ).to(
self.device )
else:
lowerCAmelCase_ :List[str] = torch.randn(__A , generator=__A , device=self.device , dtype=__A )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
lowerCAmelCase_ :Tuple = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowerCAmelCase_ :Optional[int] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCAmelCase_ :List[Any] = 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_ :List[Any] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCAmelCase_ :List[str] = {}
if accepts_eta:
lowerCAmelCase_ :Dict = eta
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase_ :int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCAmelCase_ :Dict = self.scheduler.scale_model_input(__A , __A )
# predict the noise residual
lowerCAmelCase_ :Optional[int] = self.unet(__A , __A , encoder_hidden_states=__A ).sample
# perform guidance
if do_classifier_free_guidance:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = noise_pred.chunk(2 )
lowerCAmelCase_ :Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowerCAmelCase_ :int = self.scheduler.step(__A , __A , __A , **__A ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__A , __A , __A )
lowerCAmelCase_ :str = 1 / 0.1_8_2_1_5 * latents
lowerCAmelCase_ :Union[str, Any] = self.vae.decode(__A ).sample
lowerCAmelCase_ :List[str] = (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_ :int = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCAmelCase_ :Dict = self.numpy_to_pil(__A )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=__A , nsfw_content_detected=__A )
| 84 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
lowerCAmelCase_ :int = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , lowercase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 84 | 1 |
"""simple docstring"""
import random
from typing import Any
def _snake_case ( lowerCamelCase__ : List[str] ) -> list[Any]:
for _ in range(len(__SCREAMING_SNAKE_CASE ) ):
lowerCamelCase_ : List[Any] =random.randint(0 , len(__SCREAMING_SNAKE_CASE ) - 1 )
lowerCamelCase_ : Optional[Any] =random.randint(0 , len(__SCREAMING_SNAKE_CASE ) - 1 )
lowerCamelCase_ : Optional[int] =data[b], data[a]
return data
if __name__ == "__main__":
A__ : Dict = [0, 1, 2, 3, 4, 5, 6, 7]
A__ : Tuple = ['python', 'says', 'hello', '!']
print('Fisher-Yates Shuffle:')
print('List', integers, strings)
print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 370 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : list[int] ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def _snake_case ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if curr_ind == len(lowerCamelCase__ ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(lowerCamelCase__ ) ):
if valid_connection(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
# Insert current vertex into path as next transition
lowerCamelCase_ : Tuple =next_ver
# Validate created path
if util_hamilton_cycle(lowerCamelCase__ , lowerCamelCase__ , curr_ind + 1 ):
return True
# Backtrack
lowerCamelCase_ : int =-1
return False
def _snake_case ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int = 0 ) -> list[int]:
lowerCamelCase_ : Optional[Any] =[-1] * (len(lowerCamelCase__ ) + 1)
# initialize start and end of path with starting index
lowerCamelCase_ : Optional[int] =start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(lowerCamelCase__ , lowerCamelCase__ , 1 ) else []
| 209 | 0 |
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(A__ ):
for j in range(A__ ):
if dist[i][j] != float("inf" ):
print(int(dist[i][j] ) , end="\t" )
else:
print("INF" , end="\t" )
print()
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
snake_case_ = [[float("inf" ) for _ in range(A__ )] for _ in range(A__ )]
for i in range(A__ ):
for j in range(A__ ):
snake_case_ = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(A__ ):
# looping through rows of graph array
for i in range(A__ ):
# looping through columns of graph array
for j in range(A__ ):
if (
dist[i][k] != float("inf" )
and dist[k][j] != float("inf" )
and dist[i][k] + dist[k][j] < dist[i][j]
):
snake_case_ = dist[i][k] + dist[k][j]
_print_dist(A__ , A__ )
return dist, v
if __name__ == "__main__":
lowercase__ : Any = int(input("Enter number of vertices: "))
lowercase__ : Tuple = int(input("Enter number of edges: "))
lowercase__ : Union[str, Any] = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
lowercase__ : Optional[Any] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
lowercase__ : Union[str, Any] = int(input("Enter source:"))
lowercase__ : Any = int(input("Enter destination:"))
lowercase__ : Union[str, Any] = float(input("Enter weight:"))
lowercase__ : Any = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 187 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase_ : Dict = (boundary[1] - boundary[0]) / steps
UpperCAmelCase_ : Optional[int] = boundary[0]
UpperCAmelCase_ : str = boundary[1]
UpperCAmelCase_ : Tuple = make_points(A__ ,A__ ,A__ )
UpperCAmelCase_ : List[str] = 0.0
y += (h / 2.0) * f(A__ )
for i in x_i:
# print(i)
y += h * f(A__ )
y += (h / 2.0) * f(A__ )
return y
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : Union[str, Any] = a + h
while x < (b - h):
yield x
UpperCAmelCase_ : Optional[Any] = x + h
def snake_case ( A__ ): # enter your function here
UpperCAmelCase_ : Dict = (x - 0) * (x - 0)
return y
def snake_case ( ):
UpperCAmelCase_ : Dict = 0.0 # Lower bound of integration
UpperCAmelCase_ : Optional[int] = 1.0 # Upper bound of integration
UpperCAmelCase_ : Dict = 10.0 # define number of steps or resolution
UpperCAmelCase_ : List[Any] = [a, b] # define boundary of integration
UpperCAmelCase_ : Union[str, Any] = method_a(A__ ,A__ )
print(F"""y = {y}""" )
if __name__ == "__main__":
main()
| 268 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : int ) -> list:
'''simple docstring'''
__snake_case : str = word.split()
def justify(UpperCAmelCase_ : list , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> str:
__snake_case : Tuple = max_width - width
__snake_case : List[str] = len(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
__snake_case : List[str] = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
__snake_case : Tuple = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
__snake_case : Tuple = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(UpperCAmelCase_ ):
num_spaces_between_words_list[i] += 1
__snake_case : List[str] = []
for i in range(UpperCAmelCase_ ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(UpperCAmelCase_ )
__snake_case : str = []
__snake_case : list[str] = []
__snake_case : Optional[int] = 0
for word in words:
if width + len(UpperCAmelCase_ ) + len(UpperCAmelCase_ ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(UpperCAmelCase_ )
width += len(UpperCAmelCase_ )
else:
# justify the line and add it to result
answer.append(justify(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) )
# reset new line and new width
__snake_case : Dict = [word], len(UpperCAmelCase_ )
__snake_case : Dict = max_width - width - len(UpperCAmelCase_ )
answer.append(' '.join(UpperCAmelCase_ ) + (remaining_spaces + 1) * ' ' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 360 | """simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_a : int= False
class UpperCamelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase (self : Optional[Any]) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase (self : int) -> Tuple:
__snake_case : str = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa)
pipe.to(_A)
pipe.set_progress_bar_config(disable=_A)
__snake_case : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
__snake_case : List[Any] = torch.manual_seed(0)
__snake_case : Optional[int] = pipe.dual_guided(
prompt='first prompt' , image=_A , text_to_image_strength=0.75 , generator=_A , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_A)
__snake_case : Optional[Any] = VersatileDiffusionPipeline.from_pretrained(_A , torch_dtype=torch.floataa)
pipe.to(_A)
pipe.set_progress_bar_config(disable=_A)
__snake_case : Tuple = generator.manual_seed(0)
__snake_case : int = pipe.dual_guided(
prompt='first prompt' , image=_A , text_to_image_strength=0.75 , generator=_A , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
assert np.abs(image - new_image).sum() < 1E-5, "Models don't have the same forward pass"
def _lowercase (self : Optional[Any]) -> Optional[int]:
__snake_case : Optional[Any] = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa)
pipe.to(_A)
pipe.set_progress_bar_config(disable=_A)
__snake_case : Tuple = 'cyberpunk 2077'
__snake_case : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
__snake_case : str = torch.manual_seed(0)
__snake_case : Union[str, Any] = pipe.dual_guided(
prompt=_A , image=_A , text_to_image_strength=0.75 , generator=_A , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
__snake_case : Optional[Any] = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Tuple = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
__snake_case : List[str] = 'A painting of a squirrel eating a burger '
__snake_case : str = torch.manual_seed(0)
__snake_case : str = pipe.text_to_image(
prompt=_A , generator=_A , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy').images
__snake_case : str = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Any = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
__snake_case : List[str] = pipe.image_variation(_A , generator=_A , output_type='numpy').images
__snake_case : Dict = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : List[Any] = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
| 95 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a ={
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a =["""VisionEncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a =["""TFVisionEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a =["""FlaxVisionEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
a =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 73 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a ="""\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
a ="""\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""
a ="""
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
\"accuracy\": Accuracy
\"f1\": F1 score
\"precision\": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'precision@10': 1.0}
"""
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
return float((preds == labels).mean() )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
__lowerCamelCase : Optional[Any] = simple_accuracy(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Tuple = float(fa_score(y_true=lowerCamelCase__ , y_pred=lowerCamelCase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
__lowerCamelCase : Any = np.array(lowerCamelCase__ )
__lowerCamelCase : List[Any] = np.array(lowerCamelCase__ )
__lowerCamelCase : Any = en_sentvecs.shape[0]
# mean centering
__lowerCamelCase : Union[str, Any] = en_sentvecs - np.mean(lowerCamelCase__ , axis=0 )
__lowerCamelCase : Dict = in_sentvecs - np.mean(lowerCamelCase__ , axis=0 )
__lowerCamelCase : Optional[int] = cdist(lowerCamelCase__ , lowerCamelCase__ , 'cosine' )
__lowerCamelCase : Optional[Any] = np.array(range(lowerCamelCase__ ) )
__lowerCamelCase : Dict = sim.argsort(axis=1 )[:, :1_0]
__lowerCamelCase : Optional[int] = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
def lowerCAmelCase ( self : Optional[Any]):
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]')
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('int64')
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32')),
'references': datasets.Value('int64')
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32')),
}) ,codebase_urls=[] ,reference_urls=[] ,format='numpy' if self.config_name != 'cvit-mkb-clsr' else None ,)
def lowerCAmelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : Optional[Any]):
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]')
| 73 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __a ( nn.Module ):
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float = 0.0
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : bool = True
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : jnp.dtype = jnp.floataa
def snake_case_ ( self ):
_lowerCamelCase = []
_lowerCamelCase = []
for i in range(self.num_layers ):
_lowerCamelCase = self.in_channels if i == 0 else self.out_channels
_lowerCamelCase = FlaxResnetBlockaD(
in_channels=a__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(a__ )
_lowerCamelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(a__ )
_lowerCamelCase = resnets
_lowerCamelCase = attentions
if self.add_downsample:
_lowerCamelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , a__ , a__ , a__ , a__=True ):
_lowerCamelCase = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_lowerCamelCase = resnet(a__ , a__ , deterministic=a__ )
_lowerCamelCase = attn(a__ , a__ , deterministic=a__ )
output_states += (hidden_states,)
if self.add_downsample:
_lowerCamelCase = self.downsamplers_a(a__ )
output_states += (hidden_states,)
return hidden_states, output_states
class __a ( nn.Module ):
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float = 0.0
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : bool = True
SCREAMING_SNAKE_CASE__ : jnp.dtype = jnp.floataa
def snake_case_ ( self ):
_lowerCamelCase = []
for i in range(self.num_layers ):
_lowerCamelCase = self.in_channels if i == 0 else self.out_channels
_lowerCamelCase = FlaxResnetBlockaD(
in_channels=a__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(a__ )
_lowerCamelCase = resnets
if self.add_downsample:
_lowerCamelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , a__ , a__ , a__=True ):
_lowerCamelCase = ()
for resnet in self.resnets:
_lowerCamelCase = resnet(a__ , a__ , deterministic=a__ )
output_states += (hidden_states,)
if self.add_downsample:
_lowerCamelCase = self.downsamplers_a(a__ )
output_states += (hidden_states,)
return hidden_states, output_states
class __a ( nn.Module ):
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float = 0.0
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : bool = True
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : jnp.dtype = jnp.floataa
def snake_case_ ( self ):
_lowerCamelCase = []
_lowerCamelCase = []
for i in range(self.num_layers ):
_lowerCamelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_lowerCamelCase = self.prev_output_channel if i == 0 else self.out_channels
_lowerCamelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(a__ )
_lowerCamelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(a__ )
_lowerCamelCase = resnets
_lowerCamelCase = attentions
if self.add_upsample:
_lowerCamelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , a__ , a__ , a__ , a__ , a__=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_lowerCamelCase = res_hidden_states_tuple[-1]
_lowerCamelCase = res_hidden_states_tuple[:-1]
_lowerCamelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_lowerCamelCase = resnet(a__ , a__ , deterministic=a__ )
_lowerCamelCase = attn(a__ , a__ , deterministic=a__ )
if self.add_upsample:
_lowerCamelCase = self.upsamplers_a(a__ )
return hidden_states
class __a ( nn.Module ):
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float = 0.0
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : bool = True
SCREAMING_SNAKE_CASE__ : jnp.dtype = jnp.floataa
def snake_case_ ( self ):
_lowerCamelCase = []
for i in range(self.num_layers ):
_lowerCamelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_lowerCamelCase = self.prev_output_channel if i == 0 else self.out_channels
_lowerCamelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(a__ )
_lowerCamelCase = resnets
if self.add_upsample:
_lowerCamelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , a__ , a__ , a__ , a__=True ):
for resnet in self.resnets:
# pop res hidden states
_lowerCamelCase = res_hidden_states_tuple[-1]
_lowerCamelCase = res_hidden_states_tuple[:-1]
_lowerCamelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_lowerCamelCase = resnet(a__ , a__ , deterministic=a__ )
if self.add_upsample:
_lowerCamelCase = self.upsamplers_a(a__ )
return hidden_states
class __a ( nn.Module ):
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float = 0.0
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : bool = False
SCREAMING_SNAKE_CASE__ : jnp.dtype = jnp.floataa
def snake_case_ ( self ):
# there is always at least one resnet
_lowerCamelCase = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_lowerCamelCase = []
for _ in range(self.num_layers ):
_lowerCamelCase = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(a__ )
_lowerCamelCase = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(a__ )
_lowerCamelCase = resnets
_lowerCamelCase = attentions
def __call__( self , a__ , a__ , a__ , a__=True ):
_lowerCamelCase = self.resnets[0](a__ , a__ )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_lowerCamelCase = attn(a__ , a__ , deterministic=a__ )
_lowerCamelCase = resnet(a__ , a__ , deterministic=a__ )
return hidden_states
| 80 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ : int =logging.get_logger(__name__)
A_ : Tuple ={
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : int = "deta"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self , a__=None , a__=9_00 , a__=20_48 , a__=6 , a__=20_48 , a__=8 , a__=6 , a__=10_24 , a__=8 , a__=0.0 , a__=True , a__="relu" , a__=2_56 , a__=0.1 , a__=0.0 , a__=0.0 , a__=0.02 , a__=1.0 , a__=True , a__=False , a__="sine" , a__=5 , a__=4 , a__=4 , a__=True , a__=3_00 , a__=True , a__=True , a__=1 , a__=5 , a__=2 , a__=1 , a__=1 , a__=5 , a__=2 , a__=0.1 , a__=0.25 , **a__ , ):
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
_lowerCamelCase = CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
else:
if isinstance(a__ , a__ ):
_lowerCamelCase = backbone_config.pop('model_type' )
_lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
_lowerCamelCase = config_class.from_dict(a__ )
_lowerCamelCase = backbone_config
_lowerCamelCase = num_queries
_lowerCamelCase = max_position_embeddings
_lowerCamelCase = d_model
_lowerCamelCase = encoder_ffn_dim
_lowerCamelCase = encoder_layers
_lowerCamelCase = encoder_attention_heads
_lowerCamelCase = decoder_ffn_dim
_lowerCamelCase = decoder_layers
_lowerCamelCase = decoder_attention_heads
_lowerCamelCase = dropout
_lowerCamelCase = attention_dropout
_lowerCamelCase = activation_dropout
_lowerCamelCase = activation_function
_lowerCamelCase = init_std
_lowerCamelCase = init_xavier_std
_lowerCamelCase = encoder_layerdrop
_lowerCamelCase = auxiliary_loss
_lowerCamelCase = position_embedding_type
# deformable attributes
_lowerCamelCase = num_feature_levels
_lowerCamelCase = encoder_n_points
_lowerCamelCase = decoder_n_points
_lowerCamelCase = two_stage
_lowerCamelCase = two_stage_num_proposals
_lowerCamelCase = with_box_refine
_lowerCamelCase = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.' )
# Hungarian matcher
_lowerCamelCase = class_cost
_lowerCamelCase = bbox_cost
_lowerCamelCase = giou_cost
# Loss coefficients
_lowerCamelCase = mask_loss_coefficient
_lowerCamelCase = dice_loss_coefficient
_lowerCamelCase = bbox_loss_coefficient
_lowerCamelCase = giou_loss_coefficient
_lowerCamelCase = eos_coefficient
_lowerCamelCase = focal_alpha
super().__init__(is_encoder_decoder=a__ , **a__ )
@property
def snake_case_ ( self ):
return self.encoder_attention_heads
@property
def snake_case_ ( self ):
return self.d_model
def snake_case_ ( self ):
_lowerCamelCase = copy.deepcopy(self.__dict__ )
_lowerCamelCase = self.backbone_config.to_dict()
_lowerCamelCase = self.__class__.model_type
return output
| 80 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase=13 , lowercase=30 , lowercase=2 , lowercase=3 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase=3 , lowercase=None , lowercase=2 , ) -> Optional[int]:
'''simple docstring'''
a__ : List[Any] = parent
a__ : str = batch_size
a__ : Any = image_size
a__ : int = patch_size
a__ : Tuple = num_channels
a__ : Optional[int] = is_training
a__ : int = use_labels
a__ : Union[str, Any] = hidden_size
a__ : str = num_hidden_layers
a__ : List[Any] = num_attention_heads
a__ : Dict = intermediate_size
a__ : Any = hidden_act
a__ : Any = hidden_dropout_prob
a__ : List[str] = attention_probs_dropout_prob
a__ : Dict = type_sequence_label_size
a__ : List[Any] = initializer_range
a__ : Dict = scope
a__ : List[Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
a__ : Any = (image_size // patch_size) ** 2
a__ : Any = num_patches + 2
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a__ : int = None
if self.use_labels:
a__ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__ : Tuple = self.get_config()
return config, pixel_values, labels
def __lowercase ( self) -> Dict:
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , 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 , is_decoder=lowercase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__ : int = DeiTModel(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Union[str, Any] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __lowercase ( self , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
a__ : List[str] = DeiTForMaskedImageModeling(config=lowercase)
model.to(lowercase)
model.eval()
a__ : List[Any] = model(lowercase)
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
a__ : Tuple = 1
a__ : Optional[Any] = DeiTForMaskedImageModeling(lowercase)
model.to(lowercase)
model.eval()
a__ : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
a__ : Dict = model(lowercase)
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size))
def __lowercase ( self , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : int = self.type_sequence_label_size
a__ : List[str] = DeiTForImageClassification(lowercase)
model.to(lowercase)
model.eval()
a__ : Tuple = model(lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
a__ : Union[str, Any] = 1
a__ : Optional[Any] = DeiTForImageClassification(lowercase)
model.to(lowercase)
model.eval()
a__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
a__ : str = model(lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Tuple = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) ,
) : Union[str, Any] = config_and_inputs
a__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : Dict = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__A : str = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__A : List[str] = False
__A : Union[str, Any] = False
__A : Optional[Any] = False
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : List[str] = DeiTModelTester(self)
a__ : str = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37)
def __lowercase ( self) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds')
def __lowercase ( self) -> Tuple:
'''simple docstring'''
pass
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ , a__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Union[str, Any] = model_class(lowercase)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
a__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear))
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ , a__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Union[str, Any] = model_class(lowercase)
a__ : List[str] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ : Union[str, Any] = [*signature.parameters.keys()]
a__ : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowercase)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase)
def __lowercase ( self , lowercase , lowercase , lowercase=False) -> Tuple:
'''simple docstring'''
a__ : Optional[int] = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
a__ , a__ : int = self.model_tester.prepare_config_and_inputs_for_common()
a__ : List[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowercase)
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
a__ : List[str] = model_class(lowercase)
model.to(lowercase)
model.train()
a__ : str = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
a__ : str = model(**lowercase).loss
loss.backward()
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ , a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
a__ : List[str] = False
a__ : List[str] = True
for model_class in self.all_model_classes:
if model_class in get_values(lowercase) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
a__ : int = model_class(lowercase)
model.gradient_checkpointing_enable()
model.to(lowercase)
model.train()
a__ : List[Any] = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
a__ : List[Any] = model(**lowercase).loss
loss.backward()
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ , a__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
a__ : Tuple = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowercase),
*get_values(lowercase),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'):
a__ : List[str] = problem_type['title']
a__ : Any = problem_type['num_labels']
a__ : Union[str, Any] = model_class(lowercase)
model.to(lowercase)
model.train()
a__ : List[Any] = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
if problem_type["num_labels"] > 1:
a__ : Any = inputs['labels'].unsqueeze(1).repeat(1 , problem_type['num_labels'])
a__ : Tuple = inputs['labels'].to(problem_type['dtype'])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowercase) as warning_list:
a__ : Optional[Any] = model(**lowercase).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
F'Something is going wrong in the regression problem: intercepted {w.message}')
loss.backward()
@slow
def __lowercase ( self) -> Any:
'''simple docstring'''
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Any = DeiTModel.from_pretrained(lowercase)
self.assertIsNotNone(lowercase)
def A_ ( ) -> Any:
a__ : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class A__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self) -> List[str]:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
if is_vision_available()
else None
)
@slow
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Optional[Any] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
lowercase)
a__ : Optional[Any] = self.default_image_processor
a__ : str = prepare_img()
a__ : Optional[int] = image_processor(images=lowercase , return_tensors='pt').to(lowercase)
# forward pass
with torch.no_grad():
a__ : int = model(**lowercase)
# verify the logits
a__ : Tuple = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , lowercase)
a__ : int = torch.tensor([-1.02_66, 0.19_12, -1.28_61]).to(lowercase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4))
@slow
@require_accelerate
@require_torch_gpu
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : Dict = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto')
a__ : Union[str, Any] = self.default_image_processor
a__ : List[str] = prepare_img()
a__ : int = image_processor(images=lowercase , return_tensors='pt')
a__ : str = inputs.pixel_values.to(lowercase)
# forward pass to make sure inference works in fp16
with torch.no_grad():
a__ : Any = model(lowercase)
| 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['DeiTFeatureExtractor']
__a = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = None
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE_ = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , lowerCAmelCase__ )
def __A ( self : Any ) -> str:
SCREAMING_SNAKE_CASE_ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = os.path.join(lowerCAmelCase__ , "feat_extract.json" )
feat_extract_first.to_json_file(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = self.feature_extraction_class.from_json_file(lowerCAmelCase__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = feat_extract_first.save_pretrained(lowerCAmelCase__ )[0]
check_json_file_has_correct_format(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = self.feature_extraction_class.from_pretrained(lowerCAmelCase__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __A ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.feature_extraction_class()
self.assertIsNotNone(lowerCAmelCase__ )
| 366 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
debug_launcher(test_script.main )
def A_ ( self ):
'''simple docstring'''
debug_launcher(test_ops.main )
| 311 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
a : str = getLogger(__name__)
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 8 , __magic_name__ = 1024 , __magic_name__="val" , __magic_name__=None , __magic_name__=False , __magic_name__="summarization" , __magic_name__=None , __magic_name__=1 , __magic_name__ = None , __magic_name__="" , **__magic_name__ , ):
'''simple docstring'''
UpperCAmelCase : List[Any] = str(__magic_name__ )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=__magic_name__ )
UpperCAmelCase : List[str] = Path(__magic_name__ )
UpperCAmelCase : Dict = save_dir.joinpath(F"rank_{local_rank}_output.json" )
torch.cuda.set_device(__magic_name__ )
UpperCAmelCase : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(__magic_name__ ).cuda()
if fpaa:
UpperCAmelCase : int = model.half()
# determine if we need to increase num_beams
use_task_specific_params(__magic_name__ , __magic_name__ ) # update config with task specific params
UpperCAmelCase : Dict = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase : Optional[Any] = num_return_sequences
UpperCAmelCase : Dict = AutoTokenizer.from_pretrained(__magic_name__ )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase : Any = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase : Tuple = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase : Dict = SeqaSeqDataset(
__magic_name__ , __magic_name__ , __magic_name__ , max_target_length=1024 , type_path=__magic_name__ , n_obs=__magic_name__ , prefix=__magic_name__ , **__magic_name__ , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase : int = ds.make_sortish_sampler(__magic_name__ , distributed=__magic_name__ , add_extra_examples=__magic_name__ , shuffle=__magic_name__ )
UpperCAmelCase : List[Any] = DataLoader(__magic_name__ , sampler=__magic_name__ , batch_size=__magic_name__ , collate_fn=ds.collate_fn )
UpperCAmelCase : Any = []
for batch in tqdm(__magic_name__ ):
UpperCAmelCase : List[Any] = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=__magic_name__ , num_beams=__magic_name__ , **__magic_name__ , )
UpperCAmelCase : Optional[int] = tokenizer.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ , clean_up_tokenization_spaces=__magic_name__ )
UpperCAmelCase : int = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase : List[Any] = chunks(__magic_name__ , __magic_name__ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(__magic_name__ ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(__magic_name__ , __magic_name__ )
return results, sampler.num_replicas
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : str = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=__magic_name__ , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=__magic_name__ , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=__magic_name__ , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=__magic_name__ , default=__magic_name__ )
parser.add_argument(
"--type_path" , type=__magic_name__ , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=__magic_name__ , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=__magic_name__ , default=8 , required=__magic_name__ , help="batch size" )
parser.add_argument(
"--local_rank" , type=__magic_name__ , default=-1 , required=__magic_name__ , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=__magic_name__ , default=__magic_name__ , required=__magic_name__ , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=__magic_name__ , default=1 , required=__magic_name__ , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=__magic_name__ , default=600 , required=__magic_name__ , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=__magic_name__ , default=__magic_name__ , required=__magic_name__ )
parser.add_argument("--tgt_lang" , type=__magic_name__ , default=__magic_name__ , required=__magic_name__ )
parser.add_argument(
"--prefix" , type=__magic_name__ , required=__magic_name__ , default=__magic_name__ , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase : Union[str, Any] = time.time()
UpperCAmelCase , UpperCAmelCase : Dict = parser.parse_known_args()
UpperCAmelCase : Tuple = parse_numeric_n_bool_cl_kwargs(__magic_name__ )
if generate_kwargs and args.local_rank <= 0:
print(F"parsed the following generate kwargs: {generate_kwargs}" )
UpperCAmelCase : Union[str, Any] = Path(args.save_dir + "_tmp" )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ ) # this handles locking.
UpperCAmelCase : List[Any] = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"Found files at {json_save_dir} please move or remove them." )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase : Optional[Any] = {}
if args.src_lang is not None:
UpperCAmelCase : List[str] = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase : Dict = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=__magic_name__ )
UpperCAmelCase , UpperCAmelCase : str = eval_data_dir(
args.data_dir , __magic_name__ , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=__magic_name__ , **__magic_name__ , )
if args.local_rank <= 0:
UpperCAmelCase : List[str] = Path(args.save_dir )
save_dir.mkdir(exist_ok=__magic_name__ )
UpperCAmelCase : str = gather_results_from_each_node(__magic_name__ , __magic_name__ , args.sync_timeout )
UpperCAmelCase : Dict = combine_partial_results(__magic_name__ )
if args.num_return_sequences > 1:
UpperCAmelCase : int = save_dir.joinpath("pseudolabel_results.json" )
print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/" )
save_json(__magic_name__ , __magic_name__ )
return
UpperCAmelCase : Dict = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(__magic_name__ ) as f:
UpperCAmelCase : Dict = [x.rstrip() for x in f.readlines()][: len(__magic_name__ )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase : Optional[int] = "translation" in args.task
UpperCAmelCase : str = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase : Tuple = "bleu" if calc_bleu else "rouge"
UpperCAmelCase : Dict = score_fn(__magic_name__ , __magic_name__ )
UpperCAmelCase : Any = len(__magic_name__ )
UpperCAmelCase : Union[str, Any] = time.time() - start_time
UpperCAmelCase : Dict = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase : Optional[Any] = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase : Dict = save_dir.joinpath(F"{args.type_path}_{metric_name}.json" )
save_json(__magic_name__ , __magic_name__ , indent=__magic_name__ )
print(__magic_name__ )
write_txt_file(__magic_name__ , save_dir.joinpath(F"{args.type_path}_generations.txt" ) )
if args.debug:
write_txt_file(__magic_name__ , save_dir.joinpath(F"{args.type_path}.target" ) )
else:
shutil.rmtree(__magic_name__ )
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Tuple = []
for partial_result in partial_results:
records.extend(__magic_name__ )
UpperCAmelCase : Optional[Any] = sorted(__magic_name__ , key=lambda __magic_name__ : x["id"] )
UpperCAmelCase : List[Any] = [x["pred"] for x in records]
return preds
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase : Union[str, Any] = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase : Dict = list(save_dir.glob("rank_*.json" ) )
if len(__magic_name__ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase : List[str] = lmap(__magic_name__ , __magic_name__ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 311 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
A : Union[str, Any] = logging.get_logger(__name__)
A : Union[str, Any] = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
A : List[str] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__ ) -> Any:
for attribute in key.split('''.''' ):
__a = getattr(a__ , a__ )
if weight_type is not None:
__a = getattr(a__ , a__ ).shape
else:
__a = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__a = value
elif weight_type == "weight_g":
__a = value
elif weight_type == "weight_v":
__a = value
elif weight_type == "bias":
__a = value
elif weight_type == "running_mean":
__a = value
elif weight_type == "running_var":
__a = value
elif weight_type == "num_batches_tracked":
__a = value
elif weight_type == "inv_freq":
__a = value
else:
__a = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def __lowerCAmelCase ( a__ , a__ , a__ ) -> str:
__a = []
__a = fairseq_model.state_dict()
__a = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
__a = False
if "conv_layers" in name:
load_conv_layer(
a__ , a__ , a__ , a__ , hf_model.config.feat_extract_norm == '''group''' , )
__a = True
else:
for key, mapped_key in MAPPING.items():
__a = '''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
__a = True
if "*" in mapped_key:
__a = name.split(a__ )[0].split('''.''' )[-2]
__a = mapped_key.replace('''*''' , a__ )
if "pos_bias_u" in name:
__a = None
elif "pos_bias_v" in name:
__a = None
elif "weight_g" in name:
__a = '''weight_g'''
elif "weight_v" in name:
__a = '''weight_v'''
elif "bias" in name:
__a = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__a = '''weight'''
elif "running_mean" in name:
__a = '''running_mean'''
elif "inv_freq" in name:
__a = '''inv_freq'''
elif "running_var" in name:
__a = '''running_var'''
elif "num_batches_tracked" in name:
__a = '''num_batches_tracked'''
else:
__a = None
set_recursively(a__ , a__ , a__ , a__ , a__ )
continue
if not is_used:
unused_weights.append(a__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__ ) -> Tuple:
__a = full_name.split('''conv_layers.''' )[-1]
__a = name.split('''.''' )
__a = int(items[0] )
__a = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(a__ )
@torch.no_grad()
def __lowerCAmelCase ( a__ , a__ , a__=None , a__=None , a__=True ) -> str:
if config_path is not None:
__a = WavaVecaConformerConfig.from_pretrained(a__ , hidden_act='''swish''' )
else:
__a = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
__a = '''rotary'''
if is_finetuned:
if dict_path:
__a = Dictionary.load(a__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__a = target_dict.pad_index
__a = target_dict.bos_index
__a = target_dict.eos_index
__a = len(target_dict.symbols )
__a = os.path.join(a__ , '''vocab.json''' )
if not os.path.isdir(a__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__ ) )
return
os.makedirs(a__ , exist_ok=a__ )
__a = target_dict.indices
# fairseq has the <pad> and <s> switched
__a = 0
__a = 1
with open(a__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(a__ , a__ )
__a = WavaVecaCTCTokenizer(
a__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=a__ , )
__a = True if config.feat_extract_norm == '''layer''' else False
__a = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=a__ , return_attention_mask=a__ , )
__a = WavaVecaProcessor(feature_extractor=a__ , tokenizer=a__ )
processor.save_pretrained(a__ )
__a = WavaVecaConformerForCTC(a__ )
else:
__a = WavaVecaConformerForPreTraining(a__ )
if is_finetuned:
__a , __a , __a = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__a = argparse.Namespace(task='''audio_pretraining''' )
__a = fairseq.tasks.setup_task(a__ )
__a , __a , __a = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=a__ )
__a = model[0].eval()
recursively_load_weights(a__ , a__ , not is_finetuned )
hf_wavavec.save_pretrained(a__ )
if __name__ == "__main__":
A : Dict = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
A : Optional[Any] = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
) | 33 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
A : str = logging.get_logger(__name__)
A : Any = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'label_embs_concat': 'label_embeddings_concat',
'mask_emb': 'masked_spec_embed',
'spk_proj': 'speaker_proj',
}
A : Optional[Any] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'label_embeddings_concat',
'speaker_proj',
'layer_norm_for_extract',
]
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__ ) -> Dict:
for attribute in key.split('''.''' ):
__a = getattr(a__ , a__ )
if weight_type is not None:
__a = getattr(a__ , a__ ).shape
else:
__a = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__a = value
elif weight_type == "weight_g":
__a = value
elif weight_type == "weight_v":
__a = value
elif weight_type == "bias":
__a = value
else:
__a = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def __lowerCAmelCase ( a__ , a__ ) -> List[str]:
__a = []
__a = fairseq_model.state_dict()
__a = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__a = False
if "conv_layers" in name:
load_conv_layer(
a__ , a__ , a__ , a__ , hf_model.config.feat_extract_norm == '''group''' , )
__a = True
else:
for key, mapped_key in MAPPING.items():
__a = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__a = True
if "*" in mapped_key:
__a = name.split(a__ )[0].split('''.''' )[-2]
__a = mapped_key.replace('''*''' , a__ )
if "weight_g" in name:
__a = '''weight_g'''
elif "weight_v" in name:
__a = '''weight_v'''
elif "bias" in name:
__a = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__a = '''weight'''
else:
__a = None
set_recursively(a__ , a__ , a__ , a__ , a__ )
continue
if not is_used:
unused_weights.append(a__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__ ) -> int:
__a = full_name.split('''conv_layers.''' )[-1]
__a = name.split('''.''' )
__a = int(items[0] )
__a = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
__a = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(a__ )
@torch.no_grad()
def __lowerCAmelCase ( a__ , a__ , a__=None , a__=None , a__=True ) -> Tuple:
if config_path is not None:
__a = UniSpeechSatConfig.from_pretrained(a__ )
else:
__a = UniSpeechSatConfig()
__a = ''''''
if is_finetuned:
__a = UniSpeechSatForCTC(a__ )
else:
__a = UniSpeechSatForPreTraining(a__ )
__a , __a , __a = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__a = model[0].eval()
recursively_load_weights(a__ , a__ )
hf_wavavec.save_pretrained(a__ )
if __name__ == "__main__":
A : List[Any] = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
A : Dict = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
) | 33 | 1 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(snake_case , snake_case ):
raise TypeError('Input value must be a \'int\' type' )
return bin(snake_case ).count('1' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 316 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
UpperCamelCase : Union[str, Any] = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
UpperCamelCase : Any = subprocess.check_output(f'''git diff --name-only {fork_point_sha}'''.split()).decode("utf-8").split()
UpperCamelCase : Tuple = "|".join(sys.argv[1:])
UpperCamelCase : Optional[int] = re.compile(Rf'''^({joined_dirs}).*?\.py$''')
UpperCamelCase : Optional[Any] = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 316 | 1 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
lowerCAmelCase_ = {
"""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"""
),
},
}
lowerCAmelCase_ = {
"""allenai/longformer-base-4096""": 4096,
"""allenai/longformer-large-4096""": 4096,
"""allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
"""allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
"""allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase_ ( )-> int:
_snake_case : Dict = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
_snake_case : Any = bs[:]
_snake_case : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase )
cs.append(2**8 + n )
n += 1
_snake_case : int = [chr(lowerCAmelCase ) for n in cs]
return dict(zip(lowerCAmelCase , lowerCAmelCase ) )
def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> Tuple:
_snake_case : Union[str, Any] = set()
_snake_case : Optional[int] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_snake_case : List[str] = char
return pairs
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
a_ : Optional[int] =VOCAB_FILES_NAMES
a_ : Union[str, Any] =PRETRAINED_VOCAB_FILES_MAP
a_ : Union[str, Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ : Optional[Any] =["""input_ids""", """attention_mask"""]
def __init__( self : Dict , UpperCamelCase : Any , UpperCamelCase : Optional[Any] , UpperCamelCase : Any="replace" , UpperCamelCase : int="<s>" , UpperCamelCase : Union[str, Any]="</s>" , UpperCamelCase : List[Any]="</s>" , UpperCamelCase : Optional[int]="<s>" , UpperCamelCase : Any="<unk>" , UpperCamelCase : Optional[int]="<pad>" , UpperCamelCase : Any="<mask>" , UpperCamelCase : Any=False , **UpperCamelCase : int , ):
'''simple docstring'''
_snake_case : Any = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else bos_token
_snake_case : str = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else eos_token
_snake_case : Union[str, Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else sep_token
_snake_case : int = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else cls_token
_snake_case : Union[str, Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else unk_token
_snake_case : Tuple = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case : int = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else mask_token
super().__init__(
errors=UpperCamelCase , bos_token=UpperCamelCase , eos_token=UpperCamelCase , unk_token=UpperCamelCase , sep_token=UpperCamelCase , cls_token=UpperCamelCase , pad_token=UpperCamelCase , mask_token=UpperCamelCase , add_prefix_space=UpperCamelCase , **UpperCamelCase , )
with open(UpperCamelCase , encoding='utf-8' ) as vocab_handle:
_snake_case : str = json.load(UpperCamelCase )
_snake_case : str = {v: k for k, v in self.encoder.items()}
_snake_case : str = errors # how to handle errors in decoding
_snake_case : str = bytes_to_unicode()
_snake_case : List[str] = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCamelCase , encoding='utf-8' ) as merges_handle:
_snake_case : int = merges_handle.read().split('\n' )[1:-1]
_snake_case : Optional[Any] = [tuple(merge.split() ) for merge in bpe_merges]
_snake_case : int = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
_snake_case : int = {}
_snake_case : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_snake_case : Tuple = 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 : str ):
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase_ ( self : Optional[int] , UpperCamelCase : str ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
_snake_case : Tuple = tuple(UpperCamelCase )
_snake_case : int = get_pairs(UpperCamelCase )
if not pairs:
return token
while True:
_snake_case : Any = min(UpperCamelCase , key=lambda UpperCamelCase : self.bpe_ranks.get(UpperCamelCase , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
_snake_case : str = bigram
_snake_case : str = []
_snake_case : Optional[Any] = 0
while i < len(UpperCamelCase ):
try:
_snake_case : Dict = word.index(UpperCamelCase , UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_snake_case : Dict = j
if word[i] == first and i < len(UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_snake_case : str = tuple(UpperCamelCase )
_snake_case : List[Any] = new_word
if len(UpperCamelCase ) == 1:
break
else:
_snake_case : Dict = get_pairs(UpperCamelCase )
_snake_case : List[Any] = ' '.join(UpperCamelCase )
_snake_case : Tuple = word
return word
def UpperCamelCase_ ( self : Optional[int] , UpperCamelCase : List[str] ):
'''simple docstring'''
_snake_case : Optional[Any] = []
for token in re.findall(self.pat , UpperCamelCase ):
_snake_case : Dict = ''.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(UpperCamelCase ).split(' ' ) )
return bpe_tokens
def UpperCamelCase_ ( self : Optional[int] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
return self.encoder.get(UpperCamelCase , self.encoder.get(self.unk_token ) )
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : Tuple ):
'''simple docstring'''
return self.decoder.get(UpperCamelCase )
def UpperCamelCase_ ( self : Optional[Any] , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
_snake_case : List[Any] = ''.join(UpperCamelCase )
_snake_case : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCamelCase_ ( self : Any , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(UpperCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_snake_case : Optional[int] = os.path.join(
UpperCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_snake_case : Optional[Any] = os.path.join(
UpperCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(UpperCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCamelCase , ensure_ascii=UpperCamelCase ) + '\n' )
_snake_case : Dict = 0
with open(UpperCamelCase , '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 UpperCamelCase : 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!' )
_snake_case : Optional[int] = token_index
writer.write(' '.join(UpperCamelCase ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCamelCase_ ( self : List[str] , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_snake_case : List[Any] = [self.cls_token_id]
_snake_case : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase , token_ids_a=UpperCamelCase , already_has_special_tokens=UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase )) + [1]
return [1] + ([0] * len(UpperCamelCase )) + [1, 1] + ([0] * len(UpperCamelCase )) + [1]
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
_snake_case : int = [self.sep_token_id]
_snake_case : Dict = [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 : Any , UpperCamelCase : List[str] , UpperCamelCase : Dict=False , **UpperCamelCase : Dict ):
'''simple docstring'''
_snake_case : Union[str, Any] = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase ) > 0 and not text[0].isspace()):
_snake_case : Optional[Any] = ' ' + text
return (text, kwargs)
| 356 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] , lowerCAmelCase: str , lowerCAmelCase: str , lowerCAmelCase: Path , lowerCAmelCase: str = None , lowerCAmelCase: str = None , lowerCAmelCase: str = None , )-> List[Any]:
if config_name_or_path is None:
_snake_case : int = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
_snake_case : Optional[int] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
_snake_case : List[str] = question_encoder_name_or_path
_snake_case : List[str] = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
_snake_case : Any = RagConfig.from_pretrained(lowerCAmelCase )
_snake_case : Tuple = AutoConfig.from_pretrained(lowerCAmelCase )
_snake_case : Any = AutoConfig.from_pretrained(lowerCAmelCase )
_snake_case : int = gen_config
_snake_case : Tuple = question_encoder_config
_snake_case : int = model_class.from_pretrained_question_encoder_generator(
lowerCAmelCase , lowerCAmelCase , config=lowerCAmelCase )
rag_model.save_pretrained(lowerCAmelCase )
# Sanity check.
model_class.from_pretrained(lowerCAmelCase )
# Save tokenizers.
_snake_case : int = AutoTokenizer.from_pretrained(lowerCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
_snake_case : str = AutoTokenizer.from_pretrained(lowerCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 260 | 0 |
"""simple docstring"""
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase , lowercase , lowercase ):
_lowerCamelCase : Any = None
_lowerCamelCase : Dict = None
_lowerCamelCase : List[str] = graph
self._normalize_graph(lowercase , lowercase )
_lowerCamelCase : Optional[Any] = len(lowercase )
_lowerCamelCase : Optional[Any] = None
def A_ ( self , lowercase , lowercase ):
if sources is int:
_lowerCamelCase : List[str] = [sources]
if sinks is int:
_lowerCamelCase : Dict = [sinks]
if len(lowercase ) == 0 or len(lowercase ) == 0:
return
_lowerCamelCase : Union[str, Any] = sources[0]
_lowerCamelCase : Tuple = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(lowercase ) > 1 or len(lowercase ) > 1:
_lowerCamelCase : Tuple = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_lowerCamelCase : Tuple = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_lowerCamelCase : List[Any] = max_input_flow
_lowerCamelCase : Any = 0
_lowerCamelCase : Tuple = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_lowerCamelCase : int = max_input_flow
_lowerCamelCase : List[str] = size - 1
def A_ ( self ):
if self.maximum_flow_algorithm is None:
raise Exception('You need to set maximum flow algorithm before.' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def A_ ( self , lowercase ):
_lowerCamelCase : List[Any] = algorithm(self )
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = flow_network
_lowerCamelCase : Optional[int] = flow_network.verticesCount
_lowerCamelCase : Tuple = flow_network.sourceIndex
_lowerCamelCase : int = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_lowerCamelCase : Dict = flow_network.graph
_lowerCamelCase : Tuple = False
def A_ ( self ):
if not self.executed:
self._algorithm()
_lowerCamelCase : Any = True
def A_ ( self ):
pass
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase ):
super().__init__(lowercase )
# use this to save your result
_lowerCamelCase : str = -1
def A_ ( self ):
if not self.executed:
raise Exception('You should execute algorithm before using its result!' )
return self.maximum_flow
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase ):
super().__init__(lowercase )
_lowerCamelCase : Dict = [[0] * self.verticies_count for i in range(self.verticies_count )]
_lowerCamelCase : List[str] = [0] * self.verticies_count
_lowerCamelCase : int = [0] * self.verticies_count
def A_ ( self ):
_lowerCamelCase : List[str] = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_lowerCamelCase : int = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_lowerCamelCase : int = 0
while i < len(lowercase ):
_lowerCamelCase : Any = vertices_list[i]
_lowerCamelCase : Union[str, Any] = self.heights[vertex_index]
self.process_vertex(lowercase )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(lowercase ) )
_lowerCamelCase : List[Any] = 0
else:
i += 1
_lowerCamelCase : Any = sum(self.preflow[self.source_index] )
def A_ ( self , lowercase ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(lowercase , lowercase )
self.relabel(lowercase )
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Tuple = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def A_ ( self , lowercase ):
_lowerCamelCase : Dict = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_lowerCamelCase : Any = self.heights[to_index]
if min_height is not None:
_lowerCamelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
lowercase__ = [0]
lowercase__ = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
lowercase__ = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
lowercase__ = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
lowercase__ = flow_network.find_maximum_flow()
print(F"maximum flow is {maximum_flow}") | 96 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Tuple = f'''{sampling_rate}'''
_lowerCamelCase : str = '1'
_lowerCamelCase : str = 'f32le'
_lowerCamelCase : Union[str, Any] = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
_lowerCamelCase : str = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
_lowerCamelCase : List[Any] = output_stream[0]
_lowerCamelCase : Tuple = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
_lowerCamelCase : Optional[Any] = f'''{sampling_rate}'''
_lowerCamelCase : List[str] = '1'
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
_lowerCamelCase : Dict = platform.system()
if system == "Linux":
_lowerCamelCase : Optional[int] = 'alsa'
_lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
_lowerCamelCase : Optional[int] = 'avfoundation'
_lowerCamelCase : Any = ':0'
elif system == "Windows":
_lowerCamelCase : Tuple = 'dshow'
_lowerCamelCase : Tuple = 'default'
_lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
_lowerCamelCase : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_lowerCamelCase : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
_lowerCamelCase : int = stream_chunk_s
else:
_lowerCamelCase : Optional[Any] = chunk_length_s
_lowerCamelCase : Optional[Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
_lowerCamelCase : List[str] = np.intaa
_lowerCamelCase : str = 2
elif format_for_conversion == "f32le":
_lowerCamelCase : Any = np.floataa
_lowerCamelCase : List[Any] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
_lowerCamelCase : Union[str, Any] = chunk_length_s / 6
_lowerCamelCase : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
_lowerCamelCase : Any = [stride_length_s, stride_length_s]
_lowerCamelCase : Tuple = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_lowerCamelCase : List[Any] = datetime.datetime.now()
_lowerCamelCase : Optional[int] = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
_lowerCamelCase : List[Any] = np.frombuffer(item['raw'] , dtype=lowercase__ )
_lowerCamelCase : int = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
_lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
_lowerCamelCase : int = B''
_lowerCamelCase, _lowerCamelCase : Dict = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}''' )
_lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
_lowerCamelCase : Optional[int] = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
_lowerCamelCase : str = (_stride_left, stride_right)
_lowerCamelCase : str = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
_lowerCamelCase : List[Any] = False
yield item
_lowerCamelCase : Optional[Any] = stride_left
_lowerCamelCase : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
_lowerCamelCase : Optional[Any] = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
_lowerCamelCase : Tuple = False
yield item
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
_lowerCamelCase : Optional[Any] = ffmpeg_process.stdout.read(lowercase__ )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to stream audio files from filename' ) from error | 96 | 1 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_A : List[Any] =logging.getLogger(__name__)
_A : int ='''Hello world! cécé herlolip'''
_A : Any =namedtuple(
'''BertAbsConfig''',
[
'''temp_dir''',
'''large''',
'''use_bert_emb''',
'''finetune_bert''',
'''encoder''',
'''share_emb''',
'''max_pos''',
'''enc_layers''',
'''enc_hidden_size''',
'''enc_heads''',
'''enc_ff_size''',
'''enc_dropout''',
'''dec_layers''',
'''dec_hidden_size''',
'''dec_heads''',
'''dec_ff_size''',
'''dec_dropout''',
],
)
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCamelCase__ : List[Any] = BertAbsConfig(
temp_dir=""".""" , finetune_bert=UpperCamelCase , large=UpperCamelCase , share_emb=UpperCamelCase , use_bert_emb=UpperCamelCase , encoder="""bert""" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
lowerCamelCase__ : Tuple = torch.load(UpperCamelCase , lambda UpperCamelCase , UpperCamelCase : storage )
lowerCamelCase__ : Optional[Any] = AbsSummarizer(UpperCamelCase , torch.device("""cpu""" ) , UpperCamelCase )
original.eval()
lowerCamelCase__ : Dict = BertAbsSummarizer(UpperCamelCase , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
lowerCamelCase__ : str = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
lowerCamelCase__ : Any = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase )) )
lowerCamelCase__ : Dict = torch.tensor(UpperCamelCase ).unsqueeze(0 )
lowerCamelCase__ : List[str] = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase )) )
lowerCamelCase__ : str = torch.tensor(UpperCamelCase ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
lowerCamelCase__ : List[Any] = encoder_input_ids
lowerCamelCase__ : Dict = decoder_input_ids
lowerCamelCase__ : Union[str, Any] = None
lowerCamelCase__ : int = None
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Union[str, Any] = None
lowerCamelCase__ : str = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
lowerCamelCase__ : Dict = original(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )[0]
lowerCamelCase__ : Any = original.generator(UpperCamelCase )
lowerCamelCase__ : List[Any] = new_model(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )[0]
lowerCamelCase__ : int = new_model.generator(UpperCamelCase )
lowerCamelCase__ : Optional[Any] = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase ) )
lowerCamelCase__ : int = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase ) )
lowerCamelCase__ : List[Any] = torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
_A : Dict =argparse.ArgumentParser()
parser.add_argument(
'''--bertabs_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
_A : int =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 129 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_A : List[Any] =True
except ImportError:
_A : int =False
_A : Union[str, Any] =logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Tuple:
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _lowercase ( _lowercase ):
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__: ArgumentParser ):
lowerCamelCase__ : List[str] = parser.add_parser("""add-new-model""" )
add_new_model_parser.add_argument("""--testing""" , action="""store_true""" , help="""If in testing mode.""" )
add_new_model_parser.add_argument("""--testing_file""" , type=UpperCamelCase__ , help="""Configuration file on which to run.""" )
add_new_model_parser.add_argument(
"""--path""" , type=UpperCamelCase__ , help="""Path to cookiecutter. Should only be used for testing purposes.""" )
add_new_model_parser.set_defaults(func=UpperCamelCase__ )
def __init__( self: Optional[int] , UpperCamelCase__: bool , UpperCamelCase__: str , UpperCamelCase__: str=None , *UpperCamelCase__: Optional[int] ):
lowerCamelCase__ : List[Any] = testing
lowerCamelCase__ : Tuple = testing_file
lowerCamelCase__ : int = path
def lowerCamelCase_ ( self: int ):
warnings.warn(
"""The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
"""It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
"""checks, you should use `transformers-cli add-new-model-like` instead.""" )
if not _has_cookiecutter:
raise ImportError(
"""Model creation dependencies are required to use the `add_new_model` command. Install them by running """
"""the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n""" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowerCamelCase__ : List[Any] = [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
if len(UpperCamelCase__ ) > 0:
raise ValueError(
"""Several directories starting with `cookiecutter-template-` in current working directory. """
"""Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
"""change your working directory.""" )
lowerCamelCase__ : int = (
Path(UpperCamelCase__ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
lowerCamelCase__ : int = path_to_transformer_root / """templates""" / """adding_a_new_model"""
# Execute cookiecutter
if not self._testing:
cookiecutter(str(UpperCamelCase__ ) )
else:
with open(self._testing_file , """r""" ) as configuration_file:
lowerCamelCase__ : List[str] = json.load(UpperCamelCase__ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=UpperCamelCase__ , extra_context=UpperCamelCase__ , )
lowerCamelCase__ : Optional[Any] = [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]
# Retrieve configuration
with open(directory + """/configuration.json""" , """r""" ) as configuration_file:
lowerCamelCase__ : int = json.load(UpperCamelCase__ )
lowerCamelCase__ : Tuple = configuration["""lowercase_modelname"""]
lowerCamelCase__ : int = configuration["""generate_tensorflow_pytorch_and_flax"""]
os.remove(F'''{directory}/configuration.json''' )
lowerCamelCase__ : Union[str, Any] = """PyTorch""" in generate_tensorflow_pytorch_and_flax
lowerCamelCase__ : Union[str, Any] = """TensorFlow""" in generate_tensorflow_pytorch_and_flax
lowerCamelCase__ : Tuple = """Flax""" in generate_tensorflow_pytorch_and_flax
lowerCamelCase__ : List[str] = F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}''' , exist_ok=UpperCamelCase__ )
# Tests require submodules as they have parent imports
with open(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py''' , """w""" ):
pass
shutil.move(
F'''{directory}/__init__.py''' , F'''{model_dir}/__init__.py''' , )
shutil.move(
F'''{directory}/configuration_{lowercase_model_name}.py''' , F'''{model_dir}/configuration_{lowercase_model_name}.py''' , )
def remove_copy_lines(UpperCamelCase__: Optional[int] ):
with open(UpperCamelCase__ , """r""" ) as f:
lowerCamelCase__ : Union[str, Any] = f.readlines()
with open(UpperCamelCase__ , """w""" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(UpperCamelCase__ )
if output_pytorch:
if not self._testing:
remove_copy_lines(F'''{directory}/modeling_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/modeling_{lowercase_model_name}.py''' , F'''{model_dir}/modeling_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/test_modeling_{lowercase_model_name}.py''' , F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py''' , )
else:
os.remove(F'''{directory}/modeling_{lowercase_model_name}.py''' )
os.remove(F'''{directory}/test_modeling_{lowercase_model_name}.py''' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(F'''{directory}/modeling_tf_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/modeling_tf_{lowercase_model_name}.py''' , F'''{model_dir}/modeling_tf_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/test_modeling_tf_{lowercase_model_name}.py''' , F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py''' , )
else:
os.remove(F'''{directory}/modeling_tf_{lowercase_model_name}.py''' )
os.remove(F'''{directory}/test_modeling_tf_{lowercase_model_name}.py''' )
if output_flax:
if not self._testing:
remove_copy_lines(F'''{directory}/modeling_flax_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/modeling_flax_{lowercase_model_name}.py''' , F'''{model_dir}/modeling_flax_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/test_modeling_flax_{lowercase_model_name}.py''' , F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py''' , )
else:
os.remove(F'''{directory}/modeling_flax_{lowercase_model_name}.py''' )
os.remove(F'''{directory}/test_modeling_flax_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/{lowercase_model_name}.md''' , F'''{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md''' , )
shutil.move(
F'''{directory}/tokenization_{lowercase_model_name}.py''' , F'''{model_dir}/tokenization_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/tokenization_fast_{lowercase_model_name}.py''' , F'''{model_dir}/tokenization_{lowercase_model_name}_fast.py''' , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(UpperCamelCase__: str , UpperCamelCase__: str , UpperCamelCase__: List[str] ):
# Create temp file
lowerCamelCase__ , lowerCamelCase__ : Any = mkstemp()
lowerCamelCase__ : Tuple = False
with fdopen(UpperCamelCase__ , """w""" ) as new_file:
with open(UpperCamelCase__ ) as old_file:
for line in old_file:
new_file.write(UpperCamelCase__ )
if line_to_copy_below in line:
lowerCamelCase__ : int = True
for line_to_copy in lines_to_copy:
new_file.write(UpperCamelCase__ )
if not line_found:
raise ValueError(F'''Line {line_to_copy_below} was not found in file.''' )
# Copy the file permissions from the old file to the new file
copymode(UpperCamelCase__ , UpperCamelCase__ )
# Remove original file
remove(UpperCamelCase__ )
# Move new file
move(UpperCamelCase__ , UpperCamelCase__ )
def skip_units(UpperCamelCase__: Optional[int] ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(UpperCamelCase__: List[str] ):
with open(UpperCamelCase__ ) as datafile:
lowerCamelCase__ : int = []
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : int = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowerCamelCase__ : List[str] = line.split("""\"""" )[1]
lowerCamelCase__ : List[str] = skip_units(UpperCamelCase__ )
elif "# Below: " in line and "##" not in line:
lowerCamelCase__ : List[Any] = line.split("""\"""" )[1]
lowerCamelCase__ : Union[str, Any] = skip_units(UpperCamelCase__ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = []
elif "# Replace with" in line and "##" not in line:
lowerCamelCase__ : str = []
elif "##" not in line:
lines_to_copy.append(UpperCamelCase__ )
remove(UpperCamelCase__ )
replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py''' )
os.rmdir(UpperCamelCase__ )
| 129 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowercase : str = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowercase : Optional[Any] = 'UperNetConfig'
class A ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = 1 , ) -> None:
"""simple docstring"""
super().__init__()
A : Optional[int] = nn.Convad(
in_channels=SCREAMING_SNAKE_CASE , out_channels=SCREAMING_SNAKE_CASE , kernel_size=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , bias=SCREAMING_SNAKE_CASE , dilation=SCREAMING_SNAKE_CASE , )
A : List[Any] = nn.BatchNormad(SCREAMING_SNAKE_CASE )
A : List[str] = nn.ReLU()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> torch.Tensor:
"""simple docstring"""
A : Tuple = self.conv(SCREAMING_SNAKE_CASE )
A : Optional[int] = self.batch_norm(SCREAMING_SNAKE_CASE )
A : Optional[Any] = self.activation(SCREAMING_SNAKE_CASE )
return output
class A ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
super().__init__()
A : str = [
nn.AdaptiveAvgPoolad(SCREAMING_SNAKE_CASE ),
UperNetConvModule(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> torch.Tensor:
"""simple docstring"""
A : Any = input
for layer in self.layers:
A : Union[str, Any] = layer(SCREAMING_SNAKE_CASE )
return hidden_state
class A ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
super().__init__()
A : Dict = pool_scales
A : Union[str, Any] = align_corners
A : int = in_channels
A : List[str] = channels
A : List[Any] = []
for i, pool_scale in enumerate(SCREAMING_SNAKE_CASE ):
A : str = UperNetPyramidPoolingBlock(pool_scale=SCREAMING_SNAKE_CASE , in_channels=SCREAMING_SNAKE_CASE , channels=SCREAMING_SNAKE_CASE )
self.blocks.append(SCREAMING_SNAKE_CASE )
self.add_module(str(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[torch.Tensor]:
"""simple docstring"""
A : int = []
for ppm in self.blocks:
A : Tuple = ppm(SCREAMING_SNAKE_CASE )
A : List[Any] = nn.functional.interpolate(
SCREAMING_SNAKE_CASE , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(SCREAMING_SNAKE_CASE )
return ppm_outs
class A ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
A : List[str] = config
A : Union[str, Any] = config.pool_scales # e.g. (1, 2, 3, 6)
A : Optional[int] = in_channels
A : List[str] = config.hidden_size
A : str = False
A : str = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
A : Optional[int] = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
A : Union[str, Any] = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
A : Dict = nn.ModuleList()
A : List[str] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
A : int = UperNetConvModule(SCREAMING_SNAKE_CASE , self.channels , kernel_size=1 )
A : Union[str, Any] = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(SCREAMING_SNAKE_CASE )
self.fpn_convs.append(SCREAMING_SNAKE_CASE )
A : Dict = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self.apply(self._init_weights )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
A : Union[str, Any] = inputs[-1]
A : Optional[int] = [x]
psp_outs.extend(self.psp_modules(SCREAMING_SNAKE_CASE ) )
A : str = torch.cat(SCREAMING_SNAKE_CASE , dim=1 )
A : str = self.bottleneck(SCREAMING_SNAKE_CASE )
return output
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> torch.Tensor:
"""simple docstring"""
A : Union[str, Any] = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(SCREAMING_SNAKE_CASE ) )
# build top-down path
A : int = len(SCREAMING_SNAKE_CASE )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
A : Union[str, Any] = laterals[i - 1].shape[2:]
A : Optional[Any] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=SCREAMING_SNAKE_CASE , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
A : List[str] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
A : Any = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
A : Tuple = torch.cat(SCREAMING_SNAKE_CASE , dim=1 )
A : Any = self.fpn_bottleneck(SCREAMING_SNAKE_CASE )
A : Optional[int] = self.classifier(SCREAMING_SNAKE_CASE )
return output
class A ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 3 , SCREAMING_SNAKE_CASE = 1 ) -> None:
"""simple docstring"""
super().__init__()
A : List[Any] = config
A : Optional[Any] = config.auxiliary_in_channels
A : Optional[Any] = config.auxiliary_channels
A : Union[str, Any] = config.auxiliary_num_convs
A : List[str] = config.auxiliary_concat_input
A : Optional[Any] = in_index
A : str = (kernel_size // 2) * dilation
A : Tuple = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , dilation=SCREAMING_SNAKE_CASE ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , dilation=SCREAMING_SNAKE_CASE ) )
if self.num_convs == 0:
A : Optional[Any] = nn.Identity()
else:
A : Tuple = nn.Sequential(*SCREAMING_SNAKE_CASE )
if self.concat_input:
A : Any = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=SCREAMING_SNAKE_CASE , padding=kernel_size // 2 )
A : Optional[Any] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
self.apply(self._init_weights )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> torch.Tensor:
"""simple docstring"""
A : Union[str, Any] = encoder_hidden_states[self.in_index]
A : Optional[Any] = self.convs(SCREAMING_SNAKE_CASE )
if self.concat_input:
A : Optional[Any] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
A : str = self.classifier(SCREAMING_SNAKE_CASE )
return output
class A ( __snake_case ):
__magic_name__ = UperNetConfig
__magic_name__ = '''pixel_values'''
__magic_name__ = True
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> List[str]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : int = value
lowercase : List[str] = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowercase : Union[str, Any] = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , __snake_case , )
class A ( __snake_case ):
def __init__( self , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
super().__init__(SCREAMING_SNAKE_CASE )
A : List[Any] = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
A : List[Any] = UperNetHead(SCREAMING_SNAKE_CASE , in_channels=self.backbone.channels )
A : str = UperNetFCNHead(SCREAMING_SNAKE_CASE ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , ) -> Union[tuple, SemanticSegmenterOutput]:
"""simple docstring"""
A : List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
A : Optional[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
A : Any = output_attentions if output_attentions is not None else self.config.output_attentions
A : Optional[int] = self.backbone.forward_with_filtered_kwargs(
SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE , output_attentions=SCREAMING_SNAKE_CASE )
A : int = outputs.feature_maps
A : Any = self.decode_head(SCREAMING_SNAKE_CASE )
A : str = nn.functional.interpolate(SCREAMING_SNAKE_CASE , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=SCREAMING_SNAKE_CASE )
A : List[Any] = None
if self.auxiliary_head is not None:
A : List[str] = self.auxiliary_head(SCREAMING_SNAKE_CASE )
A : Any = nn.functional.interpolate(
SCREAMING_SNAKE_CASE , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=SCREAMING_SNAKE_CASE )
A : Optional[Any] = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
A : Optional[int] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
A : str = loss_fct(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : List[str] = loss_fct(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Optional[int] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
A : Optional[Any] = (logits,) + outputs[1:]
else:
A : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=SCREAMING_SNAKE_CASE , logits=SCREAMING_SNAKE_CASE , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 3 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_A = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class lowerCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = XLNetTokenizer
SCREAMING_SNAKE_CASE = XLNetTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
def _a (self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase__ : List[str] = XLNetTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : str = """<s>"""
UpperCAmelCase__ : str = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """<eod>""" )
self.assertEqual(len(_lowerCamelCase ) , 1006 )
def _a (self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = XLNetTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
UpperCAmelCase__ : List[Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowerCamelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [285, 46, 10, 170, 382] )
UpperCAmelCase__ : Tuple = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCamelCase , [
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""",
""".""",
] , )
UpperCAmelCase__ : List[Any] = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
UpperCAmelCase__ : int = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
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 _a (self ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = XLNetTokenizer(_lowerCamelCase , do_lower_case=_lowerCamelCase )
UpperCAmelCase__ : str = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCamelCase , [
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""",
"""se""",
""".""",
] , )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""▁he""", """ll""", """o"""] )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = XLNetTokenizer(_lowerCamelCase , do_lower_case=_lowerCamelCase )
UpperCAmelCase__ : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCamelCase , [
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""",
"""se""",
""".""",
] , )
@slow
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Dict = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
UpperCAmelCase__ : int = tokenizer.encode("""sequence builders""" , add_special_tokens=_lowerCamelCase )
UpperCAmelCase__ : int = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_lowerCamelCase )
UpperCAmelCase__ : str = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
UpperCAmelCase__ : Optional[int] = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = {"""input_ids""": [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], """token_type_ids""": [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """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], [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, 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, 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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name="""xlnet-base-cased""" , revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" , )
| 171 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowercase__ = logging.get_logger(__name__)
class __snake_case ( lowercase_ ):
def __init__( self , *lowercase , **lowercase) -> None:
'''simple docstring'''
warnings.warn(
'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use PerceiverImageProcessor instead.' , a__ , )
super().__init__(*a__ , **a__)
| 354 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if n == 0:
return 0
a__: List[Any] = float('-inf' )
for i in range(1 , n + 1 ):
a__: Optional[Any] = max(
_SCREAMING_SNAKE_CASE , prices[i - 1] + naive_cut_rod_recursive(n - i , _SCREAMING_SNAKE_CASE ) )
return max_revue
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: str = [float('-inf' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
a__: Dict = float('-inf' )
for i in range(1 , n + 1 ):
a__: Optional[Any] = max(
_SCREAMING_SNAKE_CASE , prices[i - 1] + _top_down_cut_rod_recursive(n - i , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , )
a__: Optional[int] = max_revenue
return max_rev[n]
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
a__: str = [float('-inf' ) for _ in range(n + 1 )]
a__: Tuple = 0
for i in range(1 , n + 1 ):
a__: List[str] = max_rev[i]
for j in range(1 , i + 1 ):
a__: Tuple = max(_SCREAMING_SNAKE_CASE , prices[j - 1] + max_rev[i - j] )
a__: Union[str, Any] = max_revenue_i
return max_rev[n]
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Any:
if n < 0:
a__: Optional[int] = F'n must be greater than or equal to 0. Got n = {n}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if n > len(_SCREAMING_SNAKE_CASE ):
a__: List[str] = (
'Each integral piece of rod must have a corresponding price. '
F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE )}'
)
raise ValueError(_SCREAMING_SNAKE_CASE )
def __a ( ) ->str:
a__: int = [6, 10, 12, 15, 20, 23]
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
a__: Any = 36
a__: Optional[int] = top_down_cut_rod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: List[Any] = bottom_up_cut_rod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: int = naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 203 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
lowercase : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase : Optional[int] = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Union[PIL.Image.Image, np.ndarray]
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(
prior=lowercase , image_encoder=lowercase , image_processor=lowercase , scheduler=lowercase , renderer=lowercase , )
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
if latents is None:
a__ : List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}')
a__ : List[str] = latents.to(lowercase)
a__ : Dict = latents * scheduler.init_noise_sigma
return latents
def __lowercase ( self , lowercase=0) -> Optional[int]:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`')
a__ : Any = torch.device(F'cuda:{gpu_id}')
a__ : Optional[Any] = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase , lowercase)
@property
def __lowercase ( self) -> Any:
'''simple docstring'''
if self.device != torch.device('meta') or not hasattr(self.image_encoder , '_hf_hook'):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowercase , '_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
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , ) -> Any:
'''simple docstring'''
if isinstance(lowercase , lowercase) and isinstance(image[0] , torch.Tensor):
a__ : Dict = torch.cat(lowercase , axis=0) if image[0].ndim == 4 else torch.stack(lowercase , axis=0)
if not isinstance(lowercase , torch.Tensor):
a__ : List[Any] = self.image_processor(lowercase , return_tensors='pt').pixel_values[0].unsqueeze(0)
a__ : Optional[Any] = image.to(dtype=self.image_encoder.dtype , device=lowercase)
a__ : int = self.image_encoder(lowercase)['last_hidden_state']
a__ : Optional[Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
a__ : Optional[int] = image_embeds.repeat_interleave(lowercase , dim=0)
if do_classifier_free_guidance:
a__ : Tuple = torch.zeros_like(lowercase)
# 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
a__ : int = torch.cat([negative_image_embeds, image_embeds])
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowercase)
def __call__( self , lowercase , lowercase = 1 , lowercase = 25 , lowercase = None , lowercase = None , lowercase = 4.0 , lowercase = 64 , lowercase = "pil" , lowercase = True , ) -> Tuple:
'''simple docstring'''
if isinstance(lowercase , PIL.Image.Image):
a__ : List[str] = 1
elif isinstance(lowercase , torch.Tensor):
a__ : List[str] = image.shape[0]
elif isinstance(lowercase , lowercase) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image)):
a__ : List[str] = len(lowercase)
else:
raise ValueError(
F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase)}')
a__ : Tuple = self._execution_device
a__ : List[Any] = batch_size * num_images_per_prompt
a__ : Optional[Any] = guidance_scale > 1.0
a__ : Optional[int] = self._encode_image(lowercase , lowercase , lowercase , lowercase)
# prior
self.scheduler.set_timesteps(lowercase , device=lowercase)
a__ : str = self.scheduler.timesteps
a__ : Tuple = self.prior.config.num_embeddings
a__ : Optional[int] = self.prior.config.embedding_dim
a__ : Dict = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowercase , lowercase , lowercase , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
a__ : Tuple = latents.reshape(latents.shape[0] , lowercase , lowercase)
for i, t in enumerate(self.progress_bar(lowercase)):
# expand the latents if we are doing classifier free guidance
a__ : List[Any] = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
a__ : Optional[int] = self.scheduler.scale_model_input(lowercase , lowercase)
a__ : Tuple = self.prior(
lowercase , timestep=lowercase , proj_embedding=lowercase , ).predicted_image_embedding
# remove the variance
a__ , a__ : Any = noise_pred.split(
scaled_model_input.shape[2] , dim=2) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
a__ , a__ : Any = noise_pred.chunk(2)
a__ : str = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
a__ : List[str] = self.scheduler.step(
lowercase , timestep=lowercase , sample=lowercase , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowercase)
a__ : List[Any] = []
for i, latent in enumerate(lowercase):
print()
a__ : Dict = self.renderer.decode(
latent[None, :] , lowercase , size=lowercase , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(lowercase)
a__ : Union[str, Any] = torch.stack(lowercase)
if output_type not in ["np", "pil"]:
raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}')
a__ : List[Any] = images.cpu().numpy()
if output_type == "pil":
a__ : Any = [self.numpy_to_pil(lowercase) for image in images]
# Offload last model to CPU
if hasattr(self , 'final_offload_hook') and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowercase)
| 99 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
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,
)
UpperCAmelCase__ : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase__ : Dict = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to("cuda")\n\n >>> prompt = "A red cartoon frog, 4k"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n ... )\n >>> pipe.to("cuda")\n\n >>> init_image = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/frog.png"\n ... )\n\n >>> image = pipe(\n ... image=init_image,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... strength=0.2,\n ... ).images\n\n >>> image[0].save("red_frog.png")\n ```\n'
def lowerCamelCase__ ( a , a , a=8 ) -> List[Any]:
_A: int = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_A: str = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def lowerCamelCase__ ( a , a=5_12 , a=5_12 ) -> Dict:
_A: Union[str, Any] = pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1 )
_A: Tuple = np.array(pil_image.convert('''RGB''' ) )
_A: List[str] = arr.astype(np.floataa ) / 127.5 - 1
_A: Tuple = np.transpose(a , [2, 0, 1] )
_A: Any = torch.from_numpy(a ).unsqueeze(0 )
return image
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self : int , lowerCAmelCase_ : UNetaDConditionModel , lowerCAmelCase_ : DDPMScheduler , lowerCAmelCase_ : VQModel , ):
"""simple docstring"""
super().__init__()
self.register_modules(
unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ , movq=lowerCAmelCase_ , )
_A: List[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __magic_name__ ( self : List[str] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
# get the original timestep using init_timestep
_A: Union[str, Any] = min(int(num_inference_steps * strength ) , lowerCAmelCase_ )
_A: str = max(num_inference_steps - init_timestep , 0 )
_A: str = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __magic_name__ ( self : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int]=None ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase_ )}""" )
_A: Optional[int] = image.to(device=lowerCAmelCase_ , dtype=lowerCAmelCase_ )
_A: Union[str, Any] = batch_size * num_images_per_prompt
if image.shape[1] == 4:
_A: Optional[int] = image
else:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and len(lowerCAmelCase_ ) != batch_size:
raise ValueError(
F"""You have passed a list of generators of length {len(lowerCAmelCase_ )}, but requested an effective batch"""
F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_A: List[Any] = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCAmelCase_ )
]
_A: Optional[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
else:
_A: Optional[int] = self.movq.encode(lowerCAmelCase_ ).latent_dist.sample(lowerCAmelCase_ )
_A: int = self.movq.config.scaling_factor * init_latents
_A: Optional[Any] = torch.cat([init_latents] , dim=0 )
_A: Any = init_latents.shape
_A: Optional[Any] = randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ , dtype=lowerCAmelCase_ )
# get latents
_A: Union[str, Any] = self.scheduler.add_noise(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_A: List[str] = init_latents
return latents
def __magic_name__ ( self : Optional[int] , lowerCAmelCase_ : Optional[int]=0 ):
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
_A: Any = torch.device(F"""cuda:{gpu_id}""" )
_A: int = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase_ , lowerCAmelCase_ )
def __magic_name__ ( self : Any , lowerCAmelCase_ : Any=0 ):
"""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.''' )
_A: Any = torch.device(F"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=lowerCAmelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_A: int = None
for cpu_offloaded_model in [self.unet, self.movq]:
_A , _A: List[Any] = cpu_offload_with_hook(lowerCAmelCase_ , lowerCAmelCase_ , prev_module_hook=lowerCAmelCase_ )
# We'll offload the last model manually.
_A: Tuple = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCAmelCase_ , '''_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(lowerCAmelCase_ )
def __call__( self : Optional[Any] , lowerCAmelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCAmelCase_ : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] , lowerCAmelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCAmelCase_ : int = 5_1_2 , lowerCAmelCase_ : int = 5_1_2 , lowerCAmelCase_ : int = 1_0_0 , lowerCAmelCase_ : float = 4.0 , lowerCAmelCase_ : float = 0.3 , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCAmelCase_ : Optional[str] = "pil" , lowerCAmelCase_ : bool = True , ):
"""simple docstring"""
_A: Any = self._execution_device
_A: Any = guidance_scale > 1.0
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_A: Any = torch.cat(lowerCAmelCase_ , dim=0 )
_A: int = image_embeds.shape[0]
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_A: Dict = torch.cat(lowerCAmelCase_ , dim=0 )
if do_classifier_free_guidance:
_A: Any = image_embeds.repeat_interleave(lowerCAmelCase_ , dim=0 )
_A: str = negative_image_embeds.repeat_interleave(lowerCAmelCase_ , dim=0 )
_A: Dict = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCAmelCase_ )
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_A: List[str] = [image]
if not all(isinstance(lowerCAmelCase_ , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
F"""Input is in incorrect format: {[type(lowerCAmelCase_ ) for i in image]}. Currently, we only support PIL image and pytorch tensor""" )
_A: List[str] = torch.cat([prepare_image(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) for i in image] , dim=0 )
_A: Tuple = image.to(dtype=image_embeds.dtype , device=lowerCAmelCase_ )
_A: Optional[Any] = self.movq.encode(lowerCAmelCase_ )['''latents''']
_A: Optional[int] = latents.repeat_interleave(lowerCAmelCase_ , dim=0 )
self.scheduler.set_timesteps(lowerCAmelCase_ , device=lowerCAmelCase_ )
_A , _A: List[Any] = self.get_timesteps(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_A: Dict = timesteps[:1].repeat(batch_size * num_images_per_prompt )
_A , _A: Optional[int] = downscale_height_and_width(lowerCAmelCase_ , lowerCAmelCase_ , self.movq_scale_factor )
_A: Any = self.prepare_latents(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , image_embeds.dtype , lowerCAmelCase_ , lowerCAmelCase_ )
for i, t in enumerate(self.progress_bar(lowerCAmelCase_ ) ):
# expand the latents if we are doing classifier free guidance
_A: Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_A: str = {'''image_embeds''': image_embeds}
_A: Optional[int] = self.unet(
sample=lowerCAmelCase_ , timestep=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , added_cond_kwargs=lowerCAmelCase_ , return_dict=lowerCAmelCase_ , )[0]
if do_classifier_free_guidance:
_A , _A: str = noise_pred.split(latents.shape[1] , dim=1 )
_A , _A: int = noise_pred.chunk(2 )
_A , _A: int = variance_pred.chunk(2 )
_A: Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_A: List[str] = 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"]
):
_A , _A: Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_A: Any = self.scheduler.step(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_ , )[0]
# post-processing
_A: Tuple = self.movq.decode(lowerCAmelCase_ , force_not_quantize=lowerCAmelCase_ )['''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"]:
_A: int = image * 0.5 + 0.5
_A: Any = image.clamp(0 , 1 )
_A: Any = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_A: Union[str, Any] = self.numpy_to_pil(lowerCAmelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase_ )
| 121 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def A_ ( _lowerCAmelCase ) -> List[str]:
return 1.0 / (1.0 + np.exp(-_outputs ))
def A_ ( _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = np.max(_outputs , axis=-1 , keepdims=a_ )
UpperCamelCase : Tuple = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=a_ )
class A__ ( __snake_case ):
_UpperCAmelCase :str = '''sigmoid'''
_UpperCAmelCase :Optional[Any] = '''softmax'''
_UpperCAmelCase :int = '''none'''
@add_end_docstrings(
__snake_case , r'\n return_all_scores (`bool`, *optional*, defaults to `False`):\n Whether to return all prediction scores or just the one of the predicted class.\n function_to_apply (`str`, *optional*, defaults to `"default"`):\n The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n has several labels, will apply the softmax function on the output.\n - `"sigmoid"`: Applies the sigmoid function on the output.\n - `"softmax"`: Applies the softmax function on the output.\n - `"none"`: Does not apply any function on the output.\n ' , )
class A__ ( __snake_case ):
_UpperCAmelCase :Dict = False
_UpperCAmelCase :int = ClassificationFunction.NONE
def __init__( self , **A_ ):
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def __UpperCamelCase( self , A_=None , A_=None , A_="" , **A_ ):
'''simple docstring'''
UpperCamelCase : Any = tokenizer_kwargs
UpperCamelCase : Any = {}
if hasattr(self.model.config , "return_all_scores" ) and return_all_scores is None:
UpperCamelCase : Any = self.model.config.return_all_scores
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or top_k is None:
UpperCamelCase : int = top_k
UpperCamelCase : Optional[Any] = False
elif return_all_scores is not None:
warnings.warn(
"`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"
" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`." , lowerCAmelCase__ , )
if return_all_scores:
UpperCamelCase : Dict = None
else:
UpperCamelCase : Dict = 1
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCamelCase : List[Any] = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
UpperCamelCase : Dict = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *A_ , **A_ ):
'''simple docstring'''
UpperCamelCase : Any = super().__call__(*lowerCAmelCase__ , **lowerCAmelCase__ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
UpperCamelCase : Union[str, Any] = "top_k" not in kwargs
if isinstance(args[0] , lowerCAmelCase__ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def __UpperCamelCase( self , A_ , **A_ ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.framework
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
return self.tokenizer(**lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and len(lowerCAmelCase__ ) == 1 and isinstance(inputs[0] , lowerCAmelCase__ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair." )
return self.tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return self.model(**lowerCAmelCase__ )
def __UpperCamelCase( self , A_ , A_=None , A_=1 , A_=True ):
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
UpperCamelCase : Tuple = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
UpperCamelCase : Tuple = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , "function_to_apply" ) and function_to_apply is None:
UpperCamelCase : Dict = self.model.config.function_to_apply
else:
UpperCamelCase : Union[str, Any] = ClassificationFunction.NONE
UpperCamelCase : Optional[int] = model_outputs["logits"][0]
UpperCamelCase : Any = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
UpperCamelCase : List[Any] = sigmoid(lowerCAmelCase__ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
UpperCamelCase : str = softmax(lowerCAmelCase__ )
elif function_to_apply == ClassificationFunction.NONE:
UpperCamelCase : Any = outputs
else:
raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
UpperCamelCase : str = [
{"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(lowerCAmelCase__ )
]
if not _legacy:
dict_scores.sort(key=lambda A_ : x["score"] , reverse=lowerCAmelCase__ )
if top_k is not None:
UpperCamelCase : Dict = dict_scores[:top_k]
return dict_scores
| 367 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : str = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
__lowerCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 140 | 0 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = fname.split(os.path.sep )[-1]
return re.search(r"""^(.*)_\d+\.jpg$""" , lowerCamelCase__ ).groups()[0]
class SCREAMING_SNAKE_CASE_ ( _UpperCamelCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None):
__SCREAMING_SNAKE_CASE = file_names
__SCREAMING_SNAKE_CASE = image_transform
__SCREAMING_SNAKE_CASE = label_to_id
def __len__( self):
return len(self.file_names)
def __getitem__( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = self.file_names[idx]
__SCREAMING_SNAKE_CASE = PIL.Image.open(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = raw_image.convert("""RGB""")
if self.image_transform is not None:
__SCREAMING_SNAKE_CASE = self.image_transform(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = extract_label(lowerCAmelCase__)
if self.label_to_id is not None:
__SCREAMING_SNAKE_CASE = self.label_to_id[label]
return {"image": image, "label": label}
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if args.with_tracking:
__SCREAMING_SNAKE_CASE = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="""all""" , project_dir=args.project_dir )
else:
__SCREAMING_SNAKE_CASE = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__SCREAMING_SNAKE_CASE = config["lr"]
__SCREAMING_SNAKE_CASE = int(config["""num_epochs"""] )
__SCREAMING_SNAKE_CASE = int(config["""seed"""] )
__SCREAMING_SNAKE_CASE = int(config["""batch_size"""] )
__SCREAMING_SNAKE_CASE = config["image_size"]
if not isinstance(lowerCamelCase__ , (list, tuple) ):
__SCREAMING_SNAKE_CASE = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , """isdigit""" ):
if args.checkpointing_steps == "epoch":
__SCREAMING_SNAKE_CASE = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
__SCREAMING_SNAKE_CASE = int(args.checkpointing_steps )
else:
raise ValueError(
f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
else:
__SCREAMING_SNAKE_CASE = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
__SCREAMING_SNAKE_CASE = os.path.split(lowerCamelCase__ )[-1].split(""".""" )[0]
accelerator.init_trackers(lowerCamelCase__ , lowerCamelCase__ )
# Grab all the image filenames
__SCREAMING_SNAKE_CASE = [os.path.join(args.data_dir , lowerCamelCase__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(""".jpg""" )]
# Build the label correspondences
__SCREAMING_SNAKE_CASE = [extract_label(lowerCamelCase__ ) for fname in file_names]
__SCREAMING_SNAKE_CASE = list(set(lowerCamelCase__ ) )
id_to_label.sort()
__SCREAMING_SNAKE_CASE = {lbl: i for i, lbl in enumerate(lowerCamelCase__ )}
# Set the seed before splitting the data.
np.random.seed(lowerCamelCase__ )
torch.manual_seed(lowerCamelCase__ )
torch.cuda.manual_seed_all(lowerCamelCase__ )
# Split our filenames between train and validation
__SCREAMING_SNAKE_CASE = np.random.permutation(len(lowerCamelCase__ ) )
__SCREAMING_SNAKE_CASE = int(0.8 * len(lowerCamelCase__ ) )
__SCREAMING_SNAKE_CASE = random_perm[:cut]
__SCREAMING_SNAKE_CASE = random_perm[cut:]
# For training we use a simple RandomResizedCrop
__SCREAMING_SNAKE_CASE = Compose([RandomResizedCrop(lowerCamelCase__ , scale=(0.5, 1.0) ), ToTensor()] )
__SCREAMING_SNAKE_CASE = PetsDataset(
[file_names[i] for i in train_split] , image_transform=lowerCamelCase__ , label_to_id=lowerCamelCase__ )
# For evaluation, we use a deterministic Resize
__SCREAMING_SNAKE_CASE = Compose([Resize(lowerCamelCase__ ), ToTensor()] )
__SCREAMING_SNAKE_CASE = PetsDataset([file_names[i] for i in eval_split] , image_transform=lowerCamelCase__ , label_to_id=lowerCamelCase__ )
# Instantiate dataloaders.
__SCREAMING_SNAKE_CASE = DataLoader(lowerCamelCase__ , shuffle=lowerCamelCase__ , batch_size=lowerCamelCase__ , num_workers=4 )
__SCREAMING_SNAKE_CASE = DataLoader(lowerCamelCase__ , shuffle=lowerCamelCase__ , batch_size=lowerCamelCase__ , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__SCREAMING_SNAKE_CASE = create_model("""resnet50d""" , pretrained=lowerCamelCase__ , num_classes=len(lowerCamelCase__ ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__SCREAMING_SNAKE_CASE = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
__SCREAMING_SNAKE_CASE = False
for param in model.get_classifier().parameters():
__SCREAMING_SNAKE_CASE = True
# We normalize the batches of images to be a bit faster.
__SCREAMING_SNAKE_CASE = torch.tensor(model.default_cfg["""mean"""] )[None, :, None, None].to(accelerator.device )
__SCREAMING_SNAKE_CASE = torch.tensor(model.default_cfg["""std"""] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
__SCREAMING_SNAKE_CASE = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
__SCREAMING_SNAKE_CASE = OneCycleLR(optimizer=lowerCamelCase__ , max_lr=lowerCamelCase__ , epochs=lowerCamelCase__ , steps_per_epoch=len(lowerCamelCase__ ) )
# 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.
__SCREAMING_SNAKE_CASE = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# We need to keep track of how many total steps we have iterated over
__SCREAMING_SNAKE_CASE = 0
# We also need to keep track of the starting epoch so files are named properly
__SCREAMING_SNAKE_CASE = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}" )
accelerator.load_state(args.resume_from_checkpoint )
__SCREAMING_SNAKE_CASE = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
__SCREAMING_SNAKE_CASE = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
__SCREAMING_SNAKE_CASE = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
__SCREAMING_SNAKE_CASE = os.path.splitext(lowerCamelCase__ )[0]
if "epoch" in training_difference:
__SCREAMING_SNAKE_CASE = int(training_difference.replace("""epoch_""" , """""" ) ) + 1
__SCREAMING_SNAKE_CASE = None
else:
__SCREAMING_SNAKE_CASE = int(training_difference.replace("""step_""" , """""" ) )
__SCREAMING_SNAKE_CASE = resume_step // len(lowerCamelCase__ )
resume_step -= starting_epoch * len(lowerCamelCase__ )
# Now we train the model
for epoch in range(lowerCamelCase__ , lowerCamelCase__ ):
model.train()
if args.with_tracking:
__SCREAMING_SNAKE_CASE = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
__SCREAMING_SNAKE_CASE = accelerator.skip_first_batches(lowerCamelCase__ , lowerCamelCase__ )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
__SCREAMING_SNAKE_CASE = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
__SCREAMING_SNAKE_CASE = {k: v.to(accelerator.device ) for k, v in batch.items()}
__SCREAMING_SNAKE_CASE = (batch["image"] - mean) / std
__SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
__SCREAMING_SNAKE_CASE = torch.nn.functional.cross_entropy(lowerCamelCase__ , batch["""label"""] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(lowerCamelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__SCREAMING_SNAKE_CASE = f"step_{overall_step}"
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
__SCREAMING_SNAKE_CASE = os.path.join(args.output_dir , lowerCamelCase__ )
accelerator.save_state(lowerCamelCase__ )
model.eval()
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
__SCREAMING_SNAKE_CASE = {k: v.to(accelerator.device ) for k, v in batch.items()}
__SCREAMING_SNAKE_CASE = (batch["image"] - mean) / std
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
__SCREAMING_SNAKE_CASE = outputs.argmax(dim=-1 )
__SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((predictions, batch["""label"""]) )
__SCREAMING_SNAKE_CASE = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
__SCREAMING_SNAKE_CASE = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}" )
if args.with_tracking:
accelerator.log(
{
"""accuracy""": 100 * eval_metric,
"""train_loss""": total_loss.item() / len(lowerCamelCase__ ),
"""epoch""": epoch,
} , step=lowerCamelCase__ , )
if checkpointing_steps == "epoch":
__SCREAMING_SNAKE_CASE = f"epoch_{epoch}"
if args.output_dir is not None:
__SCREAMING_SNAKE_CASE = os.path.join(args.output_dir , lowerCamelCase__ )
accelerator.save_state(lowerCamelCase__ )
if args.with_tracking:
accelerator.end_training()
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument("""--data_dir""" , required=lowerCamelCase__ , help="""The data folder on disk.""" )
parser.add_argument("""--fp16""" , action="""store_true""" , help="""If passed, will use FP16 training.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCamelCase__ , default=lowerCamelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
parser.add_argument(
"""--checkpointing_steps""" , type=lowerCamelCase__ , default=lowerCamelCase__ , help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase__ , 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=lowerCamelCase__ , default=lowerCamelCase__ , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--with_tracking""" , action="""store_true""" , help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" , )
parser.add_argument(
"""--project_dir""" , type=lowerCamelCase__ , default="""logs""" , help="""Location on where to store experiment tracking logs` and relevent project information""" , )
__SCREAMING_SNAKE_CASE = parser.parse_args()
__SCREAMING_SNAKE_CASE = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main()
| 100 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
def snake_case ( self : Optional[Any] ):
lowercase__ : str = tempfile.mkdtemp()
lowercase__ : Optional[Any] = 8
# DPR tok
lowercase__ : Dict = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowercase__ : List[Any] = os.path.join(self.tmpdirname , "dpr_tokenizer" )
os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
lowercase__ : str = os.path.join(SCREAMING_SNAKE_CASE , DPR_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] ) )
# BART tok
lowercase__ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowercase__ : List[str] = dict(zip(SCREAMING_SNAKE_CASE , range(len(SCREAMING_SNAKE_CASE ) ) ) )
lowercase__ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowercase__ : List[Any] = {"unk_token": "<unk>"}
lowercase__ : Any = os.path.join(self.tmpdirname , "bart_tokenizer" )
os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = os.path.join(SCREAMING_SNAKE_CASE , BART_VOCAB_FILES_NAMES["vocab_file"] )
lowercase__ : str = os.path.join(SCREAMING_SNAKE_CASE , BART_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 snake_case ( self : Any ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def snake_case ( self : Any ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def snake_case ( self : Any ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) )
def snake_case ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def snake_case ( self : Optional[int] ):
lowercase__ : int = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def snake_case ( self : List[str] ):
lowercase__ : Union[str, Any] = self.get_dummy_dataset()
lowercase__ : str = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
lowercase__ : Union[str, Any] = dataset
lowercase__ : List[str] = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE : bool ):
lowercase__ : Union[str, Any] = self.get_dummy_dataset()
lowercase__ : Optional[int] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="custom" , )
if from_disk:
lowercase__ : Any = os.path.join(self.tmpdirname , "dataset" )
lowercase__ : Union[str, Any] = os.path.join(self.tmpdirname , "index.faiss" )
dataset.get_index("embeddings" ).save(os.path.join(self.tmpdirname , "index.faiss" ) )
dataset.drop_index("embeddings" )
dataset.save_to_disk(os.path.join(self.tmpdirname , "dataset" ) )
del dataset
lowercase__ : Tuple = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
lowercase__ : Dict = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , SCREAMING_SNAKE_CASE ) , )
return retriever
def snake_case ( self : Tuple ):
lowercase__ : Optional[int] = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
lowercase__ : Union[str, Any] = os.path.join(self.tmpdirname , "hf_bert_base.hnswSQ8_correct_phi_128.c_index" )
dataset.save_faiss_index("embeddings" , index_file_name + ".index.dpr" )
pickle.dump(dataset["id"] , open(index_file_name + ".index_meta.dpr" , "wb" ) )
lowercase__ : Optional[int] = os.path.join(self.tmpdirname , "psgs_w100.tsv.pkl" )
lowercase__ : List[str] = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
pickle.dump(SCREAMING_SNAKE_CASE , open(SCREAMING_SNAKE_CASE , "wb" ) )
lowercase__ : int = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="legacy" , index_path=self.tmpdirname , )
lowercase__ : Any = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def snake_case ( self : int ):
lowercase__ : Any = 1
lowercase__ : str = self.get_dummy_canonical_hf_index_retriever()
lowercase__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def snake_case ( self : str ):
lowercase__ : Dict = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
lowercase__ : Tuple = self.get_dummy_dataset()
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ : List[str] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
def snake_case ( self : str ):
lowercase__ : Union[str, Any] = 1
lowercase__ : Tuple = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ , lowercase__ , lowercase__ : Optional[Any] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def snake_case ( self : Union[str, Any] ):
lowercase__ : str = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ : str = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
def snake_case ( self : Union[str, Any] ):
lowercase__ : Optional[Any] = 1
lowercase__ : str = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ , lowercase__ , lowercase__ : Dict = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def snake_case ( self : List[str] ):
lowercase__ : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
lowercase__ : int = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ : Dict = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
def snake_case ( self : Union[str, Any] ):
lowercase__ : List[Any] = 1
lowercase__ : List[str] = self.get_dummy_legacy_index_retriever()
lowercase__ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ , lowercase__ , lowercase__ : str = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["text", "title"] )
self.assertEqual(len(doc_dicts[0]["text"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["text"][0] , "bar" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["text"][0] , "foo" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def snake_case ( self : Dict ):
lowercase__ : Optional[int] = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ : str = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def snake_case ( self : Any ):
import torch
lowercase__ : List[Any] = 1
lowercase__ : Union[str, Any] = self.get_dummy_canonical_hf_index_retriever()
lowercase__ : Tuple = [[5, 7], [10, 11]]
lowercase__ : Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ : int = retriever(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE )
lowercase__ , lowercase__ , lowercase__ : List[str] = (
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , np.ndarray )
lowercase__ : List[str] = retriever(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE , return_tensors="pt" , )
lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[Any] = ( # noqa: F841
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
out["doc_ids"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def snake_case ( self : int ):
lowercase__ : List[Any] = self.get_dpr_ctx_encoder_tokenizer()
lowercase__ : Optional[int] = 1
lowercase__ : str = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = [[5, 7], [10, 11]]
lowercase__ : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowercase__ : List[Any] = retriever(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(
len(SCREAMING_SNAKE_CASE ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask") ) , SCREAMING_SNAKE_CASE ) # check for doc token related keys in dictionary.
| 130 | 0 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'--original_config_file',
default=None,
type=str,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--scheduler_type',
default='pndm',
type=str,
help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
)
parser.add_argument(
'--pipeline_type',
default=None,
type=str,
help=(
'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
'. If `None` pipeline will be automatically inferred.'
),
)
parser.add_argument(
'--image_size',
default=None,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--prediction_type',
default=None,
type=str,
help=(
'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
parser.add_argument(
'--stable_unclip',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
)
parser.add_argument(
'--stable_unclip_prior',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
)
parser.add_argument(
'--clip_stats_path',
type=str,
help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
required=False,
)
parser.add_argument(
'--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
)
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--vae_path',
type=str,
default=None,
required=False,
help='Set to a path, hub id to an already converted vae to not convert it again.',
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
__SCREAMING_SNAKE_CASE : Optional[Any] = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 233 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __A (snake_case__):
'''simple docstring'''
__lowercase: List[Any] = """mobilenet_v1"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : str=3 , UpperCAmelCase_ : List[Any]=224 , UpperCAmelCase_ : List[Any]=1.0 , UpperCAmelCase_ : Any=8 , UpperCAmelCase_ : int="relu6" , UpperCAmelCase_ : str=True , UpperCAmelCase_ : List[str]=0.999 , UpperCAmelCase_ : Optional[Any]=0.02 , UpperCAmelCase_ : List[Any]=0.001 , **UpperCAmelCase_ : Any , ) ->Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case_ = num_channels
snake_case_ = image_size
snake_case_ = depth_multiplier
snake_case_ = min_depth
snake_case_ = hidden_act
snake_case_ = tf_padding
snake_case_ = classifier_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
class __A (snake_case__):
'''simple docstring'''
__lowercase: int = version.parse("""1.11""")
@property
def lowerCAmelCase ( self : Union[str, Any] ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCAmelCase ( self : int ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def lowerCAmelCase ( self : int ) ->float:
"""simple docstring"""
return 1E-4
| 233 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCamelCase__ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
UpperCAmelCase__ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path", type=__A, default="data/dump.txt", help="The path to the data." )
parser.add_argument("--tokenizer_type", type=__A, default="bert", choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name", type=__A, default="bert-base-uncased", help="The tokenizer to use." )
parser.add_argument("--dump_file", type=__A, default="data/dump", help="The dump file prefix." )
UpperCAmelCase__ = parser.parse_args()
logger.info(f"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase__ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase__ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase__ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase__ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase__ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase__ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(f"""Loading text from {args.file_path}""" )
with open(args.file_path, "r", encoding="utf8" ) as fp:
UpperCAmelCase__ = fp.readlines()
logger.info("Start encoding" )
logger.info(f"""{len(__A )} examples to process.""" )
UpperCAmelCase__ = []
UpperCAmelCase__ = 0
UpperCAmelCase__ = 10_000
UpperCAmelCase__ = time.time()
for text in data:
UpperCAmelCase__ = f"""{bos} {text.strip()} {sep}"""
UpperCAmelCase__ = tokenizer.encode(__A, add_special_tokens=__A )
rslt.append(__A )
iter += 1
if iter % interval == 0:
UpperCAmelCase__ = time.time()
logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase__ = time.time()
logger.info("Finished binarization" )
logger.info(f"""{len(__A )} examples processed.""" )
UpperCAmelCase__ = f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase__ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase__ = [np.uintaa(__A ) for d in rslt]
else:
UpperCAmelCase__ = [np.intaa(__A ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f"""Dump to {dp_file}""" )
with open(__A, "wb" ) as handle:
pickle.dump(rslt_, __A, protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 65 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = k_size // 2
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
__UpperCAmelCase : Any = 1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase ) + square(_UpperCAmelCase )) / (2 * square(_UpperCAmelCase )) )
return g
def a ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = image.shape[0], image.shape[1]
# dst image height and width
__UpperCAmelCase : str = height - k_size + 1
__UpperCAmelCase : Optional[int] = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
__UpperCAmelCase : str = zeros((dst_height * dst_width, k_size * k_size) )
__UpperCAmelCase : Optional[Any] = 0
for i, j in product(range(_UpperCAmelCase ) , range(_UpperCAmelCase ) ):
__UpperCAmelCase : int = ravel(image[i : i + k_size, j : j + k_size] )
__UpperCAmelCase : Union[str, Any] = window
row += 1
# turn the kernel into shape(k*k, 1)
__UpperCAmelCase : Tuple = gen_gaussian_kernel(_UpperCAmelCase , _UpperCAmelCase )
__UpperCAmelCase : List[Any] = ravel(_UpperCAmelCase )
# reshape and get the dst image
__UpperCAmelCase : Optional[Any] = dot(_UpperCAmelCase , _UpperCAmelCase ).reshape(_UpperCAmelCase , _UpperCAmelCase ).astype(_UpperCAmelCase )
return dst
if __name__ == "__main__":
# read original image
__A =imread(R"../image_data/lena.jpg")
# turn image in gray scale value
__A =cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
__A =gaussian_filter(gray, 3, sigma=1)
__A =gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussianaxa)
imshow("gaussian filter with 5x5 mask", gaussianaxa)
waitKey()
| 226 | 0 |
"""simple docstring"""
from math import isqrt
def _a ( _snake_case ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) )
def _a ( _snake_case = 10**6 ):
"""simple docstring"""
UpperCAmelCase = 0
UpperCAmelCase = 1
UpperCAmelCase = 7
while prime_candidate < max_prime:
primes_count += is_prime(SCREAMING_SNAKE_CASE_ )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 366 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_UpperCamelCase = logging.get_logger(__name__)
def _a ( _snake_case ):
"""simple docstring"""
if isinstance(_snake_case , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_snake_case , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_snake_case ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class lowerCamelCase__ ( snake_case ):
SCREAMING_SNAKE_CASE = ['''pixel_values''']
def __init__( self ,A = True ,A = None ,A = PILImageResampling.BILINEAR ,A = True ,A = None ,A = True ,A = 1 / 255 ,A = True ,A = True ,A = None ,A = None ,**A ,):
super().__init__(**A )
UpperCAmelCase = size if size is not None else {"""shortest_edge""": 256}
UpperCAmelCase = get_size_dict(A ,default_to_square=A )
UpperCAmelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
UpperCAmelCase = get_size_dict(A ,param_name="""crop_size""" )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = resample
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = offset
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _UpperCamelCase ( self ,A ,A ,A = PILImageResampling.BILINEAR ,A = None ,**A ,):
UpperCAmelCase = get_size_dict(A ,default_to_square=A )
if "shortest_edge" in size:
UpperCAmelCase = get_resize_output_image_size(A ,size["""shortest_edge"""] ,default_to_square=A )
elif "height" in size and "width" in size:
UpperCAmelCase = (size["""height"""], size["""width"""])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(A ,size=A ,resample=A ,data_format=A ,**A )
def _UpperCamelCase ( self ,A ,A ,A = None ,**A ,):
UpperCAmelCase = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(A ,size=(size["""height"""], size["""width"""]) ,data_format=A ,**A )
def _UpperCamelCase ( self ,A ,A ,A = True ,A = None ,**A ,):
UpperCAmelCase = image.astype(np.floataa )
if offset:
UpperCAmelCase = image - (scale / 2)
return rescale(A ,scale=A ,data_format=A ,**A )
def _UpperCamelCase ( self ,A ,A ,A ,A = None ,**A ,):
return normalize(A ,mean=A ,std=A ,data_format=A ,**A )
def _UpperCamelCase ( self ,A ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = ChannelDimension.FIRST ,):
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_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
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.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
UpperCAmelCase = to_numpy_array(A )
if do_resize:
UpperCAmelCase = self.resize(image=A ,size=A ,resample=A )
if do_center_crop:
UpperCAmelCase = self.center_crop(A ,size=A )
if do_rescale:
UpperCAmelCase = self.rescale(image=A ,scale=A ,offset=A )
if do_normalize:
UpperCAmelCase = self.normalize(image=A ,mean=A ,std=A )
UpperCAmelCase = to_channel_dimension_format(A ,A )
return image
def _UpperCamelCase ( self ,A ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = ChannelDimension.FIRST ,**A ,):
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = offset if offset is not None else self.offset
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(A ,default_to_square=A )
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(A ,param_name="""crop_size""" )
if not valid_images(A ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
UpperCAmelCase = make_batched(A )
UpperCAmelCase = [
[
self._preprocess_image(
image=A ,do_resize=A ,size=A ,resample=A ,do_center_crop=A ,crop_size=A ,do_rescale=A ,rescale_factor=A ,offset=A ,do_normalize=A ,image_mean=A ,image_std=A ,data_format=A ,)
for img in video
]
for video in videos
]
UpperCAmelCase = {"""pixel_values""": videos}
return BatchFeature(data=A ,tensor_type=A )
| 234 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE = logging.getLogger()
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : int = argparse.ArgumentParser()
parser.add_argument('-f' )
snake_case_ : List[Any] = parser.parse_args()
return args.f
class SCREAMING_SNAKE_CASE_ ( __lowercase ):
def UpperCAmelCase_ ( self : List[str] ) -> None:
"""simple docstring"""
snake_case_ : Dict = logging.StreamHandler(sys.stdout )
logger.addHandler(_a )
def UpperCAmelCase_ ( self : Union[str, Any] , _A : Any ) -> Any:
"""simple docstring"""
snake_case_ : Optional[int] = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , 'run_glue_deebert.py' )
with patch.object(_a , 'argv' , _a ):
snake_case_ : Optional[Any] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_a , 0.6_6_6 )
@slow
@require_torch_non_multi_gpu
def UpperCAmelCase_ ( self : int ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[Any] = '''
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
'''.split()
self.run_and_check(_a )
snake_case_ : Union[str, Any] = '''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(_a )
snake_case_ : List[Any] = '''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(_a )
| 327 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : int = GPTaTokenizer
UpperCAmelCase__ : str = GPTaTokenizerFast
UpperCAmelCase__ : Tuple = True
UpperCAmelCase__ : List[Any] = {"add_prefix_space": True}
UpperCAmelCase__ : int = False
def __lowercase ( self ) -> int:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a : str = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
_a : List[Any] = dict(zip(_a , range(len(_a ) ) ) )
_a : Tuple = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_a : List[str] = {'''unk_token''': '''<unk>'''}
_a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a : Tuple = 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(_a ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_a ) )
def __lowercase ( self , **_a ) -> List[str]:
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def __lowercase ( self , _a ) -> Optional[Any]:
_a : Tuple = '''lower newer'''
_a : Tuple = '''lower newer'''
return input_text, output_text
def __lowercase ( self ) -> Any:
_a : Any = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a : int = '''lower newer'''
_a : int = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
_a : List[str] = tokenizer.tokenize(_a , add_prefix_space=_a )
self.assertListEqual(_a , _a )
_a : Optional[int] = tokens + [tokenizer.unk_token]
_a : List[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def __lowercase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_a : int = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer(add_prefix_space=_a )
_a : Tuple = '''lower newer'''
# Testing tokenization
_a : List[str] = tokenizer.tokenize(_a , add_prefix_space=_a )
_a : Optional[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
# Testing conversion to ids without special tokens
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
_a : List[Any] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
# Testing conversion to ids with special tokens
_a : List[str] = self.get_rust_tokenizer(add_prefix_space=_a )
_a : List[str] = tokenizer.encode(_a , add_prefix_space=_a )
_a : Tuple = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
# Testing the unknown token
_a : Optional[Any] = tokens + [rust_tokenizer.unk_token]
_a : str = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a ) , _a )
def __lowercase ( self , *_a , **_a ) -> int:
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def __lowercase ( self , _a=1_5 ) -> str:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : Optional[int] = self.rust_tokenizer_class.from_pretrained(_a , **_a )
# Simple input
_a : List[str] = '''This is a simple input'''
_a : Optional[Any] = ['''This is a simple input 1''', '''This is a simple input 2''']
_a : Tuple = ('''This is a simple input''', '''This is a pair''')
_a : Optional[Any] = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding='''max_length''' )
# Simple input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding='''max_length''' )
# Simple input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding='''max_length''' , )
# Pair input
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding='''max_length''' )
# Pair input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding='''max_length''' )
# Pair input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding='''max_length''' , )
def __lowercase ( self ) -> List[Any]:
_a : int = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
_a : int = '''This is a simple input'''
_a : int = ['''This is a simple input looooooooong''', '''This is a simple input''']
_a : Any = ('''This is a simple input''', '''This is a pair''')
_a : Dict = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
_a : Optional[int] = tokenizer.pad_token_id
_a : List[Any] = tokenizer(_a , padding='''max_length''' , max_length=3_0 , return_tensors='''np''' )
_a : Optional[int] = tokenizer(_a , padding=_a , truncate=_a , return_tensors='''np''' )
_a : int = tokenizer(*_a , padding='''max_length''' , max_length=6_0 , return_tensors='''np''' )
_a : Union[str, Any] = tokenizer(_a , padding=_a , truncate=_a , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 3_0 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 6_0 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def __lowercase ( self ) -> Any:
_a : Union[str, Any] = '''$$$'''
_a : List[Any] = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=_a , add_bos_token=_a )
_a : Any = '''This is a simple input'''
_a : str = ['''This is a simple input 1''', '''This is a simple input 2''']
_a : Tuple = tokenizer.bos_token_id
_a : Optional[Any] = tokenizer(_a )
_a : str = tokenizer(_a )
self.assertEqual(out_s.input_ids[0] , _a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_a : str = tokenizer.decode(out_s.input_ids )
_a : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def __lowercase ( self ) -> str:
pass
def __lowercase ( self ) -> Dict:
# TODO: change to self.get_tokenizers() when the fast version is implemented
_a : Optional[int] = [self.get_tokenizer(do_lower_case=_a , add_bos_token=_a )]
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a : Tuple = '''Encode this.'''
_a : Optional[Any] = '''This one too please.'''
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
encoded_sequence += tokenizer.encode(_a , add_special_tokens=_a )
_a : List[str] = tokenizer.encode_plus(
_a , _a , add_special_tokens=_a , return_special_tokens_mask=_a , )
_a : int = encoded_sequence_dict['''input_ids''']
_a : int = encoded_sequence_dict['''special_tokens_mask''']
self.assertEqual(len(_a ) , len(_a ) )
_a : List[Any] = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(_a )
]
_a : str = [x for x in filtered_sequence if x is not None]
self.assertEqual(_a , _a )
@require_tokenizers
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> List[Any]:
# More context:
# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
_a : Any = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=_a )
_a : int = '''A photo of a cat'''
_a : List[Any] = tokenizer.encode(
_a , )
self.assertEqual(_a , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
tokenizer.save_pretrained('''test_opt''' )
_a : Union[str, Any] = AutoTokenizer.from_pretrained('''./test_opt''' )
_a : Any = tokenizer.encode(
_a , )
self.assertEqual(_a , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
def __lowercase ( self ) -> int:
_a : Union[str, Any] = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , use_slow=_a )
_a : Any = '''A photo of a cat'''
_a : Optional[int] = tokenizer.encode(
_a , )
# Same as above
self.assertEqual(_a , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
@unittest.skip('''This test is failing because of a bug in the fast tokenizer''' )
def __lowercase ( self ) -> Any:
_a : Union[str, Any] = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=_a )
_a : Optional[Any] = '''bos'''
_a : Optional[Any] = tokenizer.get_vocab()['''bos''']
_a : str = '''A photo of a cat'''
_a : int = tokenizer.encode(
_a , )
# We changed the bos token
self.assertEqual(_a , [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
tokenizer.save_pretrained('''./tok''' )
_a : Dict = AutoTokenizer.from_pretrained('''./tok''' )
self.assertTrue(tokenizer.is_fast )
_a : Union[str, Any] = tokenizer.encode(
_a , )
self.assertEqual(_a , [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
| 235 | 0 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
lowerCAmelCase__ = logging.get_logger(__name__)
logging.set_verbosity_info()
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase__ : Any = XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__ )
lowercase__ : Optional[int] = XLMProphetNetForConditionalGeneration.from_pretrained(
lowerCamelCase__ , output_loading_info=lowerCamelCase__ )
else:
lowercase__ : Optional[Any] = ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__ )
lowercase__ : Any = ProphetNetForConditionalGeneration.from_pretrained(
lowerCamelCase__ , output_loading_info=lowerCamelCase__ )
lowercase__ : Any = ["key_proj", "value_proj", "query_proj"]
lowercase__ : Optional[Any] = {
"self_attn": "ngram_self_attn",
"cross_attn": "encoder_attn",
"cross_attn_layer_norm": "encoder_attn_layer_norm",
"feed_forward_layer_norm": "final_layer_norm",
"feed_forward": "",
"intermediate": "fc1",
"output": "fc2",
"key_proj": "k_proj",
"query_proj": "q_proj",
"value_proj": "v_proj",
"word_embeddings": "embed_tokens",
"embeddings_layer_norm": "emb_layer_norm",
"relative_pos_embeddings": "relative_linear",
"ngram_embeddings": "ngram_input_embed",
"position_embeddings": "embed_positions",
}
for key in loading_info["missing_keys"]:
lowercase__ : Optional[int] = key.split("." )
if attributes[0] == "lm_head":
lowercase__ : List[Any] = prophet
lowercase__ : int = prophet_old
else:
lowercase__ : str = prophet.prophetnet
lowercase__ : Optional[Any] = prophet_old.model
lowercase__ : List[str] = False
for attribute in attributes:
if attribute in mapping:
lowercase__ : Union[str, Any] = mapping[attribute]
if not hasattr(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) > 0:
lowercase__ : Any = attribute
elif hasattr(lowerCamelCase__ , lowerCamelCase__ ):
lowercase__ : Optional[int] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase__ : List[Any] = old_model.weight
logger.info(F"""{attribute} is initialized.""" )
lowercase__ : str = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase__ : Dict = old_model.bias
logger.info(F"""{attribute} is initialized""" )
lowercase__ : Dict = True
break
elif attribute in special_keys and hasattr(lowerCamelCase__ , "in_proj_weight" ):
lowercase__ : Union[str, Any] = old_model.in_proj_weight.shape[0] // 3
lowercase__ : Dict = getattr(lowerCamelCase__ , lowerCamelCase__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase__ : List[Any] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase__ : List[Any] = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase__ : List[str] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase__ : Union[str, Any] = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase__ : List[Any] = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase__ : str = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase__ : List[Any] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase__ : int = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase__ : List[Any] = True
break
if attribute.isdigit():
lowercase__ : Any = model[int(lowerCamelCase__ )]
lowercase__ : List[str] = old_model[int(lowerCamelCase__ )]
else:
lowercase__ : Optional[Any] = getattr(lowerCamelCase__ , lowerCamelCase__ )
if old_attribute == "":
lowercase__ : Union[str, Any] = old_model
else:
if not hasattr(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(F"""{old_model} does not have {old_attribute}""" )
lowercase__ : Dict = getattr(lowerCamelCase__ , lowerCamelCase__ )
if not is_key_init:
raise ValueError(F"""{key} was not correctly initialized!""" )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowerCAmelCase__ = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 359 |
from math import ceil, sqrt
def __lowerCamelCase ( lowerCamelCase__ = 1_000_000 ):
"""simple docstring"""
lowercase__ : int = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase__ : List[str] = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
lowercase__ : List[str] = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f'''{solution() = }''')
| 121 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class a_ ( unittest.TestCase ):
def __init__( self : Dict , lowercase : Union[str, Any] , lowercase : Any=100 , lowercase : List[str]=13 , lowercase : int=30 , lowercase : Dict=2 , lowercase : List[str]=3 , lowercase : List[Any]=True , lowercase : Union[str, Any]=True , lowercase : Union[str, Any]=32 , lowercase : Tuple=5 , lowercase : List[Any]=4 , lowercase : Union[str, Any]=37 , lowercase : Optional[int]="gelu" , lowercase : Any=0.1 , lowercase : Optional[Any]=0.1 , lowercase : Optional[Any]=10 , lowercase : Union[str, Any]=0.02 , lowercase : Union[str, Any]=3 , ):
"""simple docstring"""
lowercase_ :int = parent
lowercase_ :List[Any] = vocab_size
lowercase_ :Dict = batch_size
lowercase_ :str = image_size
lowercase_ :List[Any] = patch_size
lowercase_ :List[str] = num_channels
lowercase_ :Tuple = is_training
lowercase_ :Dict = use_labels
lowercase_ :Any = hidden_size
lowercase_ :str = num_hidden_layers
lowercase_ :List[Any] = num_attention_heads
lowercase_ :Union[str, Any] = intermediate_size
lowercase_ :List[str] = hidden_act
lowercase_ :Optional[Any] = hidden_dropout_prob
lowercase_ :Optional[Any] = attention_probs_dropout_prob
lowercase_ :Union[str, Any] = type_sequence_label_size
lowercase_ :Union[str, Any] = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ :List[Any] = (image_size // patch_size) ** 2
lowercase_ :int = num_patches + 1
def lowercase__ ( self : str ):
"""simple docstring"""
lowercase_ :List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ :Tuple = None
if self.use_labels:
lowercase_ :Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ :Optional[int] = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , 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 , is_decoder=lowercase , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def lowercase__ ( self : Union[str, Any] , lowercase : str , lowercase : Optional[Any] , lowercase : Optional[Any] ):
"""simple docstring"""
lowercase_ :List[str] = FlaxBeitModel(config=lowercase )
lowercase_ :Tuple = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Union[str, Any] , lowercase : Optional[int] , lowercase : Union[str, Any] , lowercase : Dict ):
"""simple docstring"""
lowercase_ :int = FlaxBeitForMaskedImageModeling(config=lowercase )
lowercase_ :str = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def lowercase__ ( self : List[str] , lowercase : Union[str, Any] , lowercase : Union[str, Any] , lowercase : Dict ):
"""simple docstring"""
lowercase_ :str = self.type_sequence_label_size
lowercase_ :Optional[Any] = FlaxBeitForImageClassification(config=lowercase )
lowercase_ :List[str] = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase_ :Optional[int] = 1
lowercase_ :List[str] = FlaxBeitForImageClassification(lowercase )
lowercase_ :Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase_ :int = model(lowercase )
def lowercase__ ( self : Union[str, Any] ):
"""simple docstring"""
lowercase_ :int = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) :List[str] = config_and_inputs
lowercase_ :Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class a_ ( _lowerCAmelCase , unittest.TestCase ):
__A = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def lowercase__ ( self : Optional[int] ):
"""simple docstring"""
lowercase_ :Dict = FlaxBeitModelTester(self )
lowercase_ :int = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def lowercase__ ( self : int ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self : Tuple ):
"""simple docstring"""
lowercase_ , lowercase_ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ :Optional[Any] = model_class(lowercase )
lowercase_ :List[Any] = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ :str = [*signature.parameters.keys()]
lowercase_ :List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase )
def lowercase__ ( self : List[str] ):
"""simple docstring"""
lowercase_ , lowercase_ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase_ :Dict = self._prepare_for_class(lowercase , lowercase )
lowercase_ :Optional[int] = model_class(lowercase )
@jax.jit
def model_jitted(lowercase : Dict , **lowercase : Any ):
return model(pixel_values=lowercase , **lowercase )
with self.subTest("JIT Enabled" ):
lowercase_ :int = model_jitted(**lowercase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowercase_ :Any = model_jitted(**lowercase ).to_tuple()
self.assertEqual(len(lowercase ) , len(lowercase ) )
for jitted_output, output in zip(lowercase , lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowercase__ ( self : Dict ):
"""simple docstring"""
lowercase_ :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def lowercase__ ( self : List[str] ):
"""simple docstring"""
lowercase_ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
lowercase_ :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@slow
def lowercase__ ( self : int ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase_ :str = model_class_name.from_pretrained("microsoft/beit-base-patch16-224" )
lowercase_ :int = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(lowercase )
def UpperCAmelCase_ ( ):
lowercase_ :List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@require_flax
class a_ ( unittest.TestCase ):
@cached_property
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def lowercase__ ( self : str ):
"""simple docstring"""
lowercase_ :List[str] = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" )
lowercase_ :str = self.default_image_processor
lowercase_ :int = prepare_img()
lowercase_ :Any = image_processor(images=lowercase , return_tensors="np" ).pixel_values
# prepare bool_masked_pos
lowercase_ :List[str] = np.ones((1, 196) , dtype=lowercase )
# forward pass
lowercase_ :List[str] = model(pixel_values=lowercase , bool_masked_pos=lowercase )
lowercase_ :Dict = outputs.logits
# verify the logits
lowercase_ :Tuple = (1, 196, 8_192)
self.assertEqual(logits.shape , lowercase )
lowercase_ :Dict = np.array(
[[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , lowercase , atol=1e-2 ) )
@slow
def lowercase__ ( self : Tuple ):
"""simple docstring"""
lowercase_ :int = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" )
lowercase_ :Tuple = self.default_image_processor
lowercase_ :Dict = prepare_img()
lowercase_ :str = image_processor(images=lowercase , return_tensors="np" )
# forward pass
lowercase_ :Any = model(**lowercase )
lowercase_ :Tuple = outputs.logits
# verify the logits
lowercase_ :Dict = (1, 1_000)
self.assertEqual(logits.shape , lowercase )
lowercase_ :List[str] = np.array([-1.23_85, -1.09_87, -1.01_08] )
self.assertTrue(np.allclose(logits[0, :3] , lowercase , atol=1e-4 ) )
lowercase_ :Dict = 281
self.assertEqual(logits.argmax(-1 ).item() , lowercase )
@slow
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
lowercase_ :List[str] = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" )
lowercase_ :List[Any] = self.default_image_processor
lowercase_ :Union[str, Any] = prepare_img()
lowercase_ :Tuple = image_processor(images=lowercase , return_tensors="np" )
# forward pass
lowercase_ :List[Any] = model(**lowercase )
lowercase_ :Dict = outputs.logits
# verify the logits
lowercase_ :List[str] = (1, 21_841)
self.assertEqual(logits.shape , lowercase )
lowercase_ :Union[str, Any] = np.array([1.68_81, -0.27_87, 0.59_01] )
self.assertTrue(np.allclose(logits[0, :3] , lowercase , atol=1e-4 ) )
lowercase_ :List[Any] = 2_396
self.assertEqual(logits.argmax(-1 ).item() , lowercase )
| 223 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class a_ ( unittest.TestCase ):
def __init__( self : List[str] , lowercase : str , lowercase : Union[str, Any]=13 , lowercase : int=7 , lowercase : List[str]=True , lowercase : int=True , lowercase : str=True , lowercase : Any=True , lowercase : List[str]=99 , lowercase : Union[str, Any]=32 , lowercase : Optional[Any]=5 , lowercase : Dict=4 , lowercase : Dict=37 , lowercase : Dict="gelu" , lowercase : Optional[int]=0.1 , lowercase : str=0.1 , lowercase : List[Any]=512 , lowercase : str=16 , lowercase : Dict=2 , lowercase : Any=0.02 , lowercase : Any=4 , ):
"""simple docstring"""
lowercase_ :List[str] = parent
lowercase_ :Any = batch_size
lowercase_ :Dict = seq_length
lowercase_ :Union[str, Any] = is_training
lowercase_ :Optional[int] = use_attention_mask
lowercase_ :Any = use_token_type_ids
lowercase_ :Union[str, Any] = use_labels
lowercase_ :Dict = vocab_size
lowercase_ :Tuple = hidden_size
lowercase_ :Tuple = num_hidden_layers
lowercase_ :Optional[int] = num_attention_heads
lowercase_ :Optional[Any] = intermediate_size
lowercase_ :str = hidden_act
lowercase_ :Tuple = hidden_dropout_prob
lowercase_ :Optional[Any] = attention_probs_dropout_prob
lowercase_ :Tuple = max_position_embeddings
lowercase_ :Any = type_vocab_size
lowercase_ :int = type_sequence_label_size
lowercase_ :Tuple = initializer_range
lowercase_ :Optional[Any] = num_choices
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
lowercase_ :int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ :Union[str, Any] = None
if self.use_attention_mask:
lowercase_ :Dict = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ :List[str] = None
if self.use_token_type_ids:
lowercase_ :str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ :Optional[Any] = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , 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 , is_decoder=lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase__ ( self : Union[str, Any] ):
"""simple docstring"""
lowercase_ :int = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ , lowercase_ :Tuple = config_and_inputs
lowercase_ :Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
lowercase_ :Any = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ , lowercase_ :Union[str, Any] = config_and_inputs
lowercase_ :Dict = True
lowercase_ :Optional[int] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase_ :str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class a_ ( _lowerCAmelCase , unittest.TestCase ):
__A = True
__A = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase__ ( self : Any ):
"""simple docstring"""
lowercase_ :Optional[Any] = FlaxBertModelTester(self )
@slow
def lowercase__ ( self : List[str] ):
"""simple docstring"""
lowercase_ :List[str] = FlaxBertModel.from_pretrained("bert-base-cased" )
lowercase_ :str = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowercase )
| 223 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[Any] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
"""LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LiltForQuestionAnswering""",
"""LiltForSequenceClassification""",
"""LiltForTokenClassification""",
"""LiltModel""",
"""LiltPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
A_ : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
_UpperCAmelCase : int = 0
while arr[min(lowerCAmelCase_ , lowerCAmelCase_ ) - 1] < x:
_UpperCAmelCase : Optional[int] = step
step += int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase : List[Any] = prev + 1
if prev == min(lowerCAmelCase_ , lowerCAmelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A_ : str = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
A_ : int = int(input("""Enter the number to be searched:\n"""))
A_ : Any = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(f"""Number {x} is at index {res}""")
| 349 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
__a = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__a = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
__a = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase ( self : Union[str, Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , reference_urls=[] , )
def lowerCamelCase ( self : Tuple , snake_case_ : int , snake_case_ : Tuple , snake_case_ : List[Any]=None , snake_case_ : Any=False , snake_case_ : Optional[int]=False , snake_case_ : str=False , ):
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
snake_case__ : int = np.array([re.sub(snake_case_ , """""" , snake_case_ ) for x in predictions] )
snake_case__ : Any = np.array([re.sub(snake_case_ , """""" , snake_case_ ) for x in references] )
else:
snake_case__ : int = np.asarray(snake_case_ )
snake_case__ : Optional[int] = np.asarray(snake_case_ )
if ignore_case:
snake_case__ : Dict = np.char.lower(snake_case_ )
snake_case__ : Tuple = np.char.lower(snake_case_ )
if ignore_punctuation:
snake_case__ : str = string.punctuation.maketrans("""""" , """""" , string.punctuation )
snake_case__ : Tuple = np.char.translate(snake_case_ , table=snake_case_ )
snake_case__ : Dict = np.char.translate(snake_case_ , table=snake_case_ )
if ignore_numbers:
snake_case__ : Any = string.digits.maketrans("""""" , """""" , string.digits )
snake_case__ : Union[str, Any] = np.char.translate(snake_case_ , table=snake_case_ )
snake_case__ : Union[str, Any] = np.char.translate(snake_case_ , table=snake_case_ )
snake_case__ : Tuple = predictions == references
return {"exact_match": np.mean(snake_case_ ) * 100}
| 35 | 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 ( __lowercase : int ):
'''simple docstring'''
if hor == 1_28:
A_ : List[Any] = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
A_ : Tuple = (32, 1_28, 2_56)
A_ : Optional[int] = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
A_ : Union[str, Any] = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
A_ : Any = (32, 64, 1_28, 2_56)
A_ : int = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
A_ : List[str] = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
A_ : List[Any] = model.state_dict()
A_ : List[str] = {
'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',
}
A_ : Union[str, Any] = UNetaDModel(**__lowercase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
A_ : Optional[Any] = dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
A_ : Optional[int] = state_dict.pop(__lowercase )
hf_value_function.load_state_dict(__lowercase )
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(__lowercase ,__lowercase )
def UpperCamelCase ( ):
'''simple docstring'''
A_ : Any = {
'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',
}
A_ : Union[str, Any] = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
A_ : List[Any] = model
A_ : Union[str, Any] = UNetaDModel(**__lowercase )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
A_ : Optional[int] = dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
A_ : List[str] = state_dict.pop(__lowercase )
hf_value_function.load_state_dict(__lowercase )
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(__lowercase ,__lowercase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 140 | 0 |
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def _lowerCamelCase ( a :str ) -> tuple[list[int], list[int]]:
__UpperCamelCase : List[Any] = [ord(a ) for i in text]
__UpperCamelCase : Optional[int] = []
__UpperCamelCase : Dict = []
for i in plain:
__UpperCamelCase : Dict = random.randint(1 , 3_0_0 )
__UpperCamelCase : Union[str, Any] = (i + k) * k
cipher.append(a )
key.append(a )
return cipher, key
@staticmethod
def _lowerCamelCase ( a :list[int] , a :list[int] ) -> str:
__UpperCamelCase : Any = []
for i in range(len(a ) ):
__UpperCamelCase : int = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(a ) )
return "".join(a )
if __name__ == "__main__":
lowercase : List[str] = Onepad().encrypt('Hello')
print(c, k)
print(Onepad().decrypt(c, k)) | 360 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] , _lowerCamelCase : str) -> Any:
'''simple docstring'''
__UpperCamelCase : Dict = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple) -> Any:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = 0
while b > 0:
if b & 1:
__UpperCamelCase : str = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 151 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
_lowerCamelCase =None
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
_lowerCamelCase ={
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
},
}
_lowerCamelCase ={
"camembert-base": 5_12,
}
_lowerCamelCase ="▁"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
__UpperCAmelCase = CamembertTokenizer
def __init__( self : List[str] ,snake_case : str=None ,snake_case : Dict=None ,snake_case : int="<s>" ,snake_case : Optional[int]="</s>" ,snake_case : Optional[Any]="</s>" ,snake_case : Union[str, Any]="<s>" ,snake_case : Optional[int]="<unk>" ,snake_case : Optional[Any]="<pad>" ,snake_case : str="<mask>" ,snake_case : Tuple=["<s>NOTUSED", "</s>NOTUSED"] ,**snake_case : Any ,):
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE =AddedToken(snake_case ,lstrip=snake_case ,rstrip=snake_case ) if isinstance(snake_case ,snake_case ) else mask_token
super().__init__(
snake_case ,tokenizer_file=snake_case ,bos_token=snake_case ,eos_token=snake_case ,sep_token=snake_case ,cls_token=snake_case ,unk_token=snake_case ,pad_token=snake_case ,mask_token=snake_case ,additional_special_tokens=snake_case ,**snake_case ,)
SCREAMING_SNAKE_CASE =vocab_file
SCREAMING_SNAKE_CASE =False if not self.vocab_file else True
def _lowerCAmelCase ( self : Dict ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowerCAmelCase ( self : int ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.sep_token_id]
SCREAMING_SNAKE_CASE =[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 _lowerCAmelCase ( self : str ,snake_case : str ,snake_case : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(snake_case ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
SCREAMING_SNAKE_CASE =os.path.join(
snake_case ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ):
copyfile(self.vocab_file ,snake_case )
return (out_vocab_file,)
| 334 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_vision_model'
def __init__( self : List[Any] ,snake_case : List[Any]=1408 ,snake_case : Optional[Any]=6144 ,snake_case : Optional[int]=39 ,snake_case : Optional[int]=16 ,snake_case : Optional[Any]=224 ,snake_case : Tuple=14 ,snake_case : Optional[Any]="gelu" ,snake_case : Union[str, Any]=0.00_001 ,snake_case : Dict=0.0 ,snake_case : Union[str, Any]=1e-10 ,snake_case : int=True ,**snake_case : str ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =qkv_bias
@classmethod
def _lowerCAmelCase ( cls : Dict ,snake_case : Union[str, os.PathLike] ,**snake_case : str ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_qformer'
def __init__( self : Any ,snake_case : Dict=30522 ,snake_case : int=768 ,snake_case : List[Any]=12 ,snake_case : List[str]=12 ,snake_case : Optional[Any]=3072 ,snake_case : str="gelu" ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : List[Any]=0.02 ,snake_case : List[str]=1e-12 ,snake_case : Tuple=0 ,snake_case : Union[str, Any]="absolute" ,snake_case : List[Any]=2 ,snake_case : List[str]=1408 ,**snake_case : Optional[Any] ,):
super().__init__(pad_token_id=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =cross_attention_frequency
SCREAMING_SNAKE_CASE =encoder_hidden_size
@classmethod
def _lowerCAmelCase ( cls : List[Any] ,snake_case : Union[str, os.PathLike] ,**snake_case : Dict ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip-2'
__UpperCAmelCase = True
def __init__( self : int ,snake_case : Dict=None ,snake_case : Tuple=None ,snake_case : str=None ,snake_case : Union[str, Any]=32 ,**snake_case : int ):
super().__init__(**snake_case )
if vision_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
SCREAMING_SNAKE_CASE =BlipaVisionConfig(**snake_case )
SCREAMING_SNAKE_CASE =BlipaQFormerConfig(**snake_case )
SCREAMING_SNAKE_CASE =text_config['model_type'] if 'model_type' in text_config else 'opt'
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[text_model_type](**snake_case )
SCREAMING_SNAKE_CASE =self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE =self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE =num_query_tokens
SCREAMING_SNAKE_CASE =self.vision_config.hidden_size
SCREAMING_SNAKE_CASE =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE =1.0
SCREAMING_SNAKE_CASE =0.02
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] ,snake_case : BlipaVisionConfig ,snake_case : BlipaQFormerConfig ,snake_case : PretrainedConfig ,**snake_case : Any ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**snake_case ,)
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE =self.vision_config.to_dict()
SCREAMING_SNAKE_CASE =self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE =self.text_config.to_dict()
SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 334 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : str = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, 'r', encoding='utf-8') as f:
__snake_case : Optional[Any] = json.load(f)
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __A ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
return FSMTTokenizer.from_pretrained(_lowerCamelCase )
def __A ( self , _SCREAMING_SNAKE_CASE ) -> Tuple:
A_ = FSMTForConditionalGeneration.from_pretrained(_lowerCamelCase ).to(_lowerCamelCase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['''en-ru''', 26.0],
['''ru-en''', 22.0],
['''en-de''', 22.0],
['''de-en''', 29.0],
] )
@slow
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
A_ = F'''facebook/wmt19-{pair}'''
A_ = self.get_tokenizer(_lowerCamelCase )
A_ = self.get_model(_lowerCamelCase )
A_ = bleu_data[pair]['''src''']
A_ = bleu_data[pair]['''tgt''']
A_ = tokenizer(_lowerCamelCase , return_tensors='''pt''' , truncation=_lowerCamelCase , padding='''longest''' ).to(_lowerCamelCase )
A_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
A_ = tokenizer.batch_decode(
_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
A_ = calculate_bleu(_lowerCamelCase , _lowerCamelCase )
print(_lowerCamelCase )
self.assertGreaterEqual(scores['''bleu'''] , _lowerCamelCase )
| 368 | '''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
__snake_case : Any = '\\n@inproceedings{lin-2004-rouge,\n title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n author = "Lin, Chin-Yew",\n booktitle = "Text Summarization Branches Out",\n month = jul,\n year = "2004",\n address = "Barcelona, Spain",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W04-1013",\n pages = "74--81",\n}\n'
__snake_case : Dict = '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'
__snake_case : Optional[int] = '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n `"rougeL"`: Longest common subsequence based scoring.\n `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric(\'rouge\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n >>> print(results["rouge1"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results["rouge1"].mid.fmeasure)\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def __A ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
if rouge_types is None:
A_ = ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
A_ = rouge_scorer.RougeScorer(rouge_types=_SCREAMING_SNAKE_CASE , use_stemmer=_SCREAMING_SNAKE_CASE )
if use_aggregator:
A_ = scoring.BootstrapAggregator()
else:
A_ = []
for ref, pred in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ = scorer.score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if use_aggregator:
aggregator.add_scores(_SCREAMING_SNAKE_CASE )
else:
scores.append(_SCREAMING_SNAKE_CASE )
if use_aggregator:
A_ = aggregator.aggregate()
else:
A_ = {}
for key in scores[0]:
A_ = [score[key] for score in scores]
return result
| 18 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
lowercase = logging.get_logger(__name__)
lowercase = {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
"allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
),
}
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
lowerCAmelCase = '''longformer'''
def __init__( self , a = 5_12 , a = 2 , a = 1 , a = 0 , a = 2 , a = 3_05_22 , a = 7_68 , a = 12 , a = 12 , a = 30_72 , a = "gelu" , a = 0.1 , a = 0.1 , a = 5_12 , a = 2 , a = 0.02 , a = 1E-12 , a = False , **a , ) -> Dict:
super().__init__(pad_token_id=a , **a )
snake_case_ = attention_window
snake_case_ = sep_token_id
snake_case_ = bos_token_id
snake_case_ = eos_token_id
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = onnx_export
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , a , a = "default" , a = None ) -> int:
super().__init__(a , a , a )
snake_case_ = True
@property
def _UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case_ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case_ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('global_attention_mask', dynamic_axis),
] )
@property
def _UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
snake_case_ = super().outputs
if self.task == "default":
snake_case_ = {0: 'batch'}
return outputs
@property
def _UpperCamelCase ( self ) -> float:
return 1E-4
@property
def _UpperCamelCase ( self ) -> int:
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def _UpperCamelCase ( self , a , a = -1 , a = -1 , a = False , a = None , ) -> Mapping[str, Any]:
snake_case_ = super().generate_dummy_inputs(
preprocessor=a , batch_size=a , seq_length=a , is_pair=a , framework=a )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
snake_case_ = torch.zeros_like(inputs['input_ids'] )
# make every second token global
snake_case_ = 1
return inputs
| 178 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def __UpperCAmelCase ( a_ , a_ , a_ , a_ , a_):
snake_case_ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(a_)])
snake_case_ = np.array(a_)
snake_case_ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , a_)) , x.transpose()) , a_)
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])
def __UpperCAmelCase ( a_ , a_ , a_):
snake_case_ = (1, 2, 1)
snake_case_ = (1, 1, 0, 7)
snake_case_ = SARIMAX(
a_ , exog=a_ , order=a_ , seasonal_order=a_)
snake_case_ = model.fit(disp=a_ , maxiter=6_00 , method='nm')
snake_case_ = model_fit.predict(1 , len(a_) , exog=[test_match])
return result[0]
def __UpperCAmelCase ( a_ , a_ , a_):
snake_case_ = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1)
regressor.fit(a_ , a_)
snake_case_ = regressor.predict(a_)
return y_pred[0]
def __UpperCAmelCase ( a_):
train_user.sort()
snake_case_ = np.percentile(a_ , 25)
snake_case_ = np.percentile(a_ , 75)
snake_case_ = qa - qa
snake_case_ = qa - (iqr * 0.1)
return low_lim
def __UpperCAmelCase ( a_ , a_):
snake_case_ = 0
snake_case_ = 0
for i in list_vote:
if i > actual_result:
snake_case_ = not_safe + 1
else:
if abs(abs(a_) - abs(a_)) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
lowercase = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
lowercase = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
lowercase = Normalizer().fit_transform(data_input_df.values)
# split data
lowercase = normalize_df[:, 2].tolist()
lowercase = normalize_df[:, 0].tolist()
lowercase = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
lowercase = normalize_df[:, [1, 2]].tolist()
lowercase = x[: len(x) - 1]
lowercase = x[len(x) - 1 :]
# for linear regression & sarimax
lowercase = total_date[: len(total_date) - 1]
lowercase = total_user[: len(total_user) - 1]
lowercase = total_match[: len(total_match) - 1]
lowercase = total_date[len(total_date) - 1 :]
lowercase = total_user[len(total_user) - 1 :]
lowercase = total_match[len(total_match) - 1 :]
# voting system with forecasting
lowercase = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
lowercase = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 178 | 1 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('''socket.socket''' )
@patch('''builtins.open''' )
def a__ ( lowercase : Optional[int], lowercase : Optional[int] ) -> Any:
"""simple docstring"""
_UpperCamelCase = Mock()
_UpperCamelCase = conn, Mock()
_UpperCamelCase = iter([1, None] )
_UpperCamelCase = lambda lowercase : next(lowercase )
# ===== invoke =====
send_file(filename='''mytext.txt''', testing=lowercase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 367 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def a__ ( lowercase : Tuple ) -> Dict:
"""simple docstring"""
_UpperCamelCase = int(lowercase )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = t // 3600, (t // 60) % 60, t % 60
return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""
def a__ ( lowercase : List[Any], lowercase : Dict, lowercase : Optional[int], lowercase : Union[str, Any], lowercase : Any=300 ) -> Any:
"""simple docstring"""
return F"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def a__ ( lowercase : Optional[Any] ) -> Any:
"""simple docstring"""
_UpperCamelCase = '''<table border="1" class="dataframe">\n'''
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
_UpperCamelCase = F"""{elt:.6f}""" if isinstance(lowercase, lowercase ) else str(lowercase )
html_code += F""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : str = 5
_snake_case : Optional[int] = 0.2
def __init__( self : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Optional["NotebookTrainingTracker"] = None , lowerCAmelCase__ : int = 300 , ) -> int:
'''simple docstring'''
_UpperCamelCase = total
_UpperCamelCase = '''''' if prefix is None else prefix
_UpperCamelCase = leave
_UpperCamelCase = parent
_UpperCamelCase = width
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
def snake_case__ ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : str = None ) -> Dict:
'''simple docstring'''
_UpperCamelCase = value
if comment is not None:
_UpperCamelCase = comment
if self.last_value is None:
_UpperCamelCase = _UpperCamelCase = time.time()
_UpperCamelCase = _UpperCamelCase = value
_UpperCamelCase = _UpperCamelCase = None
_UpperCamelCase = self.warmup
_UpperCamelCase = 1
self.update_bar(lowerCAmelCase__ )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
_UpperCamelCase = time.time()
_UpperCamelCase = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
_UpperCamelCase = self.elapsed_time / (value - self.start_value)
else:
_UpperCamelCase = None
if value >= self.total:
_UpperCamelCase = self.total
_UpperCamelCase = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
_UpperCamelCase = self.average_time_per_item * (self.total - value)
self.update_bar(lowerCAmelCase__ )
_UpperCamelCase = value
_UpperCamelCase = current_time
if self.average_time_per_item is None:
_UpperCamelCase = 1
else:
_UpperCamelCase = max(int(self.update_every / self.average_time_per_item ) , 1 )
def snake_case__ ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : Tuple=None ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = ''' ''' * (len(str(self.total ) ) - len(str(lowerCAmelCase__ ) )) + str(lowerCAmelCase__ )
if self.elapsed_time is None:
_UpperCamelCase = f"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
_UpperCamelCase = f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"""
else:
_UpperCamelCase = (
f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"""
f""" {format_time(self.predicted_remaining )}"""
)
self.label += f""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f""", {self.comment}]"""
self.display()
def snake_case__ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
_UpperCamelCase = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
_UpperCamelCase = disp.display(disp.HTML(self.html_code ) , display_id=lowerCAmelCase__ )
else:
self.output.update(disp.HTML(self.html_code ) )
def snake_case__ ( self : Tuple ) -> Any:
'''simple docstring'''
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('''''' ) )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any]=None ) -> Dict:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
_UpperCamelCase = None if column_names is None else [column_names]
_UpperCamelCase = None
def snake_case__ ( self : List[Any] ) -> Any:
'''simple docstring'''
_UpperCamelCase = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
_UpperCamelCase = disp.display(disp.HTML(self.html_code ) , display_id=lowerCAmelCase__ )
else:
self.output.update(disp.HTML(self.html_code ) )
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : int ) -> Union[str, Any]:
'''simple docstring'''
if self.inner_table is None:
_UpperCamelCase = [list(values.keys() ), list(values.values() )]
else:
_UpperCamelCase = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(lowerCAmelCase__ )
_UpperCamelCase = columns
self.inner_table.append([values[c] for c in columns] )
def snake_case__ ( self : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : List[str]=300 ) -> int:
'''simple docstring'''
_UpperCamelCase = NotebookProgressBar(lowerCAmelCase__ , prefix=lowerCAmelCase__ , parent=self , width=lowerCAmelCase__ )
return self.child_bar
def snake_case__ ( self : Any ) -> str:
'''simple docstring'''
_UpperCamelCase = None
self.display()
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self : str ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = False
def snake_case__ ( self : List[str] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : str , **lowerCAmelCase__ : Any ) -> Dict:
'''simple docstring'''
_UpperCamelCase = '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = [self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''' )
_UpperCamelCase = NotebookTrainingTracker(state.max_steps , lowerCAmelCase__ )
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict , **lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
_UpperCamelCase = int(state.epoch ) if int(state.epoch ) == state.epoch else f"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1 , comment=f"""Epoch {epoch}/{state.num_train_epochs}""" , force_update=self._force_next_update , )
_UpperCamelCase = False
def snake_case__ ( self : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any]=None , **lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
if not has_length(lowerCAmelCase__ ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
_UpperCamelCase = self.training_tracker.add_child(len(lowerCAmelCase__ ) )
else:
_UpperCamelCase = NotebookProgressBar(len(lowerCAmelCase__ ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def snake_case__ ( self : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , **lowerCAmelCase__ : Any ) -> Optional[int]:
'''simple docstring'''
if self.prediction_bar is not None:
self.prediction_bar.close()
_UpperCamelCase = None
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int]=None , **lowerCAmelCase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
_UpperCamelCase = {'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
_UpperCamelCase = state.global_step
self.training_tracker.write_line(lowerCAmelCase__ )
def snake_case__ ( self : Optional[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]=None , **lowerCAmelCase__ : List[str] ) -> List[str]:
'''simple docstring'''
if self.training_tracker is not None:
_UpperCamelCase = {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history ):
if "loss" in log:
_UpperCamelCase = log['''loss''']
break
if self.first_column == "Epoch":
_UpperCamelCase = int(state.epoch )
else:
_UpperCamelCase = state.global_step
_UpperCamelCase = '''eval'''
for k in metrics:
if k.endswith('''_loss''' ):
_UpperCamelCase = re.sub(r'''\_loss$''' , '''''' , lowerCAmelCase__ )
_UpperCamelCase = metrics.pop('''total_flos''' , lowerCAmelCase__ )
_UpperCamelCase = metrics.pop('''epoch''' , lowerCAmelCase__ )
_UpperCamelCase = metrics.pop(f"""{metric_key_prefix}_runtime""" , lowerCAmelCase__ )
_UpperCamelCase = metrics.pop(f"""{metric_key_prefix}_samples_per_second""" , lowerCAmelCase__ )
_UpperCamelCase = metrics.pop(f"""{metric_key_prefix}_steps_per_second""" , lowerCAmelCase__ )
_UpperCamelCase = metrics.pop(f"""{metric_key_prefix}_jit_compilation_time""" , lowerCAmelCase__ )
for k, v in metrics.items():
if k == f"""{metric_key_prefix}_loss""":
_UpperCamelCase = v
else:
_UpperCamelCase = k.split('''_''' )
_UpperCamelCase = ''' '''.join([part.capitalize() for part in splits[1:]] )
_UpperCamelCase = v
self.training_tracker.write_line(lowerCAmelCase__ )
self.training_tracker.remove_child()
_UpperCamelCase = None
# Evaluation takes a long time so we should force the next update.
_UpperCamelCase = True
def snake_case__ ( self : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , **lowerCAmelCase__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
self.training_tracker.update(
state.global_step , comment=f"""Epoch {int(state.epoch )}/{state.num_train_epochs}""" , force_update=lowerCAmelCase__ )
_UpperCamelCase = None
| 287 | 0 |
def a ( A__ : int = 1000 ) -> int:
"""simple docstring"""
_lowercase =3
_lowercase =0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 205 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase_ = data_utils.TransfoXLTokenizer
lowercase_ = data_utils.TransfoXLCorpus
lowercase_ = data_utils
lowercase_ = data_utils
def a ( A__ : int , A__ : Dict , A__ : Union[str, Any] , A__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(A__ , 'rb' ) as fp:
_lowercase =pickle.load(A__ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
_lowercase =pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F'''Save vocabulary to {pytorch_vocab_dump_path}''' )
_lowercase =corpus.vocab.__dict__
torch.save(A__ , A__ )
_lowercase =corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , A__ )
_lowercase =pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F'''Save dataset to {pytorch_dataset_dump_path}''' )
torch.save(A__ , A__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
_lowercase =os.path.abspath(A__ )
_lowercase =os.path.abspath(A__ )
print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
_lowercase =TransfoXLConfig()
else:
_lowercase =TransfoXLConfig.from_json_file(A__ )
print(F'''Building PyTorch model from configuration: {config}''' )
_lowercase =TransfoXLLMHeadModel(A__ )
_lowercase =load_tf_weights_in_transfo_xl(A__ , A__ , A__ )
# Save pytorch-model
_lowercase =os.path.join(A__ , A__ )
_lowercase =os.path.join(A__ , A__ )
print(F'''Save PyTorch model to {os.path.abspath(A__ )}''' )
torch.save(model.state_dict() , A__ )
print(F'''Save configuration file to {os.path.abspath(A__ )}''' )
with open(A__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the folder to store the PyTorch model or dataset/vocab.',
)
parser.add_argument(
'--tf_checkpoint_path',
default='',
type=str,
help='An optional path to a TensorFlow checkpoint path to be converted.',
)
parser.add_argument(
'--transfo_xl_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--transfo_xl_dataset_file',
default='',
type=str,
help='An optional dataset file to be converted in a vocabulary.',
)
lowercase_ = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 205 | 1 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(_SCREAMING_SNAKE_CASE , x % y )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return (x * y) // greatest_common_divisor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def a__ ( _SCREAMING_SNAKE_CASE = 20 ):
"""simple docstring"""
UpperCamelCase = 1
for i in range(1 , n + 1 ):
UpperCamelCase = lcm(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return g
if __name__ == "__main__":
print(f'''{solution() = }''')
| 350 |
"""simple docstring"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return (position - 1) // 2
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return (2 * position) + 1
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return (2 * position) + 2
class _lowerCamelCase ( Generic[T] ):
def __init__(self ) -> None:
UpperCamelCase = []
UpperCamelCase = {}
UpperCamelCase = 0
def __len__(self ) -> int:
return self.elements
def __repr__(self ) -> str:
return str(self.heap )
def snake_case_ (self ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def snake_case_ (self , __a , __a ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
UpperCamelCase = self.elements
self.elements += 1
self._bubble_up(__a )
def snake_case_ (self ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
UpperCamelCase , UpperCamelCase = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
UpperCamelCase , UpperCamelCase = self.heap[0]
self._bubble_down(__a )
return elem
def snake_case_ (self , __a , __a ) -> None:
# Update the weight of the given key
UpperCamelCase = self.position_map[elem]
UpperCamelCase = (elem, weight)
if position > 0:
UpperCamelCase = get_parent_position(__a )
UpperCamelCase , UpperCamelCase = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(__a )
else:
self._bubble_down(__a )
else:
self._bubble_down(__a )
def snake_case_ (self , __a ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
UpperCamelCase = self.position_map[elem]
if curr_pos == 0:
return None
UpperCamelCase = get_parent_position(__a )
UpperCamelCase , UpperCamelCase = self.heap[curr_pos]
UpperCamelCase , UpperCamelCase = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(__a , __a )
return self._bubble_up(__a )
return None
def snake_case_ (self , __a ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
UpperCamelCase = self.position_map[elem]
UpperCamelCase , UpperCamelCase = self.heap[curr_pos]
UpperCamelCase = get_child_left_position(__a )
UpperCamelCase = get_child_right_position(__a )
if child_left_position < self.elements and child_right_position < self.elements:
UpperCamelCase , UpperCamelCase = self.heap[child_left_position]
UpperCamelCase , UpperCamelCase = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(__a , __a )
return self._bubble_down(__a )
if child_left_position < self.elements:
UpperCamelCase , UpperCamelCase = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(__a , __a )
return self._bubble_down(__a )
else:
return None
if child_right_position < self.elements:
UpperCamelCase , UpperCamelCase = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(__a , __a )
return self._bubble_down(__a )
return None
def snake_case_ (self , __a , __a ) -> None:
# Swap the nodes at the given positions
UpperCamelCase = self.heap[nodea_pos][0]
UpperCamelCase = self.heap[nodea_pos][0]
UpperCamelCase , UpperCamelCase = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
UpperCamelCase = nodea_pos
UpperCamelCase = nodea_pos
class _lowerCamelCase ( Generic[T] ):
def __init__(self ) -> None:
UpperCamelCase = {}
UpperCamelCase = 0
def __repr__(self ) -> str:
return str(self.connections )
def __len__(self ) -> int:
return self.nodes
def snake_case_ (self , __a ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
UpperCamelCase = {}
self.nodes += 1
def snake_case_ (self , __a , __a , __a ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(__a )
self.add_node(__a )
UpperCamelCase = weight
UpperCamelCase = weight
def a__ ( _SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
UpperCamelCase = {node: maxsize for node in graph.connections}
UpperCamelCase = {node: None for node in graph.connections}
UpperCamelCase = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if priority_queue.is_empty():
return dist, parent
# initialization
UpperCamelCase = priority_queue.extract_min()
UpperCamelCase = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCamelCase = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_SCREAMING_SNAKE_CASE , dist[neighbour] )
UpperCamelCase = node
# running prim's algorithm
while not priority_queue.is_empty():
UpperCamelCase = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCamelCase = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_SCREAMING_SNAKE_CASE , dist[neighbour] )
UpperCamelCase = node
return dist, parent
| 244 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """vocab.txt"""}
__snake_case = {
"""vocab_file""": {
"""facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
"""facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
},
}
__snake_case = {
"""facebook/esm2_t6_8M_UR50D""": 1024,
"""facebook/esm2_t12_35M_UR50D""": 1024,
}
def __lowerCAmelCase ( lowercase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(lowercase , "r" ) as f:
snake_case : str = f.read().splitlines()
return [l.strip() for l in lines]
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : Any = VOCAB_FILES_NAMES
__UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : List[str] = ["input_ids", "attention_mask"]
def __init__( self , UpperCamelCase__ , UpperCamelCase__="<unk>" , UpperCamelCase__="<cls>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__="<eos>" , **UpperCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**snake_case__ )
snake_case : List[Any] = load_vocab_file(snake_case__ )
snake_case : Dict = dict(enumerate(self.all_tokens ) )
snake_case : Union[str, Any] = {tok: ind for ind, tok in enumerate(self.all_tokens )}
snake_case : Union[str, Any] = unk_token
snake_case : int = cls_token
snake_case : Any = pad_token
snake_case : Union[str, Any] = mask_token
snake_case : List[str] = eos_token
snake_case : str = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
return self._id_to_token.get(snake_case__ , self.unk_token )
def lowerCamelCase ( self , UpperCamelCase__ ) -> Any:
'''simple docstring'''
return self._token_to_id.get(snake_case__ , self._token_to_id.get(self.unk_token ) )
def lowerCamelCase ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
return text.split()
def lowerCamelCase ( self , UpperCamelCase__=False ) -> Dict:
'''simple docstring'''
return len(self._id_to_token )
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
return {token: i for i, token in enumerate(self.all_tokens )}
def lowerCamelCase ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
return self._token_to_id.get(snake_case__ , self._token_to_id.get(self.unk_token ) )
def lowerCamelCase ( self , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return self._id_to_token.get(snake_case__ , self.unk_token )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Optional[Any]:
'''simple docstring'''
snake_case : Tuple = [self.cls_token_id]
snake_case : Dict = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!" )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False ) -> Any:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
snake_case : List[str] = [1] + ([0] * len(snake_case__ )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case__ ) + [1]
return mask
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = os.path.join(snake_case__ , (filename_prefix + "-" if filename_prefix else "") + "vocab.txt" )
with open(snake_case__ , "w" ) as f:
f.write("\n".join(self.all_tokens ) )
return (vocab_file,)
@property
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
return self.get_vocab_size(with_added_tokens=snake_case__ )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Optional[int]:
'''simple docstring'''
return super()._add_tokens(snake_case__ , special_tokens=snake_case__ )
| 203 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0 )
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowerCAmelCase : set[int] = set()
lowerCAmelCase : int
lowerCAmelCase : int
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def a__ ( SCREAMING_SNAKE_CASE : int = 5_0_0_0 ):
'''simple docstring'''
for number_to_partition in range(1 , SCREAMING_SNAKE_CASE ):
if len(partition(SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"{solution() = }")
| 108 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class a_ ( a_ ):
'''simple docstring'''
def __init__( self , *lowercase_ , **lowercase_ ) -> None:
'''simple docstring'''
warnings.warn(
'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use FlavaImageProcessor instead.' , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_ )
| 14 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCamelCase ( a_ , a_ ) -> Tuple:
lowerCAmelCase_ = XCLIPTextConfig()
# derive patch size from model name
lowerCAmelCase_ = model_name.find('patch' )
lowerCAmelCase_ = int(model_name[start_idx + len('patch' ) : start_idx + len('patch' ) + 2] )
lowerCAmelCase_ = XCLIPVisionConfig(patch_size=a_ , num_frames=a_ )
if "large" in model_name:
lowerCAmelCase_ = 768
lowerCAmelCase_ = 3_072
lowerCAmelCase_ = 12
lowerCAmelCase_ = 1_024
lowerCAmelCase_ = 4_096
lowerCAmelCase_ = 16
lowerCAmelCase_ = 24
lowerCAmelCase_ = 768
lowerCAmelCase_ = 3_072
if model_name == "xclip-large-patch14-16-frames":
lowerCAmelCase_ = 336
lowerCAmelCase_ = XCLIPConfig.from_text_vision_configs(a_ , a_ )
if "large" in model_name:
lowerCAmelCase_ = 768
return config
def lowerCamelCase ( a_ ) -> List[str]:
# text encoder
if name == "token_embedding.weight":
lowerCAmelCase_ = name.replace('token_embedding.weight' , 'text_model.embeddings.token_embedding.weight' )
if name == "positional_embedding":
lowerCAmelCase_ = name.replace('positional_embedding' , 'text_model.embeddings.position_embedding.weight' )
if "ln_1" in name:
lowerCAmelCase_ = name.replace('ln_1' , 'layer_norm1' )
if "ln_2" in name:
lowerCAmelCase_ = name.replace('ln_2' , 'layer_norm2' )
if "c_fc" in name:
lowerCAmelCase_ = name.replace('c_fc' , 'fc1' )
if "c_proj" in name:
lowerCAmelCase_ = name.replace('c_proj' , 'fc2' )
if name.startswith('transformer.resblocks' ):
lowerCAmelCase_ = name.replace('transformer.resblocks' , 'text_model.encoder.layers' )
if "attn.out_proj" in name and "message" not in name:
lowerCAmelCase_ = name.replace('attn.out_proj' , 'self_attn.out_proj' )
if "ln_final" in name:
lowerCAmelCase_ = name.replace('ln_final' , 'text_model.final_layer_norm' )
# visual encoder
if name == "visual.class_embedding":
lowerCAmelCase_ = name.replace('visual.class_embedding' , 'vision_model.embeddings.class_embedding' )
if name == "visual.positional_embedding":
lowerCAmelCase_ = name.replace('visual.positional_embedding' , 'vision_model.embeddings.position_embedding.weight' )
if name.startswith('visual.transformer.resblocks' ):
lowerCAmelCase_ = name.replace('visual.transformer.resblocks' , 'vision_model.encoder.layers' )
if "visual.conv1" in name:
lowerCAmelCase_ = name.replace('visual.conv1' , 'vision_model.embeddings.patch_embedding' )
if "visual.ln_pre" in name:
lowerCAmelCase_ = name.replace('visual.ln_pre' , 'vision_model.pre_layernorm' )
if "visual.ln_post" in name:
lowerCAmelCase_ = name.replace('visual.ln_post' , 'vision_model.post_layernorm' )
if "visual.proj" in name:
lowerCAmelCase_ = name.replace('visual.proj' , 'visual_projection.weight' )
if "text_projection" in name:
lowerCAmelCase_ = name.replace('text_projection' , 'text_projection.weight' )
# things on top
if "prompts_visual_proj" in name:
lowerCAmelCase_ = name.replace('prompts_visual_proj' , 'prompts_visual_projection' )
if "prompts_visual_ln" in name:
lowerCAmelCase_ = name.replace('prompts_visual_ln' , 'prompts_visual_layernorm' )
# mit
if name == "mit.positional_embedding":
lowerCAmelCase_ = name.replace('positional' , 'position' )
if name.startswith('mit.resblocks' ):
lowerCAmelCase_ = name.replace('mit.resblocks' , 'mit.encoder.layers' )
# prompts generator
if name.startswith('prompts_generator.norm' ):
lowerCAmelCase_ = name.replace('prompts_generator.norm' , 'prompts_generator.layernorm' )
return name
def lowerCamelCase ( a_ , a_ ) -> Dict:
for key in orig_state_dict.copy().keys():
lowerCAmelCase_ = orig_state_dict.pop(a_ )
if "attn.in_proj" in key:
lowerCAmelCase_ = key.split('.' )
if key.startswith('visual' ):
lowerCAmelCase_ = key_split[3]
lowerCAmelCase_ = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
lowerCAmelCase_ = val[
:dim, :
]
lowerCAmelCase_ = val[
dim : dim * 2, :
]
lowerCAmelCase_ = val[
-dim:, :
]
else:
lowerCAmelCase_ = val[
:dim
]
lowerCAmelCase_ = val[
dim : dim * 2
]
lowerCAmelCase_ = val[
-dim:
]
else:
if "weight" in key:
lowerCAmelCase_ = val[
:dim, :
]
lowerCAmelCase_ = val[
dim : dim * 2, :
]
lowerCAmelCase_ = val[
-dim:, :
]
else:
lowerCAmelCase_ = val[:dim]
lowerCAmelCase_ = val[
dim : dim * 2
]
lowerCAmelCase_ = val[-dim:]
elif key.startswith('mit' ):
lowerCAmelCase_ = key_split[2]
lowerCAmelCase_ = config.vision_config.mit_hidden_size
if "weight" in key:
lowerCAmelCase_ = val[:dim, :]
lowerCAmelCase_ = val[dim : dim * 2, :]
lowerCAmelCase_ = val[-dim:, :]
else:
lowerCAmelCase_ = val[:dim]
lowerCAmelCase_ = val[dim : dim * 2]
lowerCAmelCase_ = val[-dim:]
else:
lowerCAmelCase_ = key_split[2]
lowerCAmelCase_ = config.text_config.hidden_size
if "weight" in key:
lowerCAmelCase_ = val[:dim, :]
lowerCAmelCase_ = val[
dim : dim * 2, :
]
lowerCAmelCase_ = val[-dim:, :]
else:
lowerCAmelCase_ = val[:dim]
lowerCAmelCase_ = val[
dim : dim * 2
]
lowerCAmelCase_ = val[-dim:]
else:
lowerCAmelCase_ = rename_key(a_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
lowerCAmelCase_ = val.T
lowerCAmelCase_ = val
return orig_state_dict
def lowerCamelCase ( a_ ) -> List[str]:
if num_frames == 8:
lowerCAmelCase_ = 'eating_spaghetti_8_frames.npy'
elif num_frames == 16:
lowerCAmelCase_ = 'eating_spaghetti.npy'
elif num_frames == 32:
lowerCAmelCase_ = 'eating_spaghetti_32_frames.npy'
lowerCAmelCase_ = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename=a_ , repo_type='dataset' , )
lowerCAmelCase_ = np.load(a_ )
return list(a_ )
def lowerCamelCase ( a_ , a_=None , a_=False ) -> List[Any]:
lowerCAmelCase_ = {
# fully supervised kinetics-400 checkpoints
'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
'xclip-base-patch32-16-frames': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
),
'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
'xclip-base-patch16-16-frames': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
),
'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f',
# fully supervised kinetics-600 checkpoints
'xclip-base-patch16-kinetics-600': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
),
'xclip-base-patch16-kinetics-600-16-frames': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
),
'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
# few shot
'xclip-base-patch16-hmdb-2-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
),
'xclip-base-patch16-hmdb-4-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
),
'xclip-base-patch16-hmdb-8-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
),
'xclip-base-patch16-hmdb-16-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
),
'xclip-base-patch16-ucf-2-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
),
'xclip-base-patch16-ucf-4-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
),
'xclip-base-patch16-ucf-8-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
),
'xclip-base-patch16-ucf-16-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
),
# zero shot
'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
}
lowerCAmelCase_ = model_to_url[model_name]
lowerCAmelCase_ = 8
if "16-frames" in model_name:
lowerCAmelCase_ = 16
elif "shot" in model_name:
lowerCAmelCase_ = 32
lowerCAmelCase_ = get_xclip_config(a_ , a_ )
lowerCAmelCase_ = XCLIPModel(a_ )
model.eval()
if "drive" in checkpoint_url:
lowerCAmelCase_ = 'pytorch_model.bin'
gdown.cached_download(a_ , a_ , quiet=a_ )
lowerCAmelCase_ = torch.load(a_ , map_location='cpu' )['model']
else:
lowerCAmelCase_ = torch.hub.load_state_dict_from_url(a_ )['model']
lowerCAmelCase_ = convert_state_dict(a_ , a_ )
lowerCAmelCase_ = XCLIPModel(a_ )
lowerCAmelCase_ , lowerCAmelCase_ = model.load_state_dict(a_ , strict=a_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
lowerCAmelCase_ = 336 if model_name == 'xclip-large-patch14-16-frames' else 224
lowerCAmelCase_ = VideoMAEImageProcessor(size=a_ )
lowerCAmelCase_ = CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32' )
lowerCAmelCase_ = CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32' )
lowerCAmelCase_ = XCLIPProcessor(image_processor=a_ , tokenizer=a_ )
lowerCAmelCase_ = prepare_video(a_ )
lowerCAmelCase_ = processor(
text=['playing sports', 'eating spaghetti', 'go shopping'] , videos=a_ , return_tensors='pt' , padding=a_ )
print('Shape of pixel values:' , inputs.pixel_values.shape )
with torch.no_grad():
lowerCAmelCase_ = model(**a_ )
# Verify outputs
lowerCAmelCase_ = outputs.logits_per_video
lowerCAmelCase_ = logits_per_video.softmax(dim=1 )
print('Probs:' , a_ )
# kinetics-400
if model_name == "xclip-base-patch32":
lowerCAmelCase_ = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
lowerCAmelCase_ = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
lowerCAmelCase_ = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
lowerCAmelCase_ = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
lowerCAmelCase_ = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
lowerCAmelCase_ = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
lowerCAmelCase_ = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
lowerCAmelCase_ = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
lowerCAmelCase_ = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
lowerCAmelCase_ = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
lowerCAmelCase_ = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
lowerCAmelCase_ = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
lowerCAmelCase_ = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
lowerCAmelCase_ = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
lowerCAmelCase_ = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
lowerCAmelCase_ = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
lowerCAmelCase_ = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
lowerCAmelCase_ = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F'''Model name {model_name} not supported''' )
assert torch.allclose(a_ , a_ , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(a_ )
if push_to_hub:
print('Pushing model, processor and slow tokenizer files to the hub...' )
model.push_to_hub(a_ , organization='nielsr' )
processor.push_to_hub(a_ , organization='nielsr' )
slow_tokenizer.push_to_hub(a_ , organization='nielsr' )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
lowerCamelCase_ = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 14 | 1 |
def __magic_name__ ( A : list ):
'''simple docstring'''
def merge(A : list, A : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(A ) <= 1:
return collection
a = len(A ) // 2
return merge(merge_sort(collection[:mid] ), merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase : List[Any] = input('Enter numbers separated by a comma:\n').strip()
__lowerCAmelCase : List[Any] = [int(item) for item in user_input.split(',')]
print(*merge_sort(unsorted), sep=',')
| 107 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
"ChineseCLIPOnnxConfig",
"ChineseCLIPTextConfig",
"ChineseCLIPVisionConfig",
],
"processing_chinese_clip": ["ChineseCLIPProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[Any] = ["ChineseCLIPFeatureExtractor"]
lowercase : List[Any] = ["ChineseCLIPImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Any = [
"CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ChineseCLIPModel",
"ChineseCLIPPreTrainedModel",
"ChineseCLIPTextModel",
"ChineseCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
lowercase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = """▁"""
__snake_case : Dict = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
}
__snake_case : Union[str, Any] = {
"""vocab_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
),
},
"""spm_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
)
},
}
__snake_case : Union[str, Any] = {
"""facebook/s2t-small-librispeech-asr""": 10_24,
}
__snake_case : Optional[Any] = ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]
__snake_case : Optional[int] = {"""mustc""": MUSTC_LANGS}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase__):
_SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Tuple = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : Union[str, Any] = MAX_MODEL_INPUT_SIZES
_SCREAMING_SNAKE_CASE : int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE : List[int] = []
def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase="<s>" , _UpperCamelCase="</s>" , _UpperCamelCase="<pad>" , _UpperCamelCase="<unk>" , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , do_upper_case=snake_case__ , do_lower_case=snake_case__ , tgt_lang=snake_case__ , lang_codes=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
lowerCAmelCase__ = do_upper_case
lowerCAmelCase__ = do_lower_case
lowerCAmelCase__ = load_json(snake_case__ )
lowerCAmelCase__ = {v: k for k, v in self.encoder.items()}
lowerCAmelCase__ = spm_file
lowerCAmelCase__ = load_spm(snake_case__ , self.sp_model_kwargs )
if lang_codes is not None:
lowerCAmelCase__ = lang_codes
lowerCAmelCase__ = LANGUAGES[lang_codes]
lowerCAmelCase__ = [F"<lang:{lang}>" for lang in self.langs]
lowerCAmelCase__ = {lang: self.sp_model.PieceToId(F"<lang:{lang}>" ) for lang in self.langs}
lowerCAmelCase__ = self.lang_tokens
lowerCAmelCase__ = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
lowerCAmelCase__ = {}
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return len(self.encoder )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = new_tgt_lang
self.set_tgt_lang_special_tokens(snake_case__ )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self.lang_code_to_id[tgt_lang]
lowerCAmelCase__ = [lang_code_id]
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return self.sp_model.encode(snake_case__ , out_type=snake_case__ )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return self.encoder.get(snake_case__ , self.encoder[self.unk_token] )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return self.decoder.get(snake_case__ , self.unk_token )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
lowerCAmelCase__ = self.sp_model.decode(snake_case__ )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
lowerCAmelCase__ = []
else:
current_sub_tokens.append(snake_case__ )
lowerCAmelCase__ = self.sp_model.decode(snake_case__ )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase=None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
lowerCAmelCase__ = [1] * len(self.prefix_tokens )
lowerCAmelCase__ = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(snake_case__ )) + suffix_ones
return prefix_ones + ([0] * len(snake_case__ )) + ([0] * len(snake_case__ )) + suffix_ones
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
lowerCAmelCase__ = self.__dict__.copy()
lowerCAmelCase__ = None
return state
def __setstate__( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowerCAmelCase__ = {}
lowerCAmelCase__ = load_spm(self.spm_file , self.sp_model_kwargs )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase = None ):
"""simple docstring"""
lowerCAmelCase__ = Path(snake_case__ )
assert save_dir.is_dir(), F"{save_directory} should be a directory"
lowerCAmelCase__ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
lowerCAmelCase__ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , snake_case__ )
if os.path.abspath(self.spm_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , snake_case__ )
elif not os.path.isfile(self.spm_file ):
with open(snake_case__ , 'wb' ) as fi:
lowerCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (str(snake_case__ ), str(snake_case__ ))
def _UpperCamelCase ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] ) -> sentencepiece.SentencePieceProcessor:
"""simple docstring"""
lowerCAmelCase__ = sentencepiece.SentencePieceProcessor(**__lowerCAmelCase )
spm.Load(str(__lowerCAmelCase ) )
return spm
def _UpperCamelCase ( UpperCamelCase_ : List[Any] ) -> Union[Dict, List]:
"""simple docstring"""
with open(__lowerCAmelCase , 'r' ) as f:
return json.load(__lowerCAmelCase )
def _UpperCamelCase ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Tuple ) -> None:
"""simple docstring"""
with open(__lowerCAmelCase , 'w' ) as f:
json.dump(__lowerCAmelCase , __lowerCAmelCase , indent=2 )
| 362 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__snake_case : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( __lowercase):
_SCREAMING_SNAKE_CASE : Tuple = ['''input_features''', '''is_longer''']
def __init__( self , _UpperCamelCase=64 , _UpperCamelCase=4_80_00 , _UpperCamelCase=4_80 , _UpperCamelCase=10 , _UpperCamelCase=10_24 , _UpperCamelCase=0.0 , _UpperCamelCase=False , _UpperCamelCase = 0 , _UpperCamelCase = 1_40_00 , _UpperCamelCase = None , _UpperCamelCase = "fusion" , _UpperCamelCase = "repeatpad" , **_UpperCamelCase , ):
"""simple docstring"""
super().__init__(
feature_size=_UpperCamelCase , sampling_rate=_UpperCamelCase , padding_value=_UpperCamelCase , return_attention_mask=_UpperCamelCase , **_UpperCamelCase , )
lowerCAmelCase__ = top_db
lowerCAmelCase__ = truncation
lowerCAmelCase__ = padding
lowerCAmelCase__ = fft_window_size
lowerCAmelCase__ = (fft_window_size >> 1) + 1
lowerCAmelCase__ = hop_length
lowerCAmelCase__ = max_length_s
lowerCAmelCase__ = max_length_s * sampling_rate
lowerCAmelCase__ = sampling_rate
lowerCAmelCase__ = frequency_min
lowerCAmelCase__ = frequency_max
lowerCAmelCase__ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_UpperCamelCase , min_frequency=_UpperCamelCase , max_frequency=_UpperCamelCase , sampling_rate=_UpperCamelCase , norm=_UpperCamelCase , mel_scale='htk' , )
lowerCAmelCase__ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_UpperCamelCase , min_frequency=_UpperCamelCase , max_frequency=_UpperCamelCase , sampling_rate=_UpperCamelCase , norm='slaney' , mel_scale='slaney' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = copy.deepcopy(self.__dict__ )
lowerCAmelCase__ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase = None ):
"""simple docstring"""
lowerCAmelCase__ = spectrogram(
_UpperCamelCase , window_function(self.fft_window_size , 'hann' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=_UpperCamelCase , log_mel='dB' , )
return log_mel_spectrogram.T
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase__ = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase__ = [0]
# randomly choose index for each part
lowerCAmelCase__ = np.random.choice(ranges[0] )
lowerCAmelCase__ = np.random.choice(ranges[1] )
lowerCAmelCase__ = np.random.choice(ranges[2] )
lowerCAmelCase__ = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase__ = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase__ = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase__ = torch.tensor(mel[None, None, :] )
lowerCAmelCase__ = torch.nn.functional.interpolate(
_UpperCamelCase , size=[chunk_frames, 64] , mode='bilinear' , align_corners=_UpperCamelCase )
lowerCAmelCase__ = mel_shrink[0][0].numpy()
lowerCAmelCase__ = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase__ = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase__ = len(_UpperCamelCase ) - max_length
lowerCAmelCase__ = np.random.randint(0 , overflow + 1 )
lowerCAmelCase__ = waveform[idx : idx + max_length]
lowerCAmelCase__ = self._np_extract_fbank_features(_UpperCamelCase , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase__ = self._np_extract_fbank_features(_UpperCamelCase , self.mel_filters )
lowerCAmelCase__ = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase__ = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
lowerCAmelCase__ = np.stack([mel, mel, mel, mel] , axis=0 )
lowerCAmelCase__ = False
else:
lowerCAmelCase__ = self._random_mel_fusion(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
lowerCAmelCase__ = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented" )
else:
lowerCAmelCase__ = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
lowerCAmelCase__ = int(max_length / len(_UpperCamelCase ) )
lowerCAmelCase__ = np.stack(np.tile(_UpperCamelCase , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase__ = int(max_length / len(_UpperCamelCase ) )
lowerCAmelCase__ = np.stack(np.tile(_UpperCamelCase , _UpperCamelCase ) )
lowerCAmelCase__ = np.pad(_UpperCamelCase , (0, max_length - waveform.shape[0]) , mode='constant' , constant_values=0 )
if truncation == "fusion":
lowerCAmelCase__ = self._np_extract_fbank_features(_UpperCamelCase , self.mel_filters )
lowerCAmelCase__ = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
lowerCAmelCase__ = self._np_extract_fbank_features(_UpperCamelCase , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ = truncation if truncation is not None else self.truncation
lowerCAmelCase__ = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
F" was sampled with {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
lowerCAmelCase__ = isinstance(_UpperCamelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}" )
lowerCAmelCase__ = is_batched_numpy or (
isinstance(_UpperCamelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase__ = [np.asarray(_UpperCamelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_UpperCamelCase , np.ndarray ):
lowerCAmelCase__ = np.asarray(_UpperCamelCase , dtype=np.floataa )
elif isinstance(_UpperCamelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase__ = [np.asarray(_UpperCamelCase )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase__ = [
self._get_input_mel(_UpperCamelCase , max_length if max_length else self.nb_max_samples , _UpperCamelCase , _UpperCamelCase )
for waveform in raw_speech
]
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for mel, longer in padded_inputs:
input_mel.append(_UpperCamelCase )
is_longer.append(_UpperCamelCase )
if truncation == "fusion" and sum(_UpperCamelCase ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase__ = np.random.randint(0 , len(_UpperCamelCase ) )
lowerCAmelCase__ = True
if isinstance(input_mel[0] , _UpperCamelCase ):
lowerCAmelCase__ = [np.asarray(_UpperCamelCase , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase__ = [[longer] for longer in is_longer]
lowerCAmelCase__ = {'input_features': input_mel, 'is_longer': is_longer}
lowerCAmelCase__ = BatchFeature(_UpperCamelCase )
if return_tensors is not None:
lowerCAmelCase__ = input_features.convert_to_tensors(_UpperCamelCase )
return input_features
| 122 | 0 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Union[str, Any], UpperCAmelCase__ : str ):
__lowercase = 3
__lowercase = 2_5_0
__lowercase = ids_tensor((batch_size, length), UpperCAmelCase__ )
__lowercase = torch.ones((batch_size, length), device=UpperCAmelCase__, dtype=torch.float ) / length
return input_ids, scores
def _lowercase ( self : List[Any] ):
__lowercase ,__lowercase = self._get_tensors(5 )
__lowercase = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=1_0 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase ,__lowercase = self._get_tensors(9 )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase ,__lowercase = self._get_tensors(1_0 )
self.assertTrue(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
def _lowercase ( self : Any ):
__lowercase = MaxLengthCriteria(max_length=1_0 )
__lowercase ,__lowercase = self._get_tensors(5 )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase ,__lowercase = self._get_tensors(9 )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase ,__lowercase = self._get_tensors(1_0 )
self.assertTrue(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
def _lowercase ( self : List[Any] ):
__lowercase = MaxNewTokensCriteria(start_length=5, max_new_tokens=5 )
__lowercase ,__lowercase = self._get_tensors(5 )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase ,__lowercase = self._get_tensors(9 )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase ,__lowercase = self._get_tensors(1_0 )
self.assertTrue(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length, 1_0 )
def _lowercase ( self : Optional[int] ):
__lowercase ,__lowercase = self._get_tensors(5 )
__lowercase = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
__lowercase = MaxTimeCriteria(max_time=0.1, initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(UpperCAmelCase__, UpperCAmelCase__ ) )
def _lowercase ( self : int ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] ), 1_0 )
with self.assertWarns(UpperCAmelCase__ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] ), 1_1 )
__lowercase = validate_stopping_criteria(StoppingCriteriaList(), 1_1 )
self.assertEqual(len(UpperCAmelCase__ ), 1 )
| 17 | import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowerCAmelCase : str = None , lowerCAmelCase : str = None , lowerCAmelCase : str = None , ):
"""simple docstring"""
if config_name_or_path is None:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"
if generator_tokenizer_name_or_path is None:
SCREAMING_SNAKE_CASE_ : Dict = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = question_encoder_name_or_path
SCREAMING_SNAKE_CASE_ : Union[str, Any] = RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration
# Save model.
SCREAMING_SNAKE_CASE_ : List[Any] = RagConfig.from_pretrained(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = AutoConfig.from_pretrained(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : int = AutoConfig.from_pretrained(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = gen_config
SCREAMING_SNAKE_CASE_ : Optional[Any] = question_encoder_config
SCREAMING_SNAKE_CASE_ : Dict = model_class.from_pretrained_question_encoder_generator(
lowerCAmelCase , lowerCAmelCase , config=lowerCAmelCase )
rag_model.save_pretrained(lowerCAmelCase )
# Sanity check.
model_class.from_pretrained(lowerCAmelCase )
# Save tokenizers.
SCREAMING_SNAKE_CASE_ : Optional[Any] = AutoTokenizer.from_pretrained(lowerCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/" )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/" )
if __name__ == "__main__":
__lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''',
choices=['''rag_sequence''', '''rag_token'''],
required=True,
type=str,
help='''RAG model type: rag_sequence, rag_token''',
)
parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
parser.add_argument(
'''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
)
parser.add_argument(
'''--generator_tokenizer_name_or_path''',
type=str,
help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
)
parser.add_argument(
'''--question_encoder_tokenizer_name_or_path''',
type=str,
help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
)
parser.add_argument(
'''--config_name_or_path''',
type=str,
help=(
'''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
''' ``model_type``'''
),
)
__lowerCamelCase : str = parser.parse_args()
__lowerCamelCase : int = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 18 | 0 |
"""simple docstring"""
import numpy as np
class a_ :
'''simple docstring'''
def __init__(self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = (0, 0)
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Optional[Any] = 0
lowerCamelCase__ : Optional[int] = 0
lowerCamelCase__ : Dict = 0
def __eq__(self, lowerCamelCase_ ):
'''simple docstring'''
return self.position == cell.position
def a__ (self ):
'''simple docstring'''
print(self.position )
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_=(5, 5) ):
'''simple docstring'''
lowerCamelCase__ : str = np.zeros(lowerCamelCase_ )
lowerCamelCase__ : Any = world_size[0]
lowerCamelCase__ : Optional[int] = world_size[1]
def a__ (self ):
'''simple docstring'''
print(self.w )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
lowerCamelCase__ : Dict = cell.position[0]
lowerCamelCase__ : Dict = cell.position[1]
lowerCamelCase__ : List[str] = []
for n in neughbour_cord:
lowerCamelCase__ : str = current_x + n[0]
lowerCamelCase__ : List[str] = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
lowerCamelCase__ : Union[str, Any] = Cell()
lowerCamelCase__ : Any = (x, y)
lowerCamelCase__ : List[str] = cell
neighbours.append(lowerCamelCase_ )
return neighbours
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : str = []
lowerCamelCase__ : str = []
_open.append(_lowerCamelCase )
while _open:
lowerCamelCase__ : List[Any] = np.argmin([n.f for n in _open] )
lowerCamelCase__ : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowerCamelCase ) )
if current == goal:
break
for n in world.get_neigbours(_lowerCamelCase ):
for c in _closed:
if c == n:
continue
lowerCamelCase__ : List[str] = current.g + 1
lowerCamelCase__ , lowerCamelCase__ : str = n.position
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = goal.position
lowerCamelCase__ : Dict = (ya - ya) ** 2 + (xa - xa) ** 2
lowerCamelCase__ : str = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowerCamelCase )
lowerCamelCase__ : Tuple = []
while current.parent is not None:
path.append(current.position )
lowerCamelCase__ : List[str] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
A_ : Optional[Any] = Gridworld()
# Start position and goal
A_ : Optional[Any] = Cell()
A_ : Optional[Any] = (0, 0)
A_ : Union[str, Any] = Cell()
A_ : Union[str, Any] = (4, 4)
print(f"path from {start.position} to {goal.position}")
A_ : Tuple = astar(world, start, goal)
# Just for visual reasons.
for i in s:
A_ : Any = 1
print(world.w)
| 316 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ : Any = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 1_28,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def a__ (cls ):
'''simple docstring'''
lowerCamelCase__ : Tuple = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def a__ (cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='test-dynamic-config' )
except HTTPError:
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('test-config', use_auth_token=self._token )
lowerCamelCase__ : List[str] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_, repo_id='test-config', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : List[Any] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org', use_auth_token=self._token )
lowerCamelCase__ : int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_, repo_id='valid_org/test-config-org', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : Tuple = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
lowerCamelCase__ : str = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config', use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {'AutoConfig': 'custom_configuration.CustomConfig'} )
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''', trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, 'CustomConfig' )
self.assertEqual(new_config.attribute, 4_2 )
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowerCamelCase__ : Union[str, Any] = c.n_embd + 1 # int
lowerCamelCase__ : Optional[Any] = c.resid_pdrop + 1.0 # float
lowerCamelCase__ : str = not c.scale_attn_weights # bool
lowerCamelCase__ : Any = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCamelCase_, c.n_embd, 'mismatch for key: n_embd' )
self.assertEqual(lowerCamelCase_, c.resid_pdrop, 'mismatch for key: resid_pdrop' )
self.assertEqual(lowerCamelCase_, c.scale_attn_weights, 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowerCamelCase_, c.summary_type, 'mismatch for key: summary_type' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = PretrainedConfig()
lowerCamelCase__ : Union[str, Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase_, ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
lowerCamelCase__ : str = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase_, lowerCamelCase_ )]
if len(lowerCamelCase_ ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {', '.join(lowerCamelCase_ )}.''' )
def a__ (self ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
lowerCamelCase__ : Any = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder', subfolder='bert' )
self.assertIsNotNone(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = mock.Mock()
lowerCamelCase__ : str = 5_0_0
lowerCamelCase__ : Union[str, Any] = {}
lowerCamelCase__ : Any = HTTPError
lowerCamelCase__ : str = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ : Dict = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request', return_value=lowerCamelCase_ ) as mock_head:
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = AutoConfig.from_pretrained('bert-base-cased' )
lowerCamelCase__ : Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Tuple = 2
json.dump(configuration.to_dict(), open(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowerCamelCase__ : Optional[Any] = ['config.42.0.0.json']
lowerCamelCase__ : List[Any] = 7_6_8
configuration.save_pretrained(lowerCamelCase_ )
shutil.move(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), os.path.join(lowerCamelCase_, 'config.42.0.0.json' ) )
lowerCamelCase__ : str = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 7_6_8 )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
lowerCamelCase__ : Dict = 'v4.0.0'
lowerCamelCase__ , lowerCamelCase__ : str = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase_, return_unused_kwargs=lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase_, {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowerCamelCase__ : Optional[Any] = 'v3.0.0'
lowerCamelCase__ : Optional[int] = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(old_configuration.hidden_size, 7_6_8 )
| 316 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : str ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__lowerCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 223 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int = 1_00 ):
lowercase_ :Tuple = n * (n + 1) * (2 * n + 1) / 6
lowercase_ :List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 223 | 1 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: str ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : list[list[str]] = [[] for _ in range(__UpperCamelCase )]
SCREAMING_SNAKE_CASE : Tuple = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(__UpperCamelCase ) <= key:
return input_string
for position, character in enumerate(__UpperCamelCase ):
SCREAMING_SNAKE_CASE : Optional[int] = position % (lowest * 2) # puts it in bounds
SCREAMING_SNAKE_CASE : Dict = min(__UpperCamelCase ,lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(__UpperCamelCase )
SCREAMING_SNAKE_CASE : str = [''.join(__UpperCamelCase ) for row in temp_grid]
SCREAMING_SNAKE_CASE : Any = ''.join(__UpperCamelCase )
return output_string
def lowercase__( __UpperCamelCase: str ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : int = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1:
return input_string
SCREAMING_SNAKE_CASE : list[list[str]] = [[] for _ in range(__UpperCamelCase )] # generates template
for position in range(len(__UpperCamelCase ) ):
SCREAMING_SNAKE_CASE : Tuple = position % (lowest * 2) # puts it in bounds
SCREAMING_SNAKE_CASE : int = min(__UpperCamelCase ,lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('*' )
SCREAMING_SNAKE_CASE : Tuple = 0
for row in temp_grid: # fills in the characters
SCREAMING_SNAKE_CASE : List[str] = input_string[counter : counter + len(__UpperCamelCase )]
grid.append(list(__UpperCamelCase ) )
counter += len(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = '' # reads as zigzag
for position in range(len(__UpperCamelCase ) ):
SCREAMING_SNAKE_CASE : Tuple = position % (lowest * 2) # puts it in bounds
SCREAMING_SNAKE_CASE : Any = min(__UpperCamelCase ,lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def lowercase__( __UpperCamelCase: str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = {}
for key_guess in range(1 ,len(__UpperCamelCase ) ): # tries every key
SCREAMING_SNAKE_CASE : Optional[Any] = decrypt(__UpperCamelCase ,__UpperCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 246 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar("T")
class _a ( Generic[T] ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = data
SCREAMING_SNAKE_CASE : int = self
SCREAMING_SNAKE_CASE : Optional[Any] = 0
class _a ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : dict[T, DisjointSetTreeNode[T]] = {}
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = DisjointSetTreeNode(A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.map[data]
if elem_ref != elem_ref.parent:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def UpperCamelCase_ ( self, A, A ):
'''simple docstring'''
if nodea.rank > nodea.rank:
SCREAMING_SNAKE_CASE : Optional[int] = nodea
else:
SCREAMING_SNAKE_CASE : Optional[int] = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def UpperCamelCase_ ( self, A, A ):
'''simple docstring'''
self.link(self.find_set(A ), self.find_set(A ) )
class _a ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : dict[T, dict[T, int]] = {}
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if node not in self.connections:
SCREAMING_SNAKE_CASE : Dict = {}
def UpperCamelCase_ ( self, A, A, A ):
'''simple docstring'''
self.add_node(A )
self.add_node(A )
SCREAMING_SNAKE_CASE : Dict = weight
SCREAMING_SNAKE_CASE : Optional[int] = weight
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : int = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda A : x[2] )
# creating the disjoint set
SCREAMING_SNAKE_CASE : int = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(A )
# MST generation
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : Dict = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = edges[index]
index += 1
SCREAMING_SNAKE_CASE : List[Any] = disjoint_set.find_set(A )
SCREAMING_SNAKE_CASE : Tuple = disjoint_set.find_set(A )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(A, A, A )
disjoint_set.union(A, A )
return graph
| 246 | 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 lowerCamelCase_ ( __a , unittest.TestCase ):
lowerCAmelCase__ = LEDTokenizer
lowerCAmelCase__ = LEDTokenizerFast
lowerCAmelCase__ = True
def lowercase_ ( self : int ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : List[Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
UpperCAmelCase__ : Optional[Any] = dict(zip(_A , range(len(_A ) ) ) )
UpperCAmelCase__ : Union[str, Any] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
UpperCAmelCase__ : Any = {'''unk_token''': '''<unk>'''}
UpperCAmelCase__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase__ : Tuple = 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(_A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_A ) )
def lowercase_ ( self : Optional[int] , **_A : Any ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_A )
def lowercase_ ( self : Union[str, Any] , **_A : Optional[Any] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **_A )
def lowercase_ ( self : Tuple , _A : List[str] ):
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
return LEDTokenizer.from_pretrained('''allenai/led-base-16384''' )
@cached_property
def lowercase_ ( self : Any ):
'''simple docstring'''
return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''' )
@require_torch
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase__ : Dict = [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ : Union[str, Any] = tokenizer(_A , max_length=len(_A ) , padding=_A , return_tensors='''pt''' )
self.assertIsInstance(_A , _A )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
UpperCAmelCase__ : int = batch.input_ids.tolist()[0]
self.assertListEqual(_A , _A )
@require_torch
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ : List[str] = tokenizer(_A , padding=_A , return_tensors='''pt''' )
self.assertIn('''input_ids''' , _A )
self.assertIn('''attention_mask''' , _A )
self.assertNotIn('''labels''' , _A )
self.assertNotIn('''decoder_attention_mask''' , _A )
@require_torch
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [
'''Summary of the text.''',
'''Another summary.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ : Optional[Any] = tokenizer(text_target=_A , max_length=32 , padding='''max_length''' , return_tensors='''pt''' )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
@require_torch
def lowercase_ ( self : Tuple ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ : Any = tokenizer(
['''I am a small frog''' * 1_024, '''I am a small frog'''] , padding=_A , truncation=_A , return_tensors='''pt''' )
self.assertIsInstance(_A , _A )
self.assertEqual(batch.input_ids.shape , (2, 5_122) )
@require_torch
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Any = ['''A long paragraph for summarization.''']
UpperCAmelCase__ : List[Any] = [
'''Summary of the text.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ : Optional[Any] = tokenizer(_A , return_tensors='''pt''' )
UpperCAmelCase__ : int = tokenizer(text_target=_A , return_tensors='''pt''' )
UpperCAmelCase__ : str = inputs['''input_ids''']
UpperCAmelCase__ : Tuple = 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 lowercase_ ( self : List[str] ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ : Tuple = ['''Summary of the text.''', '''Another summary.''']
UpperCAmelCase__ : List[str] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
UpperCAmelCase__ : List[str] = tokenizer(_A , padding=_A )
UpperCAmelCase__ : str = [[0] * len(_A ) for x in encoded_output['''input_ids''']]
UpperCAmelCase__ : Any = tokenizer.pad(_A )
self.assertSequenceEqual(outputs['''global_attention_mask'''] , _A )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowercase_ ( self : Dict ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase__ : Optional[int] = self.rust_tokenizer_class.from_pretrained(_A , **_A )
UpperCAmelCase__ : int = self.tokenizer_class.from_pretrained(_A , **_A )
UpperCAmelCase__ : Any = '''A, <mask> AllenNLP sentence.'''
UpperCAmelCase__ : Dict = tokenizer_r.encode_plus(_A , add_special_tokens=_A , return_token_type_ids=_A )
UpperCAmelCase__ : Optional[int] = tokenizer_p.encode_plus(_A , add_special_tokens=_A , return_token_type_ids=_A )
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'''] ) , )
UpperCAmelCase__ : str = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
UpperCAmelCase__ : Tuple = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
_A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
_A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
| 181 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase_ ( __a ):
def __init__( self : Any , _A : int , _A : str ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=_A , scheduler=_A )
@torch.no_grad()
def __call__( self : Union[str, Any] , _A : int = 1 , _A : int = 100 , _A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _A : Optional[float] = None , _A : bool = True , ):
'''simple docstring'''
if audio_length_in_s is None:
UpperCAmelCase__ : List[str] = self.unet.config.sample_size / self.unet.config.sample_rate
UpperCAmelCase__ : Dict = audio_length_in_s * self.unet.config.sample_rate
UpperCAmelCase__ : Union[str, Any] = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
UpperCAmelCase__ : Optional[Any] = int(_A )
if sample_size % down_scale_factor != 0:
UpperCAmelCase__ : List[str] = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
''' process.''' )
UpperCAmelCase__ : Union[str, Any] = int(_A )
UpperCAmelCase__ : Any = next(iter(self.unet.parameters() ) ).dtype
UpperCAmelCase__ : Union[str, Any] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(_A , _A ) and len(_A ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
UpperCAmelCase__ : int = randn_tensor(_A , generator=_A , device=self.device , dtype=_A )
# set step values
self.scheduler.set_timesteps(_A , device=audio.device )
UpperCAmelCase__ : Union[str, Any] = self.scheduler.timesteps.to(_A )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
UpperCAmelCase__ : Any = self.unet(_A , _A ).sample
# 2. compute previous image: x_t -> t_t-1
UpperCAmelCase__ : Union[str, Any] = self.scheduler.step(_A , _A , _A ).prev_sample
UpperCAmelCase__ : Any = audio.clamp(-1 , 1 ).float().cpu().numpy()
UpperCAmelCase__ : List[str] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=_A )
| 181 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def a__ ( lowercase : float, lowercase : float, lowercase : float ) -> tuple[float, float, float]:
"""simple docstring"""
_UpperCamelCase = point_y / 4 / point_x
_UpperCamelCase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
_UpperCamelCase = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
_UpperCamelCase = (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
_UpperCamelCase = outgoing_gradient**2 + 4
_UpperCamelCase = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
_UpperCamelCase = (point_y - outgoing_gradient * point_x) ** 2 - 100
_UpperCamelCase = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
_UpperCamelCase = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
_UpperCamelCase = x_minus if isclose(lowercase, lowercase ) else x_plus
_UpperCamelCase = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def a__ ( lowercase : float = 1.4, lowercase : float = -9.6 ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = first_x_coord
_UpperCamelCase = first_y_coord
_UpperCamelCase = (1_0.1 - point_y) / (0.0 - point_x)
while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = next_point(lowercase, lowercase, lowercase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"""{solution() = }""")
| 287 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowercase__ : Any = logging.getLogger(__name__)
def a__ ( lowercase : Optional[Any], lowercase : Tuple ) -> Any:
"""simple docstring"""
return (preds == labels).mean()
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_snake_case : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_snake_case : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_snake_case : Optional[str] = field(
default=__magic_name__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
_snake_case : str = field(metadata={'help': 'Should contain the data files for the task.'} )
_snake_case : int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_snake_case : bool = field(
default=__magic_name__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def a__ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1 ), training_args.fpaa, )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''', lowercase )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(lowercase )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path ), config=lowercase, cache_dir=model_args.cache_dir, )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
if training_args.do_eval
else None
)
def compute_metrics(lowercase : EvalPrediction ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions, axis=1 )
return {"acc": simple_accuracy(lowercase, p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(lowercase, pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir, '''eval_results.txt''' )
if trainer.is_world_master():
with open(lowercase, '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''', lowercase, lowercase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(lowercase )
return results
def a__ ( lowercase : Tuple ) -> List[Any]:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 287 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_lowerCamelCase : str = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : int , *UpperCAmelCase__ : Dict , **UpperCAmelCase__ : List[Any]) ->None:
'''simple docstring'''
warnings.warn(
'''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use FlavaImageProcessor instead.''' , UpperCAmelCase__ , )
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__)
| 14 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipConfig""",
"""BlipTextConfig""",
"""BlipVisionConfig""",
],
"""processing_blip""": ["""BlipProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Tuple = ["""BlipImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[Any] = [
"""BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlipModel""",
"""BlipPreTrainedModel""",
"""BlipForConditionalGeneration""",
"""BlipForQuestionAnswering""",
"""BlipVisionModel""",
"""BlipTextModel""",
"""BlipForImageTextRetrieval""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"""TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBlipModel""",
"""TFBlipPreTrainedModel""",
"""TFBlipForConditionalGeneration""",
"""TFBlipForQuestionAnswering""",
"""TFBlipVisionModel""",
"""TFBlipTextModel""",
"""TFBlipForImageTextRetrieval""",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_lowerCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 14 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_UpperCAmelCase : Union[str, Any] = """"""
_UpperCAmelCase : Optional[Any] = """"""
_UpperCAmelCase : List[str] = """"""
_UpperCAmelCase : int = 1 # (0 is vertical, 1 is horizontal)
def __lowerCamelCase ( ):
'''simple docstring'''
snake_case_ , snake_case_ = get_dataset(UpperCamelCase__ , UpperCamelCase__ )
print('Processing...' )
snake_case_ , snake_case_ , snake_case_ = update_image_and_anno(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for index, image in enumerate(UpperCamelCase__ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ = random_chars(32 )
snake_case_ = paths[index].split(os.sep )[-1].rsplit('.' , 1 )[0]
snake_case_ = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , UpperCamelCase__ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(UpperCamelCase__ )} with {file_name}''' )
snake_case_ = []
for anno in new_annos[index]:
snake_case_ = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(UpperCamelCase__ )
with open(F'''/{file_root}.txt''' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = []
for label_file in glob.glob(os.path.join(UpperCamelCase__ , '*.txt' ) ):
snake_case_ = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(UpperCamelCase__ ) as in_file:
snake_case_ = in_file.readlines()
snake_case_ = os.path.join(UpperCamelCase__ , F'''{label_name}.jpg''' )
snake_case_ = []
for obj_list in obj_lists:
snake_case_ = obj_list.rstrip('\n' ).split(' ' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(UpperCamelCase__ )
labels.append(UpperCamelCase__ )
return img_paths, labels
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 ):
'''simple docstring'''
snake_case_ = []
snake_case_ = []
snake_case_ = []
for idx in range(len(UpperCamelCase__ ) ):
snake_case_ = []
snake_case_ = img_list[idx]
path_list.append(UpperCamelCase__ )
snake_case_ = anno_list[idx]
snake_case_ = cva.imread(UpperCamelCase__ )
if flip_type == 1:
snake_case_ = cva.flip(UpperCamelCase__ , UpperCamelCase__ )
for bbox in img_annos:
snake_case_ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
snake_case_ = cva.flip(UpperCamelCase__ , UpperCamelCase__ )
for bbox in img_annos:
snake_case_ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(UpperCamelCase__ )
new_imgs_list.append(UpperCamelCase__ )
return new_imgs_list, new_annos_lists, path_list
def __lowerCamelCase ( UpperCamelCase__ = 32 ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case_ = ascii_lowercase + digits
return "".join(random.choice(UpperCamelCase__ ) for _ in range(UpperCamelCase__ ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 361 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class lowercase :
def __init__( self , snake_case , snake_case=99 , snake_case=13 , snake_case=7 , snake_case=9 , snake_case=True , snake_case=True , snake_case=False , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case=8 , snake_case=0.1 , snake_case=0.0_02 , snake_case=1 , snake_case=0 , snake_case=0 , snake_case=None , snake_case=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = encoder_seq_length
snake_case_ = decoder_seq_length
# For common tests
snake_case_ = self.decoder_seq_length
snake_case_ = is_training
snake_case_ = use_attention_mask
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = d_ff
snake_case_ = relative_attention_num_buckets
snake_case_ = dropout_rate
snake_case_ = initializer_factor
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = decoder_start_token_id
snake_case_ = None
snake_case_ = decoder_layers
def a ( self ):
return TaConfig.from_pretrained('google/umt5-base' )
def a ( self , snake_case , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case=None , snake_case=None , ):
if attention_mask is None:
snake_case_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
snake_case_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
snake_case_ = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=snake_case )
if decoder_head_mask is None:
snake_case_ = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=snake_case )
if cross_attn_head_mask is None:
snake_case_ = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=snake_case )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def a ( self ):
snake_case_ = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
snake_case_ = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe 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
snake_case_ = input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = self.get_config()
snake_case_ = config.num_attention_heads
snake_case_ = self.prepare_inputs_dict(snake_case , snake_case , snake_case )
return config, input_dict
def a ( self ):
snake_case_ , snake_case_ = self.prepare_config_and_inputs()
return config, inputs_dict
def a ( self ):
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def a ( self ):
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = UMTaModel(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(
input_ids=snake_case , decoder_input_ids=snake_case , attention_mask=snake_case , decoder_attention_mask=snake_case , )
snake_case_ = model(input_ids=snake_case , decoder_input_ids=snake_case )
snake_case_ = result.last_hidden_state
snake_case_ = result.past_key_values
snake_case_ = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(snake_case ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = UMTaModel(config=snake_case ).get_decoder().to(snake_case ).eval()
# first forward pass
snake_case_ = model(snake_case , use_cache=snake_case )
snake_case_ = model(snake_case )
snake_case_ = model(snake_case , use_cache=snake_case )
self.parent.assertTrue(len(snake_case ) == len(snake_case ) )
self.parent.assertTrue(len(snake_case ) == len(snake_case ) + 1 )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = model(snake_case )['last_hidden_state']
snake_case_ = model(snake_case , past_key_values=snake_case )['last_hidden_state']
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -1, random_slice_idx].detach()
snake_case_ = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1e-3 ) )
def a ( self , snake_case , snake_case , ):
snake_case_ = UMTaModel(config=snake_case ).to(snake_case ).half().eval()
snake_case_ = model(**snake_case )['last_hidden_state']
self.parent.assertFalse(torch.isnan(snake_case ).any().item() )
@require_torch
class lowercase ( lowercase_ , lowercase_ , lowercase_ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__SCREAMING_SNAKE_CASE : int = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Optional[int] = (
{
'''conversational''': UMTaForConditionalGeneration,
'''feature-extraction''': UMTaModel,
'''summarization''': UMTaForConditionalGeneration,
'''text2text-generation''': UMTaForConditionalGeneration,
'''translation''': UMTaForConditionalGeneration,
'''question-answering''': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = False
__SCREAMING_SNAKE_CASE : int = False
__SCREAMING_SNAKE_CASE : Optional[int] = True
__SCREAMING_SNAKE_CASE : Any = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__SCREAMING_SNAKE_CASE : List[str] = [0.8, 0.9]
def a ( self ):
snake_case_ = UMTaModelTester(self )
@unittest.skip('Test has a segmentation fault on torch 1.8.0' )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = UMTaModel(config_and_inputs[0] ).to(snake_case )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
snake_case , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'''{tmpdirname}/t5_test.onnx''' , export_params=snake_case , opset_version=9 , input_names=['input_ids', 'decoder_input_ids'] , )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision' )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*snake_case )
def a ( self ):
snake_case_ = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = config_and_inputs[0]
snake_case_ = UMTaForConditionalGeneration(snake_case ).eval()
model.to(snake_case )
snake_case_ = {
'head_mask': torch.zeros(config.num_layers , config.num_heads , device=snake_case ),
'decoder_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=snake_case ),
'cross_attn_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=snake_case ),
}
for attn_name, (name, mask) in zip(snake_case , head_masking.items() ):
snake_case_ = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
snake_case_ = torch.ones(
config.num_decoder_layers , config.num_heads , device=snake_case )
snake_case_ = model.generate(
config_and_inputs[1]['input_ids'] , num_beams=1 , max_length=3 , output_attentions=snake_case , return_dict_in_generate=snake_case , **snake_case , )
# We check the state of decoder_attentions and cross_attentions just from the last step
snake_case_ = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.' )
def a ( self ):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase ( unittest.TestCase ):
@slow
@unittest.skip(
'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged' )
def a ( self ):
snake_case_ = UMTaForConditionalGeneration.from_pretrained('google/umt5-small' , return_dict=snake_case ).to(snake_case )
snake_case_ = AutoTokenizer.from_pretrained('google/umt5-small' , use_fast=snake_case , legacy=snake_case )
snake_case_ = [
'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
'No se como puedo <extra_id_0>.',
'This is the reason why we <extra_id_0> them.',
'The <extra_id_0> walks in <extra_id_1>, seats',
'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
]
snake_case_ = tokenizer(snake_case , return_tensors='pt' , padding=snake_case ).input_ids
# fmt: off
snake_case_ = torch.tensor(
[
[ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(snake_case , snake_case )
snake_case_ = model.generate(input_ids.to(snake_case ) )
snake_case_ = [
'<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
'<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
]
snake_case_ = tokenizer.batch_decode(snake_case )
self.assertEqual(snake_case , snake_case )
| 200 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ :Any = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
'''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
'''tokenization_funnel''': ['''FunnelTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :List[str] = ['''FunnelTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :Tuple = [
'''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FunnelBaseModel''',
'''FunnelForMaskedLM''',
'''FunnelForMultipleChoice''',
'''FunnelForPreTraining''',
'''FunnelForQuestionAnswering''',
'''FunnelForSequenceClassification''',
'''FunnelForTokenClassification''',
'''FunnelModel''',
'''FunnelPreTrainedModel''',
'''load_tf_weights_in_funnel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :Union[str, Any] = [
'''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFFunnelBaseModel''',
'''TFFunnelForMaskedLM''',
'''TFFunnelForMultipleChoice''',
'''TFFunnelForPreTraining''',
'''TFFunnelForQuestionAnswering''',
'''TFFunnelForSequenceClassification''',
'''TFFunnelForTokenClassification''',
'''TFFunnelModel''',
'''TFFunnelPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ :Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :Dict = logging.get_logger(__name__)
lowerCAmelCase__ :Optional[int] = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class __a ( UpperCAmelCase ):
_a : List[str] = 'openai-gpt'
_a : int = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , _SCREAMING_SNAKE_CASE=40478 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=1e-5 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE="cls_index" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.1 , **_SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = afn
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = attn_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = summary_type
_UpperCAmelCase = summary_use_proj
_UpperCAmelCase = summary_activation
_UpperCAmelCase = summary_first_dropout
_UpperCAmelCase = summary_proj_to_labels
super().__init__(**_SCREAMING_SNAKE_CASE )
| 329 | 1 |
def lowerCamelCase__ ( a__ : float ) -> float:
if edge <= 0 or not isinstance(a__ , a__ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def lowerCamelCase__ ( a__ : float ) -> float:
if edge <= 0 or not isinstance(a__ , a__ ):
raise ValueError("""Length must be a positive.""" )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 261 |
from __future__ import annotations
def lowerCamelCase__ ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(a__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(a__ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 261 | 1 |
"""simple docstring"""
import numpy as np
class __lowerCAmelCase :
def __init__( self ):
'''simple docstring'''
__UpperCamelCase = (0, 0)
__UpperCamelCase = None
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 0
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
return self.position == cell.position
def UpperCAmelCase ( self ):
'''simple docstring'''
print(self.position )
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase=(5, 5) ):
'''simple docstring'''
__UpperCamelCase = np.zeros(__UpperCAmelCase )
__UpperCamelCase = world_size[0]
__UpperCamelCase = world_size[1]
def UpperCAmelCase ( self ):
'''simple docstring'''
print(self.w )
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
__UpperCamelCase = cell.position[0]
__UpperCamelCase = cell.position[1]
__UpperCamelCase = []
for n in neughbour_cord:
__UpperCamelCase = current_x + n[0]
__UpperCamelCase = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
__UpperCamelCase = Cell()
__UpperCamelCase = (x, y)
__UpperCamelCase = cell
neighbours.append(__UpperCAmelCase )
return neighbours
def A ( snake_case :Dict , snake_case :Tuple , snake_case :int ) -> Any:
__UpperCamelCase = []
__UpperCamelCase = []
_open.append(snake_case )
while _open:
__UpperCamelCase = np.argmin([n.f for n in _open] )
__UpperCamelCase = _open[min_f]
_closed.append(_open.pop(snake_case ) )
if current == goal:
break
for n in world.get_neigbours(snake_case ):
for c in _closed:
if c == n:
continue
__UpperCamelCase = current.g + 1
__UpperCamelCase , __UpperCamelCase = n.position
__UpperCamelCase , __UpperCamelCase = goal.position
__UpperCamelCase = (ya - ya) ** 2 + (xa - xa) ** 2
__UpperCamelCase = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(snake_case )
__UpperCamelCase = []
while current.parent is not None:
path.append(current.position )
__UpperCamelCase = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
UpperCamelCase : Optional[int] = Gridworld()
# Start position and goal
UpperCamelCase : List[Any] = Cell()
UpperCamelCase : int = (0, 0)
UpperCamelCase : str = Cell()
UpperCamelCase : Optional[int] = (4, 4)
print(f'''path from {start.position} to {goal.position}''')
UpperCamelCase : Optional[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
UpperCamelCase : Tuple = 1
print(world.w)
| 316 |
"""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
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : Any = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : Dict = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Dict = {
"gpt2": 1_0_2_4,
"gpt2-medium": 1_0_2_4,
"gpt2-large": 1_0_2_4,
"gpt2-xl": 1_0_2_4,
"distilgpt2": 1_0_2_4,
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ["input_ids", "attention_mask"]
lowercase = GPTaTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase=False , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , unk_token=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , **__UpperCAmelCase , )
__UpperCamelCase = kwargs.pop('add_bos_token' , __UpperCAmelCase )
__UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __UpperCAmelCase ) != add_prefix_space:
__UpperCamelCase = getattr(__UpperCAmelCase , pre_tok_state.pop('type' ) )
__UpperCamelCase = add_prefix_space
__UpperCamelCase = pre_tok_class(**__UpperCAmelCase )
__UpperCamelCase = add_prefix_space
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = kwargs.get('is_split_into_words' , __UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = kwargs.get('is_split_into_words' , __UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__UpperCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [self.eos_token_id] )
if len(__UpperCAmelCase ) > self.model_max_length:
__UpperCamelCase = input_ids[-self.model_max_length :]
return input_ids
| 316 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , lowerCamelCase__ = False ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = scheduler
__lowerCamelCase = optimizers if isinstance(lowerCamelCase__ , (list, tuple) ) else [optimizers]
__lowerCamelCase = split_batches
__lowerCamelCase = step_with_optimizer
__lowerCamelCase = GradientState()
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__lowerCamelCase = AcceleratorState().num_processes
for _ in range(lowerCamelCase__ ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
else:
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
return self.scheduler.get_last_lr()
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
return self.scheduler.state_dict()
def lowercase_ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
self.scheduler.load_state_dict(lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
return self.scheduler.get_lr()
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
return self.scheduler.print_lr(*lowerCamelCase__ , **lowerCamelCase__ )
| 368 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 10_55.0_55_85,
"footpound": 1.3_5_5_8_1_8,
}
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowerCamelCase = (
F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {', '.join(UpperCamelCase__ )}"""
)
raise ValueError(UpperCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowercase = 42 # [batch_size x 3]
lowercase = 42 # [batch_size x 3]
lowercase = 42 # [batch_size x 3]
lowercase = 42 # [batch_size x 3]
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 42
def lowerCamelCase ( self : Optional[Any] ):
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def lowerCamelCase ( self : Union[str, Any] ):
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def lowerCamelCase ( self : str ):
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def lowerCamelCase ( self : List[str] ):
snake_case__ : str = torch.arange(self.height * self.width )
snake_case__ : Optional[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(snake_case_ , self.width , rounding_mode="""trunc""" ),
] , axis=1 , )
return coords
@property
def lowerCamelCase ( self : Tuple ):
snake_case__ , *snake_case__ : List[Any] = self.shape
snake_case__ : str = int(np.prod(snake_case_ ) )
snake_case__ : List[Any] = self.get_image_coords()
snake_case__ : Tuple = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
snake_case__ : int = self.get_camera_rays(snake_case_ )
snake_case__ : Tuple = rays.view(snake_case_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def lowerCamelCase ( self : Any , snake_case_ : torch.Tensor ):
snake_case__ , *snake_case__ , snake_case__ : Union[str, Any] = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
snake_case__ : int = coords.view(snake_case_ , -1 , 2 )
snake_case__ : Dict = self.resolution()
snake_case__ : List[str] = self.fov()
snake_case__ : Union[str, Any] = (flat.float() / (res - 1)) * 2 - 1
snake_case__ : str = fracs * torch.tan(fov / 2 )
snake_case__ : int = fracs.view(snake_case_ , -1 , 2 )
snake_case__ : List[str] = (
self.z.view(snake_case_ , 1 , 3 )
+ self.x.view(snake_case_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(snake_case_ , 1 , 3 ) * fracs[:, :, 1:]
)
snake_case__ : str = directions / directions.norm(dim=-1 , keepdim=snake_case_ )
snake_case__ : Dict = torch.stack(
[
torch.broadcast_to(self.origin.view(snake_case_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(snake_case_ , *snake_case_ , 2 , 3 )
def lowerCamelCase ( self : Union[str, Any] , snake_case_ : int , snake_case_ : int ):
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=snake_case_ , height=snake_case_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def __snake_case( _lowerCAmelCase ) -> DifferentiableProjectiveCamera:
snake_case__ : Union[str, Any] = []
snake_case__ : int = []
snake_case__ : List[Any] = []
snake_case__ : Tuple = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
snake_case__ : Any = np.array([np.sin(_lowerCAmelCase ), np.cos(_lowerCAmelCase ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
snake_case__ : Optional[int] = -z * 4
snake_case__ : List[str] = np.array([np.cos(_lowerCAmelCase ), -np.sin(_lowerCAmelCase ), 0.0] )
snake_case__ : Optional[int] = np.cross(_lowerCAmelCase , _lowerCAmelCase )
origins.append(_lowerCAmelCase )
xs.append(_lowerCAmelCase )
ys.append(_lowerCAmelCase )
zs.append(_lowerCAmelCase )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(_lowerCAmelCase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(_lowerCAmelCase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(_lowerCAmelCase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(_lowerCAmelCase , axis=0 ) ).float() , width=_lowerCAmelCase , height=_lowerCAmelCase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(_lowerCAmelCase )) , )
| 35 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def __snake_case ( ):
lowerCamelCase_ = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=UpperCAmelCase_ )
lowerCamelCase_ = parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=UpperCAmelCase_ )
env_command_parser(subparsers=UpperCAmelCase_ )
launch_command_parser(subparsers=UpperCAmelCase_ )
tpu_command_parser(subparsers=UpperCAmelCase_ )
test_command_parser(subparsers=UpperCAmelCase_ )
# Let's go
lowerCamelCase_ = parser.parse_args()
if not hasattr(UpperCAmelCase_ , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(UpperCAmelCase_ )
if __name__ == "__main__":
main()
| 55 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = BarthezTokenizer
__UpperCamelCase = BarthezTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
super().setUp()
A_ : int = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
A_ : int = tokenizer
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
A_ : Any = "<pad>"
A_ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
A_ : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(snake_case ) , 101_122 )
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 101_122 )
@require_torch
def SCREAMING_SNAKE_CASE ( self :Tuple ):
'''simple docstring'''
A_ : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
A_ : Any = [0, 57, 3_018, 70_307, 91, 2]
A_ : Union[str, Any] = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors="pt" )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
A_ : Union[str, Any] = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
A_ : int = self.get_tokenizer()
A_ : Any = self.get_rust_tokenizer()
A_ : Tuple = "I was born in 92000, and this is falsé."
A_ : Dict = tokenizer.tokenize(snake_case )
A_ : Optional[Any] = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
A_ : List[str] = tokenizer.encode(snake_case , add_special_tokens=snake_case )
A_ : Any = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
A_ : Dict = self.get_rust_tokenizer()
A_ : Optional[int] = tokenizer.encode(snake_case )
A_ : Any = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
A_ : Tuple = {"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 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], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "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, 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, 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]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
A_ : Optional[Any] = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=snake_case , )
| 354 |
from collections.abc import Sequence
def __snake_case ( _lowerCAmelCase : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
A_ : Any = nums[0]
for i in range(1 , len(_lowerCAmelCase ) ):
A_ : Any = nums[i]
A_ : List[str] = max(_lowerCAmelCase , ans + num , _lowerCAmelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
_lowerCAmelCase : List[Any] = int(input('''Enter number of elements : ''').strip())
_lowerCAmelCase : Dict = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
print(max_subsequence_sum(array))
| 70 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ () -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(_lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'{solution() = }')
| 41 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__ : int = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
]
)
def UpperCamelCase ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : int, _lowerCAmelCase : Optional[int] ) -> Dict:
_UpperCAmelCase : Union[str, Any] = state_dict.pop(_lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = val
def UpperCamelCase ( _lowerCAmelCase : List[Any] ) -> List[str]:
_UpperCAmelCase : Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_UpperCAmelCase : Tuple = key.replace("""backbone.0.body""", """backbone.conv_encoder.model""" )
_UpperCAmelCase : Any = value
else:
_UpperCAmelCase : List[Any] = value
return new_state_dict
def UpperCamelCase ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Tuple=False ) -> Optional[Any]:
_UpperCAmelCase : int = """"""
if is_panoptic:
_UpperCAmelCase : str = """conditional_detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
_UpperCAmelCase : Union[str, Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Any = in_proj_weight[:256, :]
_UpperCAmelCase : Tuple = in_proj_bias[:256]
_UpperCAmelCase : Optional[int] = in_proj_weight[256:512, :]
_UpperCAmelCase : str = in_proj_bias[256:512]
_UpperCAmelCase : int = in_proj_weight[-256:, :]
_UpperCAmelCase : List[Any] = in_proj_bias[-256:]
def UpperCamelCase ( ) -> Any:
_UpperCAmelCase : str = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase : Dict = Image.open(requests.get(_lowerCAmelCase, stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Any ) -> List[Any]:
_UpperCAmelCase : str = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
_UpperCAmelCase : Dict = """resnet101"""
if "dc5" in model_name:
_UpperCAmelCase : Union[str, Any] = True
_UpperCAmelCase : Optional[Any] = """panoptic""" in model_name
if is_panoptic:
_UpperCAmelCase : Optional[int] = 250
else:
_UpperCAmelCase : str = 91
_UpperCAmelCase : Optional[int] = """huggingface/label-files"""
_UpperCAmelCase : str = """coco-detection-id2label.json"""
_UpperCAmelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase, _lowerCAmelCase, repo_type="""dataset""" ), """r""" ) )
_UpperCAmelCase : Union[str, Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : List[str] = idalabel
_UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
# load image processor
_UpperCAmelCase : Optional[int] = """coco_panoptic""" if is_panoptic else """coco_detection"""
_UpperCAmelCase : int = ConditionalDetrImageProcessor(format=_lowerCAmelCase )
# prepare image
_UpperCAmelCase : List[str] = prepare_img()
_UpperCAmelCase : Any = image_processor(images=_lowerCAmelCase, return_tensors="""pt""" )
_UpperCAmelCase : Any = encoding["""pixel_values"""]
logger.info(f'''Converting model {model_name}...''' )
# load original model from torch hub
_UpperCAmelCase : Tuple = torch.hub.load("""DeppMeng/ConditionalDETR""", _lowerCAmelCase, pretrained=_lowerCAmelCase ).eval()
_UpperCAmelCase : Tuple = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
_UpperCAmelCase : Optional[int] = """conditional_detr.""" + src
rename_key(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = rename_backbone_keys(_lowerCAmelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowerCAmelCase, is_panoptic=_lowerCAmelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_UpperCAmelCase : List[str] = """conditional_detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""conditional_detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
_UpperCAmelCase : Tuple = state_dict.pop(_lowerCAmelCase )
_UpperCAmelCase : Any = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_UpperCAmelCase : Optional[Any] = state_dict.pop(_lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
_UpperCAmelCase : Tuple = state_dict.pop(_lowerCAmelCase )
_UpperCAmelCase : Optional[int] = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
_UpperCAmelCase : Tuple = state_dict.pop(_lowerCAmelCase )
_UpperCAmelCase : Any = val
# finally, create HuggingFace model and load state dict
_UpperCAmelCase : Union[str, Any] = ConditionalDetrForSegmentation(_lowerCAmelCase ) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
model.eval()
model.push_to_hub(repo_id=_lowerCAmelCase, organization="""DepuMeng""", commit_message="""Add model""" )
# verify our conversion
_UpperCAmelCase : Any = conditional_detr(_lowerCAmelCase )
_UpperCAmelCase : int = model(_lowerCAmelCase )
assert torch.allclose(outputs.logits, original_outputs["""pred_logits"""], atol=1E-4 )
assert torch.allclose(outputs.pred_boxes, original_outputs["""pred_boxes"""], atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs["""pred_masks"""], atol=1E-4 )
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
lowerCamelCase__ : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''conditional_detr_resnet50''',
type=str,
help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
lowerCamelCase__ : int = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 246 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
lowercase__: List[str] = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=_UpperCAmelCase , cache_dir=_UpperCAmelCase )
lowercase__: List[str] = [t[-1] for t in os.walk(os.path.join(_UpperCAmelCase , os.listdir(_UpperCAmelCase )[0] , '''snapshots''' ) )]
lowercase__: int = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''' ) for f in files )
@slow
@require_flax
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ):
lowercase__, lowercase__: Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=_UpperCAmelCase )
lowercase__: List[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__: int = jax.random.PRNGKey(0 )
lowercase__: Tuple = 4
lowercase__: List[Any] = jax.device_count()
lowercase__: Optional[Any] = num_samples * [prompt]
lowercase__: Dict = pipeline.prepare_inputs(_UpperCAmelCase )
# shard inputs and rng
lowercase__: List[str] = replicate(_UpperCAmelCase )
lowercase__: List[str] = jax.random.split(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Tuple = shard(_UpperCAmelCase )
lowercase__: List[Any] = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_514_745 ) < 1e-3
assert np.abs(np.abs(_UpperCAmelCase , dtype=np.floataa ).sum() - 49_947.875 ) < 5e-1
lowercase__: List[Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(_UpperCAmelCase ) == num_samples
def _snake_case ( self ):
lowercase__, lowercase__: Any = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=_UpperCAmelCase )
lowercase__: Optional[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__: List[Any] = jax.random.PRNGKey(0 )
lowercase__: int = 50
lowercase__: str = jax.device_count()
lowercase__: int = num_samples * [prompt]
lowercase__: Any = pipeline.prepare_inputs(_UpperCAmelCase )
# shard inputs and rng
lowercase__: int = replicate(_UpperCAmelCase )
lowercase__: Dict = jax.random.split(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Optional[int] = shard(_UpperCAmelCase )
lowercase__: Tuple = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_652_401) ) < 1e-3
assert np.abs((np.abs(_UpperCAmelCase , dtype=np.floataa ).sum() - 2_383_808.2) ) < 5e-1
def _snake_case ( self ):
lowercase__, lowercase__: List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=_UpperCAmelCase )
lowercase__: Union[str, Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__: List[Any] = jax.random.PRNGKey(0 )
lowercase__: Optional[Any] = 50
lowercase__: Optional[int] = jax.device_count()
lowercase__: List[str] = num_samples * [prompt]
lowercase__: Any = pipeline.prepare_inputs(_UpperCAmelCase )
# shard inputs and rng
lowercase__: Optional[int] = replicate(_UpperCAmelCase )
lowercase__: Any = jax.random.split(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: List[str] = shard(_UpperCAmelCase )
lowercase__: str = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(_UpperCAmelCase , dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def _snake_case ( self ):
lowercase__, lowercase__: List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa )
lowercase__: List[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__: List[str] = jax.random.PRNGKey(0 )
lowercase__: List[Any] = 50
lowercase__: List[Any] = jax.device_count()
lowercase__: Optional[int] = num_samples * [prompt]
lowercase__: Tuple = pipeline.prepare_inputs(_UpperCAmelCase )
# shard inputs and rng
lowercase__: Any = replicate(_UpperCAmelCase )
lowercase__: int = jax.random.split(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Optional[int] = shard(_UpperCAmelCase )
lowercase__: Any = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(_UpperCAmelCase , dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def _snake_case ( self ):
lowercase__: Optional[int] = FlaxDDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , set_alpha_to_one=_UpperCAmelCase , steps_offset=1 , )
lowercase__, lowercase__: List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=_UpperCAmelCase , safety_checker=_UpperCAmelCase , )
lowercase__: List[Any] = scheduler.create_state()
lowercase__: Tuple = scheduler_state
lowercase__: Optional[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__: str = jax.random.PRNGKey(0 )
lowercase__: Dict = 50
lowercase__: Tuple = jax.device_count()
lowercase__: str = num_samples * [prompt]
lowercase__: Dict = pipeline.prepare_inputs(_UpperCAmelCase )
# shard inputs and rng
lowercase__: Union[str, Any] = replicate(_UpperCAmelCase )
lowercase__: str = jax.random.split(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Union[str, Any] = shard(_UpperCAmelCase )
lowercase__: List[str] = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.045_043_945) ) < 1e-3
assert np.abs((np.abs(_UpperCAmelCase , dtype=np.floataa ).sum() - 2_347_693.5) ) < 5e-1
def _snake_case ( self ):
lowercase__: Optional[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__: str = jax.device_count()
lowercase__: List[str] = num_samples * [prompt]
lowercase__: Any = jax.random.split(jax.random.PRNGKey(0 ) , _UpperCAmelCase )
lowercase__, lowercase__: Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=_UpperCAmelCase , )
lowercase__: List[str] = replicate(_UpperCAmelCase )
lowercase__: str = pipeline.prepare_inputs(_UpperCAmelCase )
lowercase__: Any = shard(_UpperCAmelCase )
lowercase__: Union[str, Any] = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
lowercase__: Optional[int] = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
lowercase__, lowercase__: Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=_UpperCAmelCase , use_memory_efficient_attention=_UpperCAmelCase , )
lowercase__: Optional[Any] = replicate(_UpperCAmelCase )
lowercase__: int = pipeline.prepare_inputs(_UpperCAmelCase )
lowercase__: Optional[Any] = shard(_UpperCAmelCase )
lowercase__: List[str] = pipeline(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , jit=_UpperCAmelCase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
lowercase__: List[Any] = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 2 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :Optional[Any] = "beit"
def __init__( self , _UpperCAmelCase=8192 , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=224 , _UpperCAmelCase=16 , _UpperCAmelCase=3 , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=True , _UpperCAmelCase=[3, 5, 7, 11] , _UpperCAmelCase=[1, 2, 3, 6] , _UpperCAmelCase=True , _UpperCAmelCase=0.4 , _UpperCAmelCase=256 , _UpperCAmelCase=1 , _UpperCAmelCase=False , _UpperCAmelCase=255 , **_UpperCAmelCase , ):
super().__init__(**_UpperCAmelCase )
lowercase__: Union[str, Any] = vocab_size
lowercase__: List[Any] = hidden_size
lowercase__: Optional[int] = num_hidden_layers
lowercase__: Optional[int] = num_attention_heads
lowercase__: int = intermediate_size
lowercase__: List[str] = hidden_act
lowercase__: List[Any] = hidden_dropout_prob
lowercase__: Dict = attention_probs_dropout_prob
lowercase__: List[str] = initializer_range
lowercase__: Optional[int] = layer_norm_eps
lowercase__: int = image_size
lowercase__: Tuple = patch_size
lowercase__: int = num_channels
lowercase__: Optional[Any] = use_mask_token
lowercase__: List[Any] = use_absolute_position_embeddings
lowercase__: Optional[int] = use_relative_position_bias
lowercase__: Optional[int] = use_shared_relative_position_bias
lowercase__: Optional[Any] = layer_scale_init_value
lowercase__: Union[str, Any] = drop_path_rate
lowercase__: Tuple = use_mean_pooling
# decode head attributes (semantic segmentation)
lowercase__: Tuple = out_indices
lowercase__: Optional[int] = pool_scales
# auxiliary head attributes (semantic segmentation)
lowercase__: List[str] = use_auxiliary_head
lowercase__: Optional[Any] = auxiliary_loss_weight
lowercase__: str = auxiliary_channels
lowercase__: List[str] = auxiliary_num_convs
lowercase__: Tuple = auxiliary_concat_input
lowercase__: Dict = semantic_loss_ignore_index
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :Dict = version.parse("1.11" )
@property
def _snake_case ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _snake_case ( self ):
return 1e-4
| 2 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def _lowerCamelCase ( lowercase : np.ndarray , lowercase : np.ndarray , lowercase : np.ndarray , lowercase : np.ndarray | None = None , ) -> np.ndarray:
_a = np.shape(lowercase )
_a = np.shape(lowercase )
_a = np.shape(lowercase )
if shape_a[0] != shape_b[0]:
_a = (
"Expected the same number of rows for A and B. "
F'Instead found A of size {shape_a} and B of size {shape_b}'
)
raise ValueError(lowercase )
if shape_b[1] != shape_c[1]:
_a = (
"Expected the same number of columns for B and C. "
F'Instead found B of size {shape_b} and C of size {shape_c}'
)
raise ValueError(lowercase )
_a = pseudo_inv
if a_inv is None:
try:
_a = np.linalg.inv(lowercase )
except np.linalg.LinAlgError:
raise ValueError(
"Input matrix A is not invertible. Cannot compute Schur complement." )
return mat_c - mat_b.T @ a_inv @ mat_b
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : int ):
_a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_a = np.array([[0, 3], [3, 0], [2, 3]] )
_a = np.array([[2, 1], [6, 3]] )
_a = schur_complement(__a , __a , __a )
_a = np.block([[a, b], [b.T, c]] )
_a = np.linalg.det(__a )
_a = np.linalg.det(__a )
_a = np.linalg.det(__a )
self.assertAlmostEqual(__a , det_a * det_s )
def UpperCamelCase__ ( self : str ):
_a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_a = np.array([[0, 3], [3, 0], [2, 3]] )
_a = np.array([[2, 1], [6, 3]] )
with self.assertRaises(__a ):
schur_complement(__a , __a , __a )
def UpperCamelCase__ ( self : Optional[int] ):
_a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_a = np.array([[0, 3], [3, 0], [2, 3]] )
_a = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(__a ):
schur_complement(__a , __a , __a )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 63 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase__ ):
return [ord(UpperCamelCase__ ) - 9_6 for elem in plain]
def _UpperCamelCase ( UpperCamelCase__ ):
return "".join(chr(elem + 9_6 ) for elem in encoded )
def _UpperCamelCase ( ):
UpperCAmelCase__ : int = encode(input("""-> """ ).strip().lower() )
print("""Encoded: """ , UpperCamelCase__ )
print("""Decoded:""" , decode(UpperCamelCase__ ) )
if __name__ == "__main__":
main() | 163 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCamelCase__ ( self ):
"""simple docstring"""
# A mock response for an HTTP head request to emulate server down
lowerCAmelCase__ = mock.Mock()
lowerCAmelCase__ = 5_00
lowerCAmelCase__ = {}
lowerCAmelCase__ = HTTPError
lowerCAmelCase__ = {}
# Download this model to make sure it's in the cache.
lowerCAmelCase__ = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=__snake_case ) as mock_head:
lowerCAmelCase__ = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def UpperCamelCase__ ( self ):
"""simple docstring"""
# A mock response for an HTTP head request to emulate server down
lowerCAmelCase__ = mock.Mock()
lowerCAmelCase__ = 5_00
lowerCAmelCase__ = {}
lowerCAmelCase__ = HTTPError
lowerCAmelCase__ = {}
# Download this model to make sure it's in the cache.
lowerCAmelCase__ = GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=__snake_case ) as mock_head:
lowerCAmelCase__ = GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase__ ( self ):
"""simple docstring"""
# This test is for deprecated behavior and can be removed in v5
try:
lowerCAmelCase__ = tempfile.mktemp()
with open(__snake_case , 'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' , __snake_case )
lowerCAmelCase__ = AlbertTokenizer.from_pretrained(__snake_case )
finally:
os.remove(__snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' , 'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' , __snake_case )
lowerCAmelCase__ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 10_00 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
# This test is for deprecated behavior and can be removed in v5
lowerCAmelCase__ = AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
_SCREAMING_SNAKE_CASE : Optional[Any] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def UpperCamelCase__ ( cls ):
"""simple docstring"""
lowerCAmelCase__ = TOKEN
HfFolder.save_token(__snake_case )
@classmethod
def UpperCamelCase__ ( cls ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ = os.path.join(__snake_case , 'vocab.txt' )
with open(__snake_case , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
lowerCAmelCase__ = BertTokenizer(__snake_case )
tokenizer.push_to_hub('test-tokenizer' , use_auth_token=self._token )
lowerCAmelCase__ = BertTokenizer.from_pretrained(F"{USER}/test-tokenizer" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__snake_case , repo_id='test-tokenizer' , push_to_hub=__snake_case , use_auth_token=self._token )
lowerCAmelCase__ = BertTokenizer.from_pretrained(F"{USER}/test-tokenizer" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def UpperCamelCase__ ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ = os.path.join(__snake_case , 'vocab.txt' )
with open(__snake_case , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
lowerCAmelCase__ = BertTokenizer(__snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' , use_auth_token=self._token )
lowerCAmelCase__ = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
__snake_case , repo_id='valid_org/test-tokenizer-org' , push_to_hub=__snake_case , use_auth_token=self._token )
lowerCAmelCase__ = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def UpperCamelCase__ ( self ):
"""simple docstring"""
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ = os.path.join(__snake_case , 'vocab.txt' )
with open(__snake_case , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
lowerCAmelCase__ = CustomTokenizer(__snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' , use_auth_token=self._token )
lowerCAmelCase__ = AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer" , trust_remote_code=__snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ = os.path.join(__snake_case , 'vocab.txt' )
with open(__snake_case , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
lowerCAmelCase__ = BertTokenizerFast.from_pretrained(__snake_case )
bert_tokenizer.save_pretrained(__snake_case )
lowerCAmelCase__ = CustomTokenizerFast.from_pretrained(__snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' , use_auth_token=self._token )
lowerCAmelCase__ = AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer" , trust_remote_code=__snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizerFast' )
lowerCAmelCase__ = AutoTokenizer.from_pretrained(
F"{USER}/test-dynamic-tokenizer" , use_fast=__snake_case , trust_remote_code=__snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizer' )
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data , {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data , {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) , ['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) , ['[CLS]', ' This is a ', 'extra_id_100'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) , ['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) , ['BC', 'A'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) , ['This is something ', '[SPECIAL_TOKEN]'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) , ['This is something ', '[SPECIAL_TOKEN]'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) , ['AB', 'C'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) , ['ABC', 'D'] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
# Even if the offsets are wrong, we necessarily output correct string
# parts.
lowerCAmelCase__ = Trie()
lowerCAmelCase__ = trie.cut_text('ABC' , [0, 0, 2, 1, 2, 3] )
self.assertEqual(__snake_case , ['AB', 'C'] )
| 353 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__snake_case : Optional[Any] = TypeVar("""KEY""")
__snake_case : str = TypeVar("""VAL""")
@dataclass(frozen=__lowercase , slots=__lowercase)
class __SCREAMING_SNAKE_CASE ( Generic[KEY, VAL]):
_SCREAMING_SNAKE_CASE : KEY
_SCREAMING_SNAKE_CASE : VAL
class __SCREAMING_SNAKE_CASE ( _Item):
def __init__( self ):
"""simple docstring"""
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __bool__( self ):
"""simple docstring"""
return False
__snake_case : int = _DeletedItem()
class __SCREAMING_SNAKE_CASE ( MutableMapping[KEY, VAL]):
def __init__( self , _UpperCamelCase = 8 , _UpperCamelCase = 0.75 ):
"""simple docstring"""
lowerCAmelCase__ = initial_block_size
lowerCAmelCase__ = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
lowerCAmelCase__ = capacity_factor
lowerCAmelCase__ = 0
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return hash(_UpperCamelCase ) % len(self._buckets )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return (ind + 1) % len(self._buckets )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self._buckets[ind]
if not stored:
lowerCAmelCase__ = _Item(_UpperCamelCase , _UpperCamelCase )
self._len += 1
return True
elif stored.key == key:
lowerCAmelCase__ = _Item(_UpperCamelCase , _UpperCamelCase )
return True
else:
return False
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_UpperCamelCase )
def UpperCamelCase__ ( self ):
"""simple docstring"""
if len(self._buckets ) <= self._initial_block_size:
return False
lowerCAmelCase__ = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self._buckets
lowerCAmelCase__ = [None] * new_size
lowerCAmelCase__ = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._resize(len(self._buckets ) * 2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._resize(len(self._buckets ) // 2 )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self._get_bucket_index(_UpperCamelCase )
for _ in range(len(self._buckets ) ):
yield ind
lowerCAmelCase__ = self._get_next_ind(_UpperCamelCase )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
for ind in self._iterate_buckets(_UpperCamelCase ):
if self._try_set(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
break
def __setitem__( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if self._is_full():
self._size_up()
self._add_item(_UpperCamelCase , _UpperCamelCase )
def __delitem__( self , _UpperCamelCase ):
"""simple docstring"""
for ind in self._iterate_buckets(_UpperCamelCase ):
lowerCAmelCase__ = self._buckets[ind]
if item is None:
raise KeyError(_UpperCamelCase )
if item is _deleted:
continue
if item.key == key:
lowerCAmelCase__ = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , _UpperCamelCase ):
"""simple docstring"""
for ind in self._iterate_buckets(_UpperCamelCase ):
lowerCAmelCase__ = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_UpperCamelCase )
def __len__( self ):
"""simple docstring"""
return self._len
def __iter__( self ):
"""simple docstring"""
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
"""simple docstring"""
lowerCAmelCase__ = ' ,'.join(
F"{item.key}: {item.val}" for item in self._buckets if item )
return F"HashMap({val_string})"
| 122 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ) -> tuple[list[int], int]:
__lowercase = [randint(-1000 , 1000 ) for i in range(10 )]
__lowercase = randint(-5000 , 5000 )
return (arr, r)
SCREAMING_SNAKE_CASE__ = make_dataset()
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> tuple[int, ...]:
for triplet in permutations(SCREAMING_SNAKE_CASE , 3 ):
if sum(SCREAMING_SNAKE_CASE ) == target:
return tuple(sorted(SCREAMING_SNAKE_CASE ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> tuple[int, int, int]:
arr.sort()
__lowercase = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
__lowercase , __lowercase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ) -> tuple[float, float]:
__lowercase = '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
__lowercase = '\ntriplet_sum1(*dataset)\n'
__lowercase = '\ntriplet_sum2(*dataset)\n'
__lowercase = repeat(setup=SCREAMING_SNAKE_CASE , stmt=SCREAMING_SNAKE_CASE , repeat=5 , number=10000 )
__lowercase = repeat(setup=SCREAMING_SNAKE_CASE , stmt=SCREAMING_SNAKE_CASE , repeat=5 , number=10000 )
return (min(SCREAMING_SNAKE_CASE ), min(SCREAMING_SNAKE_CASE ))
if __name__ == "__main__":
from doctest import testmod
testmod()
SCREAMING_SNAKE_CASE__ = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 325 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class A__ ( lowerCAmelCase__ ):
lowerCAmelCase__ : Tuple = "mask2former"
lowerCAmelCase__ : List[Any] = ["swin"]
lowerCAmelCase__ : str = {"hidden_size": "hidden_dim"}
def __init__( self : Optional[int] , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 10_24 , _UpperCAmelCase : str = "relu" , _UpperCAmelCase : int = 6 , _UpperCAmelCase : int = 10 , _UpperCAmelCase : int = 8 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : int = 20_48 , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 4 , _UpperCAmelCase : int = 2_55 , _UpperCAmelCase : int = 1_00 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 2.0 , _UpperCAmelCase : float = 5.0 , _UpperCAmelCase : float = 5.0 , _UpperCAmelCase : int = 1_25_44 , _UpperCAmelCase : float = 3.0 , _UpperCAmelCase : float = 0.75 , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : bool = True , _UpperCAmelCase : List[int] = [4, 8, 16, 32] , _UpperCAmelCase : bool = None , **_UpperCAmelCase : List[str] , ) -> int:
"""simple docstring"""
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__lowercase = CONFIG_MAPPING['swin'](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_UpperCAmelCase , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__lowercase = backbone_config.pop('model_type' )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(_UpperCAmelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
__lowercase = backbone_config
__lowercase = feature_size
__lowercase = mask_feature_size
__lowercase = hidden_dim
__lowercase = encoder_feedforward_dim
__lowercase = activation_function
__lowercase = encoder_layers
__lowercase = decoder_layers
__lowercase = num_attention_heads
__lowercase = dropout
__lowercase = dim_feedforward
__lowercase = pre_norm
__lowercase = enforce_input_projection
__lowercase = common_stride
__lowercase = ignore_value
__lowercase = num_queries
__lowercase = no_object_weight
__lowercase = class_weight
__lowercase = mask_weight
__lowercase = dice_weight
__lowercase = train_num_points
__lowercase = oversample_ratio
__lowercase = importance_sample_ratio
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = use_auxiliary_loss
__lowercase = feature_strides
__lowercase = output_auxiliary_logits
__lowercase = decoder_layers
super().__init__(**_UpperCAmelCase )
@classmethod
def a__ ( cls : Union[str, Any] , _UpperCAmelCase : PretrainedConfig , **_UpperCAmelCase : Optional[int] ) -> Dict:
"""simple docstring"""
return cls(
backbone_config=_UpperCAmelCase , **_UpperCAmelCase , )
def a__ ( self : str ) -> Dict[str, any]:
"""simple docstring"""
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 325 | 1 |
from __future__ import annotations
from cmath import sqrt
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_lowercase : Any = b * b - 4 * a * c
_lowercase : Union[str, Any] = (-b + sqrt(__lowerCAmelCase )) / (2 * a)
_lowercase : Any = (-b - sqrt(__lowerCAmelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCamelCase_( ) -> str:
_lowercase , _lowercase : List[str] = quadratic_roots(a=5 , b=6 , c=1 )
print(F'''The solutions are: {solutiona} and {solutiona}''' )
if __name__ == "__main__":
main()
| 353 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : Union[str, Any] = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
_lowercase : Any = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
a :str = logging.get_logger(__name__)
a :List[Any] = TypeVar("DatasetType", Dataset, IterableDataset)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = "first_exhausted" , ) -> DatasetType:
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("""Unable to interleave an empty list of datasets.""" )
for i, dataset in enumerate(__lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , (Dataset, IterableDataset) ):
if isinstance(__lowerCAmelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
"""is an empty dataset dictionary.""" )
raise ValueError(
F'''Dataset at position {i} has at least one split: {list(__lowerCAmelCase )}\n'''
F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowerCAmelCase ) )}\']''' )
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase ).__name__}.''' )
if i == 0:
SCREAMING_SNAKE_CASE__ : Optional[int] = (
(Dataset, IterableDataset) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , stopping_strategy=__lowerCAmelCase )
else:
return _interleave_iterable_datasets(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , stopping_strategy=__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = 0 , ) -> DatasetType:
if not dsets:
raise ValueError("""Unable to concatenate an empty list of datasets.""" )
for i, dataset in enumerate(__lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , (Dataset, IterableDataset) ):
if isinstance(__lowerCAmelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
"""is an empty dataset dictionary.""" )
raise ValueError(
F'''Dataset at position {i} has at least one split: {list(__lowerCAmelCase )}\n'''
F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowerCAmelCase ) )}\']''' )
raise ValueError(
F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase ).__name__}.''' )
if i == 0:
SCREAMING_SNAKE_CASE__ : str = (
(Dataset, IterableDataset) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(__lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , axis=__lowerCAmelCase )
else:
return _concatenate_iterable_datasets(__lowerCAmelCase , info=__lowerCAmelCase , split=__lowerCAmelCase , axis=__lowerCAmelCase )
| 132 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
# TODO Update this
__snake_case = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Tuple = """esm"""
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1026 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__="absolute" , snake_case__=True , snake_case__=None , snake_case__=False , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , mask_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : List[str] =vocab_size
UpperCAmelCase : str =hidden_size
UpperCAmelCase : List[Any] =num_hidden_layers
UpperCAmelCase : Optional[Any] =num_attention_heads
UpperCAmelCase : str =intermediate_size
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : int =attention_probs_dropout_prob
UpperCAmelCase : Dict =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : Union[str, Any] =layer_norm_eps
UpperCAmelCase : Dict =position_embedding_type
UpperCAmelCase : Optional[Any] =use_cache
UpperCAmelCase : int =emb_layer_norm_before
UpperCAmelCase : List[str] =token_dropout
UpperCAmelCase : Optional[Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
UpperCAmelCase : Optional[Any] =EsmFoldConfig()
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =EsmFoldConfig(**snake_case__ )
UpperCAmelCase : Tuple =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
UpperCAmelCase : Any =get_default_vocab_list()
else:
UpperCAmelCase : Tuple =vocab_list
else:
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : Union[str, Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , snake_case__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , snake_case__ ):
UpperCAmelCase : str =self.esmfold_config.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : str = None
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : float = 0
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : int = 128
__lowerCamelCase : "TrunkConfig" = None
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.trunk is None:
UpperCAmelCase : str =TrunkConfig()
elif isinstance(self.trunk , snake_case__ ):
UpperCAmelCase : Optional[int] =TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =asdict(self )
UpperCAmelCase : Any =self.trunk.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 48
__lowerCamelCase : int = 1024
__lowerCamelCase : int = 128
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : float = 0
__lowerCamelCase : float = 0
__lowerCamelCase : bool = False
__lowerCamelCase : int = 4
__lowerCamelCase : Optional[int] = 128
__lowerCamelCase : "StructureModuleConfig" = None
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
if self.structure_module is None:
UpperCAmelCase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , snake_case__ ):
UpperCAmelCase : str =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
UpperCAmelCase : Optional[int] =self.sequence_state_dim // self.sequence_head_width
UpperCAmelCase : Any =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =asdict(self )
UpperCAmelCase : Tuple =self.structure_module.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 384
__lowerCamelCase : int = 128
__lowerCamelCase : int = 16
__lowerCamelCase : int = 128
__lowerCamelCase : int = 12
__lowerCamelCase : int = 4
__lowerCamelCase : int = 8
__lowerCamelCase : float = 0.1
__lowerCamelCase : int = 8
__lowerCamelCase : int = 1
__lowerCamelCase : int = 2
__lowerCamelCase : int = 7
__lowerCamelCase : int = 10
__lowerCamelCase : float = 1E-8
__lowerCamelCase : float = 1E5
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return asdict(self )
def lowerCAmelCase_ ( )-> Tuple:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 348 | 0 |
"""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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
UpperCAmelCase__ = {
"""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"""
),
},
}
UpperCAmelCase__ = {
"""allenai/longformer-base-4096""": 4_0_9_6,
"""allenai/longformer-large-4096""": 4_0_9_6,
"""allenai/longformer-large-4096-finetuned-triviaqa""": 4_0_9_6,
"""allenai/longformer-base-4096-extra.pos.embd.only""": 4_0_9_6,
"""allenai/longformer-large-4096-extra.pos.embd.only""": 4_0_9_6,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = (
list(range(ord("""!""" ) ,ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) ,ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) ,ord("""ÿ""" ) + 1 ) )
)
_UpperCAmelCase = bs[:]
_UpperCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase )
cs.append(2**8 + n )
n += 1
_UpperCAmelCase = [chr(lowercase ) for n in cs]
return dict(zip(lowercase ,lowercase ) )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = set()
_UpperCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCAmelCase = char
return pairs
class a ( lowerCAmelCase_ ):
_snake_case : Optional[Any] = VOCAB_FILES_NAMES
_snake_case : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : List[Any] = ['input_ids', 'attention_mask']
def __init__( self : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any]="replace" , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : int="</s>" , __lowerCAmelCase : str="</s>" , __lowerCAmelCase : Any="<s>" , __lowerCAmelCase : str="<unk>" , __lowerCAmelCase : Optional[Any]="<pad>" , __lowerCAmelCase : List[Any]="<mask>" , __lowerCAmelCase : Dict=False , **__lowerCAmelCase : Union[str, Any] , ):
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else bos_token
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else eos_token
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else sep_token
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else cls_token
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else unk_token
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
super().__init__(
errors=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase , **__lowerCAmelCase , )
with open(__lowerCAmelCase , encoding="""utf-8""" ) as vocab_handle:
_UpperCAmelCase = json.load(__lowerCAmelCase )
_UpperCAmelCase = {v: k for k, v in self.encoder.items()}
_UpperCAmelCase = errors # how to handle errors in decoding
_UpperCAmelCase = bytes_to_unicode()
_UpperCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
_UpperCAmelCase = merges_handle.read().split("""\n""" )[1:-1]
_UpperCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_UpperCAmelCase = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_UpperCAmelCase = {}
_UpperCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_UpperCAmelCase = re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
def lowerCAmelCase_ ( self : List[Any] ):
return len(self.encoder )
def lowerCAmelCase_ ( self : Optional[int] ):
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : Tuple ):
if token in self.cache:
return self.cache[token]
_UpperCAmelCase = tuple(__lowerCAmelCase )
_UpperCAmelCase = get_pairs(__lowerCAmelCase )
if not pairs:
return token
while True:
_UpperCAmelCase = min(__lowerCAmelCase , key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCAmelCase , _UpperCAmelCase = bigram
_UpperCAmelCase = []
_UpperCAmelCase = 0
while i < len(__lowerCAmelCase ):
try:
_UpperCAmelCase = word.index(__lowerCAmelCase , __lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCAmelCase = j
if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCAmelCase = tuple(__lowerCAmelCase )
_UpperCAmelCase = new_word
if len(__lowerCAmelCase ) == 1:
break
else:
_UpperCAmelCase = get_pairs(__lowerCAmelCase )
_UpperCAmelCase = """ """.join(__lowerCAmelCase )
_UpperCAmelCase = word
return word
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : Dict ):
_UpperCAmelCase = []
for token in re.findall(self.pat , __lowerCAmelCase ):
_UpperCAmelCase = """""".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(__lowerCAmelCase ).split(""" """ ) )
return bpe_tokens
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : Union[str, Any] ):
return self.encoder.get(__lowerCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : Optional[Any] ):
return self.decoder.get(__lowerCAmelCase )
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : List[Any] ):
_UpperCAmelCase = """""".join(__lowerCAmelCase )
_UpperCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
__lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
_UpperCAmelCase = os.path.join(
__lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCAmelCase , ensure_ascii=__lowerCAmelCase ) + """\n""" )
_UpperCAmelCase = 0
with open(__lowerCAmelCase , """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 __lowerCAmelCase : 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!""" )
_UpperCAmelCase = token_index
writer.write(""" """.join(__lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
_UpperCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase_ ( self : List[str] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [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 lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False , **__lowerCAmelCase : Dict ):
_UpperCAmelCase = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCAmelCase ) > 0 and not text[0].isspace()):
_UpperCAmelCase = """ """ + text
return (text, kwargs)
| 371 | """simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class a ( lowerCAmelCase_ ):
def __init__( self : Optional[int] , __lowerCAmelCase : Any=None , __lowerCAmelCase : Any=None , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : Optional[int] ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
if config is None:
assert isinstance(self.model , __lowerCAmelCase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
_UpperCAmelCase = self.model.config
else:
_UpperCAmelCase = config
_UpperCAmelCase = data_args
_UpperCAmelCase = self.config.tgt_vocab_size if isinstance(self.config , __lowerCAmelCase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
""" padding..""" )
if self.args.label_smoothing == 0:
_UpperCAmelCase = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase = label_smoothed_nll_loss
def lowerCAmelCase_ ( self : Dict , __lowerCAmelCase : int ):
if self.optimizer is None:
_UpperCAmelCase = ["""bias""", """LayerNorm.weight"""]
_UpperCAmelCase = [
{
"""params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"""weight_decay""": self.args.weight_decay,
},
{
"""params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"""weight_decay""": 0.0,
},
]
_UpperCAmelCase = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase = Adafactor
_UpperCAmelCase = {"""scale_parameter""": False, """relative_step""": False}
else:
_UpperCAmelCase = AdamW
_UpperCAmelCase = {
"""betas""": (self.args.adam_betaa, self.args.adam_betaa),
"""eps""": self.args.adam_epsilon,
}
_UpperCAmelCase = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase = OSS(
params=__lowerCAmelCase , optim=__lowerCAmelCase , **__lowerCAmelCase , )
else:
_UpperCAmelCase = optimizer_cls(__lowerCAmelCase , **__lowerCAmelCase )
if self.lr_scheduler is None:
_UpperCAmelCase = self._get_lr_scheduler(__lowerCAmelCase )
else: # ignoring --lr_scheduler
logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.""" )
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : List[str] ):
_UpperCAmelCase = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=__lowerCAmelCase )
return scheduler
def lowerCAmelCase_ ( self : Optional[int] ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase = model(**__lowerCAmelCase , use_cache=__lowerCAmelCase )[0]
_UpperCAmelCase = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase = model(**__lowerCAmelCase , labels=__lowerCAmelCase , use_cache=__lowerCAmelCase )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase = model(**__lowerCAmelCase , use_cache=__lowerCAmelCase )[0]
_UpperCAmelCase = torch.nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = self.loss_fn(__lowerCAmelCase , __lowerCAmelCase , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : int ):
_UpperCAmelCase = inputs.pop("""labels""" )
_UpperCAmelCase , _UpperCAmelCase = self._compute_loss(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return loss
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : nn.Module , __lowerCAmelCase : Dict[str, Union[torch.Tensor, Any]] , __lowerCAmelCase : bool , __lowerCAmelCase : Optional[List[str]] = None , ):
_UpperCAmelCase = self._prepare_inputs(__lowerCAmelCase )
_UpperCAmelCase = {
"""max_length""": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"""num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase = self.model.generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , **__lowerCAmelCase , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase = self._pad_tensors_to_max_len(__lowerCAmelCase , gen_kwargs["""max_length"""] )
_UpperCAmelCase = inputs.pop("""labels""" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase = self._compute_loss(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase = self._pad_tensors_to_max_len(__lowerCAmelCase , gen_kwargs["""max_length"""] )
return (loss, logits, labels)
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"""Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
f''' padded to `max_length`={max_length}''' )
_UpperCAmelCase = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase = tensor
return padded_tensor
| 30 | 0 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
# warning at import time
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.' , UpperCAmelCase__ , )
| 109 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
UpperCAmelCase : Optional[Any] = {
"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}
UpperCAmelCase : Union[str, Any] = {
"ctrl": 2_56,
}
UpperCAmelCase : List[str] = {
"Pregnancy": 16_86_29,
"Christianity": 76_75,
"Explain": 10_64_23,
"Fitness": 6_34_40,
"Saving": 6_31_63,
"Ask": 2_71_71,
"Ass": 9_59_85,
"Joke": 16_35_09,
"Questions": 4_56_22,
"Thoughts": 4_96_05,
"Retail": 5_23_42,
"Feminism": 16_43_38,
"Writing": 1_19_92,
"Atheism": 19_22_63,
"Netflix": 4_86_16,
"Computing": 3_96_39,
"Opinion": 4_32_13,
"Alone": 4_49_67,
"Funny": 5_89_17,
"Gaming": 4_03_58,
"Human": 40_88,
"India": 13_31,
"Joker": 7_71_38,
"Diet": 3_62_06,
"Legal": 1_18_59,
"Norman": 49_39,
"Tip": 7_26_89,
"Weight": 5_23_43,
"Movies": 4_62_73,
"Running": 2_34_25,
"Science": 20_90,
"Horror": 3_77_93,
"Confession": 6_05_72,
"Finance": 1_22_50,
"Politics": 1_63_60,
"Scary": 19_19_85,
"Support": 1_26_54,
"Technologies": 3_25_16,
"Teenage": 6_61_60,
"Event": 3_27_69,
"Learned": 6_74_60,
"Notion": 18_27_70,
"Wikipedia": 3_75_83,
"Books": 66_65,
"Extract": 7_60_50,
"Confessions": 10_27_01,
"Conspiracy": 7_59_32,
"Links": 6_36_74,
"Narcissus": 15_04_25,
"Relationship": 5_47_66,
"Relationships": 13_47_96,
"Reviews": 4_16_71,
"News": 42_56,
"Translation": 2_68_20,
"multilingual": 12_84_06,
}
def __lowerCamelCase ( lowerCamelCase__ : Any ):
'''simple docstring'''
lowerCamelCase = set()
lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase = char
lowerCamelCase = set(lowerCamelCase__ )
return pairs
class __lowercase ( a_ ):
"""simple docstring"""
UpperCamelCase : Any = VOCAB_FILES_NAMES
UpperCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : Optional[int] = CONTROL_CODES
def __init__( self , A , A , A="<unk>" , **A ) -> int:
'''simple docstring'''
super().__init__(unk_token=A , **A )
with open(A , encoding="""utf-8""" ) as vocab_handle:
lowerCamelCase = json.load(A )
lowerCamelCase = {v: k for k, v in self.encoder.items()}
with open(A , encoding="""utf-8""" ) as merges_handle:
lowerCamelCase = merges_handle.read().split("""\n""" )[1:-1]
lowerCamelCase = [tuple(merge.split() ) for merge in merges]
lowerCamelCase = dict(zip(A , range(len(A ) ) ) )
lowerCamelCase = {}
@property
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def __A ( self ) -> List[str]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def __A ( self , A ) -> str:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase = tuple(A )
lowerCamelCase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
lowerCamelCase = get_pairs(A )
if not pairs:
return token
while True:
lowerCamelCase = min(A , key=lambda A : self.bpe_ranks.get(A , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase , lowerCamelCase = bigram
lowerCamelCase = []
lowerCamelCase = 0
while i < len(A ):
try:
lowerCamelCase = word.index(A , A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase = j
if word[i] == first and i < len(A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase = tuple(A )
lowerCamelCase = new_word
if len(A ) == 1:
break
else:
lowerCamelCase = get_pairs(A )
lowerCamelCase = """@@ """.join(A )
lowerCamelCase = word[:-4]
lowerCamelCase = word
return word
def __A ( self , A ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = []
lowerCamelCase = re.findall(r"""\S+\n?""" , A )
for token in words:
split_tokens.extend(list(self.bpe(A ).split(""" """ ) ) )
return split_tokens
def __A ( self , A ) -> int:
'''simple docstring'''
return self.encoder.get(A , self.encoder.get(self.unk_token ) )
def __A ( self , A ) -> Any:
'''simple docstring'''
return self.decoder.get(A , self.unk_token )
def __A ( self , A ) -> str:
'''simple docstring'''
lowerCamelCase = """ """.join(A ).replace("""@@ """ , """""" ).strip()
return out_string
def __A ( self , A , A = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCamelCase = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCamelCase = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(A , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=A , ensure_ascii=A ) + """\n""" )
lowerCamelCase = 0
with open(A , """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 A : 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(A ) + """\n""" )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 252 | 0 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=30 , lowerCAmelCase__=2 , lowerCAmelCase__=3 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=32 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=37 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=10 , lowerCAmelCase__=0.02 , lowerCAmelCase__=3 , lowerCAmelCase__=0.6 , lowerCAmelCase__=None , ) -> Tuple:
a : Optional[int] = parent
a : str = batch_size
a : str = image_size
a : List[Any] = patch_size
a : int = num_channels
a : int = is_training
a : Any = use_labels
a : str = hidden_size
a : List[Any] = num_hidden_layers
a : List[Any] = num_attention_heads
a : List[Any] = intermediate_size
a : Dict = hidden_act
a : List[Any] = hidden_dropout_prob
a : Union[str, Any] = attention_probs_dropout_prob
a : Optional[Any] = type_sequence_label_size
a : List[str] = initializer_range
a : List[Any] = mask_ratio
a : Optional[int] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
a : Tuple = (image_size // patch_size) ** 2
a : Tuple = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __a ( self ) -> List[Any]:
a : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a : int = None
if self.use_labels:
a : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __a ( self ) -> Dict:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , 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 , is_decoder=lowerCAmelCase__ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
a : List[str] = ViTMAEModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : Any = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
a : Optional[int] = ViTMAEForPreTraining(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : Union[str, Any] = model(lowerCAmelCase__ )
a : Optional[Any] = (self.image_size // self.patch_size) ** 2
a : List[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
a : Dict = 1
a : Dict = ViTMAEForPreTraining(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a : Dict = model(lowerCAmelCase__ )
a : Tuple = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def __a ( self ) -> List[Any]:
a : List[str] = self.prepare_config_and_inputs()
a : str = config_and_inputs
a : List[str] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( a__ , a__ , unittest.TestCase ):
lowerCamelCase : str =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowerCamelCase : Optional[int] ={"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}
lowerCamelCase : Dict =False
lowerCamelCase : Optional[int] =False
lowerCamelCase : str =False
lowerCamelCase : Tuple =False
def __a ( self ) -> Optional[Any]:
a : Union[str, Any] = ViTMAEModelTester(self )
a : Dict = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ , hidden_size=37 )
def __a ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def __a ( self ) -> str:
pass
def __a ( self ) -> Optional[int]:
a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a : Optional[Any] = model_class(lowerCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
a : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase__ , nn.Linear ) )
def __a ( self ) -> List[Any]:
a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a : str = model_class(lowerCAmelCase__ )
a : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a : Optional[int] = [*signature.parameters.keys()]
a : List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def __a ( self ) -> Dict:
a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def __a ( self ) -> Tuple:
a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
# make masks reproducible
np.random.seed(2 )
a : List[Any] = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
a : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
a : List[Any] = torch.from_numpy(lowerCAmelCase__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
a : int = pt_noise
super().check_pt_tf_models(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> Optional[int]:
a : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a : List[Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
a : Any = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
a : Tuple = outputs[0].cpu().numpy()
a : Optional[Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
a : Optional[int] = model_class.from_pretrained(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
a : Union[str, Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
# Make sure we don't have nans
a : Tuple = after_outputs[0].cpu().numpy()
a : str = 0
a : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase__ , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def __a ( self ) -> Optional[int]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def __a ( self ) -> Optional[Any]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def __a ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def __a ( self ) -> Any:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def __a ( self ) -> Union[str, Any]:
pass
@slow
def __a ( self ) -> str:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : str = ViTMAEModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( ) ->Tuple:
'''simple docstring'''
a : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def __a ( self ) -> str:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def __a ( self ) -> Union[str, Any]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
a : Dict = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(lowerCAmelCase__ )
a : List[str] = self.default_image_processor
a : Union[str, Any] = prepare_img()
a : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).to(lowerCAmelCase__ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
a : List[str] = ViTMAEConfig()
a : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
a : List[str] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
a : Dict = model(**lowerCAmelCase__ , noise=torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ) )
# verify the logits
a : Optional[Any] = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
a : Optional[Any] = torch.tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowerCAmelCase__ ) , atol=1E-4 ) )
| 354 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : List[str] = logging.get_logger(__name__)
a : Optional[int] = '''T5Config'''
def _SCREAMING_SNAKE_CASE ( _lowercase : jnp.array , _lowercase : int , _lowercase : int ) ->jnp.ndarray:
'''simple docstring'''
a : Tuple = jnp.zeros_like(_lowercase )
a : Tuple = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
a : Dict = shifted_input_ids.at[:, 0].set(_lowercase )
a : Optional[Any] = jnp.where(shifted_input_ids == -100 , _lowercase , _lowercase )
return shifted_input_ids
class __UpperCamelCase ( a__ ):
lowerCamelCase : Any ="""mt5"""
lowerCamelCase : Dict =MTaConfig
class __UpperCamelCase ( a__ ):
lowerCamelCase : str ="""mt5"""
lowerCamelCase : Tuple =MTaConfig
class __UpperCamelCase ( a__ ):
lowerCamelCase : List[str] ="""mt5"""
lowerCamelCase : Tuple =MTaConfig
| 79 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class __lowerCAmelCase (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self : Optional[Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
lowercase__ = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=UpperCamelCase , cache_dir=UpperCamelCase )
lowercase__ = [t[-1] for t in os.walk(os.path.join(UpperCamelCase , os.listdir(UpperCamelCase )[0] , '''snapshots''' ) )]
lowercase__ = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''' ) for f in files )
@slow
@require_flax
class __lowerCAmelCase (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self : Tuple ):
'''simple docstring'''
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=UpperCamelCase )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 4
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
# shard inputs and rng
lowercase__ = replicate(UpperCamelCase )
lowercase__ = jax.random.split(UpperCamelCase , UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3
assert np.abs(np.abs(UpperCamelCase , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1
lowercase__ = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(UpperCamelCase ) == num_samples
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=UpperCamelCase )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
# shard inputs and rng
lowercase__ = replicate(UpperCamelCase )
lowercase__ = jax.random.split(UpperCamelCase , UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3
assert np.abs((np.abs(UpperCamelCase , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1
def UpperCamelCase__ (self : str ):
'''simple docstring'''
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=UpperCamelCase )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
# shard inputs and rng
lowercase__ = replicate(UpperCamelCase )
lowercase__ = jax.random.split(UpperCamelCase , UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3
assert np.abs((np.abs(UpperCamelCase , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def UpperCamelCase__ (self : str ):
'''simple docstring'''
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
# shard inputs and rng
lowercase__ = replicate(UpperCamelCase )
lowercase__ = jax.random.split(UpperCamelCase , UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3
assert np.abs((np.abs(UpperCamelCase , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
lowercase__ = FlaxDDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=UpperCamelCase , steps_offset=1 , )
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=UpperCamelCase , safety_checker=UpperCamelCase , )
lowercase__ = scheduler.create_state()
lowercase__ = scheduler_state
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
# shard inputs and rng
lowercase__ = replicate(UpperCamelCase )
lowercase__ = jax.random.split(UpperCamelCase , UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3
assert np.abs((np.abs(UpperCamelCase , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1
def UpperCamelCase__ (self : str ):
'''simple docstring'''
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = jax.random.split(jax.random.PRNGKey(0 ) , UpperCamelCase )
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=UpperCamelCase , )
lowercase__ = replicate(UpperCamelCase )
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
lowercase__ = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
lowercase__ ,lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=UpperCamelCase , use_memory_efficient_attention=UpperCamelCase , )
lowercase__ = replicate(UpperCamelCase )
lowercase__ = pipeline.prepare_inputs(UpperCamelCase )
lowercase__ = shard(UpperCamelCase )
lowercase__ = pipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase , jit=UpperCamelCase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
lowercase__ = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 2 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCamelCase : Optional[Any] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D TH AD',
'KS 8D 4D 9S 4S', # pair
'8C 4S KH JS 4D', # pair
'QH 8H KD JH 8S', # pair
'KC 4H KS 2H 8D', # pair
'KD 4S KC 3H 8S', # pair
'AH 8S AS KC JH', # pair
'3H 4C 4H 3S 2H', # 2 pairs
'5S 5D 2C KH KH', # 2 pairs
'3C KH 5D 5S KH', # 2 pairs
'AS 3C KH AD KH', # 2 pairs
'7C 7S 3S 7H 5S', # 3 of a kind
'7C 7S KH 2H 7H', # 3 of a kind
'AC KH QH AH AS', # 3 of a kind
'2H 4D 3C AS 5S', # straight (low ace)
'3C 5C 4C 2C 6H', # straight
'6S 8S 7S 5H 9H', # straight
'JS QS 9H TS KH', # straight
'QC KH TS JS AH', # straight (high ace)
'8C 9C 5C 3C TC', # flush
'3S 8S 9S 5S KS', # flush
'4C 5C 9C 8C KC', # flush
'JH 8H AH KH QH', # flush
'3D 2H 3H 2C 2D', # full house
'2H 2C 3S 3H 3D', # full house
'KH KC 3S 3H 3D', # full house
'JC 6H JS JD JH', # 4 of a kind
'JC 7H JS JD JH', # 4 of a kind
'JC KH JS JD JH', # 4 of a kind
'2S AS 4S 5S 3S', # straight flush (low ace)
'2D 6D 3D 4D 5D', # straight flush
'5C 6C 3C 7C 4C', # straight flush
'JH 9H TH KH QH', # straight flush
'JH AH TH KH QH', # royal flush (high ace straight flush)
)
lowerCamelCase : Tuple = (
('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)
lowerCamelCase : Dict = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', True),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', False),
('AS 3S 4S 8S 2S', True),
)
lowerCamelCase : Any = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', False),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', True),
)
lowerCamelCase : Tuple = (
('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 14]),
('2H 5D 3C AS 5S', False, [14, 5, 5, 3, 2]),
('JH QD KC AS TS', False, [14, 13, 12, 11, 10]),
('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)
lowerCamelCase : Optional[int] = (
('JH AH TH KH QH', 0),
('JH 9H TH KH QH', 0),
('JC KH JS JD JH', 7),
('KH KC 3S 3H 3D', 6),
('8C 9C 5C 3C TC', 0),
('JS QS 9H TS KH', 0),
('7C 7S KH 2H 7H', 3),
('3C KH 5D 5S KH', 2),
('QH 8H KD JH 8S', 1),
('2D 6D 9D TH 7D', 0),
)
lowerCamelCase : Dict = (
('JH AH TH KH QH', 23),
('JH 9H TH KH QH', 22),
('JC KH JS JD JH', 21),
('KH KC 3S 3H 3D', 20),
('8C 9C 5C 3C TC', 19),
('JS QS 9H TS KH', 18),
('7C 7S KH 2H 7H', 17),
('3C KH 5D 5S KH', 16),
('QH 8H KD JH 8S', 15),
('2D 6D 9D TH 7D', 14),
)
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
"""simple docstring"""
lowercase__ ,lowercase__ = randrange(len(A ) ), randrange(len(A ) )
lowercase__ = ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
lowercase__ ,lowercase__ = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def _SCREAMING_SNAKE_CASE (A = 100 ) -> str:
"""simple docstring"""
return (generate_random_hand() for _ in range(A ))
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> List[str]:
"""simple docstring"""
assert PokerHand(A )._is_flush() == expected
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Union[str, Any]:
"""simple docstring"""
assert PokerHand(A )._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values''' , A )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Any:
"""simple docstring"""
lowercase__ = PokerHand(A )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Tuple:
"""simple docstring"""
assert PokerHand(A )._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(A )._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Union[str, Any]:
"""simple docstring"""
assert PokerHand(A ).compare_with(PokerHand(A ) ) == expected
@pytest.mark.parametrize('''hand, other, expected''' , generate_random_hands() )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(A ).compare_with(PokerHand(A ) ) == expected
def _SCREAMING_SNAKE_CASE () -> Tuple:
"""simple docstring"""
lowercase__ = [PokerHand(A ) for hand in SORTED_HANDS]
lowercase__ = poker_hands.copy()
shuffle(A )
lowercase__ = chain(sorted(A ) )
for index, hand in enumerate(A ):
assert hand == poker_hands[index]
def _SCREAMING_SNAKE_CASE () -> List[Any]:
"""simple docstring"""
lowercase__ = [PokerHand('''2D AC 3H 4H 5S''' ), PokerHand('''2S 3H 4H 5S 6C''' )]
pokerhands.sort(reverse=A )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def _SCREAMING_SNAKE_CASE () -> int:
"""simple docstring"""
lowercase__ = PokerHand('''2C 4S AS 3D 5C''' )
lowercase__ = True
lowercase__ = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
"""simple docstring"""
lowercase__ = 0
lowercase__ = os.path.abspath(os.path.dirname(A ) )
lowercase__ = os.path.join(A , '''poker_hands.txt''' )
with open(A ) as file_hand:
for line in file_hand:
lowercase__ = line[:14].strip()
lowercase__ = line[15:].strip()
lowercase__ ,lowercase__ = PokerHand(A ), PokerHand(A )
lowercase__ = player.compare_with(A )
if output == "Win":
answer += 1
assert answer == 376
| 2 | 1 |
import random
def _a ( SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase = num - 1
__lowerCAmelCase = 0
while s % 2 == 0:
__lowerCAmelCase = s // 2
t += 1
for _ in range(5 ):
__lowerCAmelCase = random.randrange(2 , num - 1 )
__lowerCAmelCase = pow(_lowercase , _lowercase , _lowercase )
if v != 1:
__lowerCAmelCase = 0
while v != (num - 1):
if i == t - 1:
return False
else:
__lowerCAmelCase = i + 1
__lowerCAmelCase = (v**2) % num
return True
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
if num < 2:
return False
__lowerCAmelCase = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
1_01,
1_03,
1_07,
1_09,
1_13,
1_27,
1_31,
1_37,
1_39,
1_49,
1_51,
1_57,
1_63,
1_67,
1_73,
1_79,
1_81,
1_91,
1_93,
1_97,
1_99,
2_11,
2_23,
2_27,
2_29,
2_33,
2_39,
2_41,
2_51,
2_57,
2_63,
2_69,
2_71,
2_77,
2_81,
2_83,
2_93,
3_07,
3_11,
3_13,
3_17,
3_31,
3_37,
3_47,
3_49,
3_53,
3_59,
3_67,
3_73,
3_79,
3_83,
3_89,
3_97,
4_01,
4_09,
4_19,
4_21,
4_31,
4_33,
4_39,
4_43,
4_49,
4_57,
4_61,
4_63,
4_67,
4_79,
4_87,
4_91,
4_99,
5_03,
5_09,
5_21,
5_23,
5_41,
5_47,
5_57,
5_63,
5_69,
5_71,
5_77,
5_87,
5_93,
5_99,
6_01,
6_07,
6_13,
6_17,
6_19,
6_31,
6_41,
6_43,
6_47,
6_53,
6_59,
6_61,
6_73,
6_77,
6_83,
6_91,
7_01,
7_09,
7_19,
7_27,
7_33,
7_39,
7_43,
7_51,
7_57,
7_61,
7_69,
7_73,
7_87,
7_97,
8_09,
8_11,
8_21,
8_23,
8_27,
8_29,
8_39,
8_53,
8_57,
8_59,
8_63,
8_77,
8_81,
8_83,
8_87,
9_07,
9_11,
9_19,
9_29,
9_37,
9_41,
9_47,
9_53,
9_67,
9_71,
9_77,
9_83,
9_91,
9_97,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(_lowercase )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] = 10_24 ):
while True:
__lowerCAmelCase = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(_lowercase ):
return num
if __name__ == "__main__":
UpperCamelCase__ = generate_large_prime()
print(("""Prime number:""", num))
print(("""is_prime_low_num:""", is_prime_low_num(num)))
| 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ):
try:
__lowerCAmelCase = int(SCREAMING_SNAKE_CASE_ )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
__lowerCAmelCase = []
__lowerCAmelCase = 2
while len(SCREAMING_SNAKE_CASE_ ) < nth:
if is_prime(SCREAMING_SNAKE_CASE_ ):
primes.append(SCREAMING_SNAKE_CASE_ )
num += 1
else:
num += 1
return primes[len(SCREAMING_SNAKE_CASE_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 102 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
for attribute in key.split('''.''' ):
A: str = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if weight_type is not None:
A: Any = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).shape
else:
A: List[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
A: List[Any] = value
elif weight_type == "weight_g":
A: Dict = value
elif weight_type == "weight_v":
A: Any = value
elif weight_type == "bias":
A: Optional[Any] = value
else:
A: Any = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Tuple:
A: Optional[int] = []
A: str = fairseq_model.state_dict()
A: Tuple = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
A: List[Any] = False
if "conv_layers" in name:
load_conv_layer(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hf_model.config.feat_extract_norm == '''group''' , )
A: Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
A: Any = '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
A: List[Any] = True
if "*" in mapped_key:
A: List[str] = name.split(SCREAMING_SNAKE_CASE__ )[0].split('''.''' )[-2]
A: List[str] = mapped_key.replace('''*''' , SCREAMING_SNAKE_CASE__ )
if "weight_g" in name:
A: str = '''weight_g'''
elif "weight_v" in name:
A: Tuple = '''weight_v'''
elif "weight" in name:
A: List[Any] = '''weight'''
elif "bias" in name:
A: Tuple = '''bias'''
else:
A: Any = None
set_recursively(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
continue
if not is_used:
unused_weights.append(SCREAMING_SNAKE_CASE__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Union[str, Any]:
A: List[str] = full_name.split('''conv_layers.''' )[-1]
A: Tuple = name.split('''.''' )
A: Any = int(items[0] )
A: Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
A: Optional[int] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
A: Optional[int] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
A: Dict = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
A: Tuple = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=None , __lowercase=None , __lowercase=True ) -> str:
if config_path is not None:
A: Union[str, Any] = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
else:
A: Union[str, Any] = HubertConfig()
if is_finetuned:
if dict_path:
A: Union[str, Any] = Dictionary.load(SCREAMING_SNAKE_CASE__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A: List[Any] = target_dict.pad_index
A: Optional[Any] = target_dict.bos_index
A: Optional[int] = target_dict.eos_index
A: Union[str, Any] = len(target_dict.symbols )
A: Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE__ , '''vocab.json''' )
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE__ ) )
return
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , SCREAMING_SNAKE_CASE__ )
A: Optional[Any] = WavaVecaCTCTokenizer(
SCREAMING_SNAKE_CASE__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=SCREAMING_SNAKE_CASE__ , )
A: Any = True if config.feat_extract_norm == '''layer''' else False
A: List[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , )
A: Any = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ )
processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
A: str = HubertForCTC(SCREAMING_SNAKE_CASE__ )
else:
A: Union[str, Any] = HubertModel(SCREAMING_SNAKE_CASE__ )
if is_finetuned:
A: Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
A: Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
A: Dict = model[0].eval()
recursively_load_weights(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
UpperCamelCase = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 319 |
import sys
def _A ( SCREAMING_SNAKE_CASE__ : List[str] ):
UpperCamelCase :Any = len(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Any = [[0 for x in range(SCREAMING_SNAKE_CASE__ )] for x in range(SCREAMING_SNAKE_CASE__ )]
UpperCamelCase :List[Any] = [[0 for x in range(SCREAMING_SNAKE_CASE__ )] for x in range(SCREAMING_SNAKE_CASE__ )]
for chain_length in range(2 , SCREAMING_SNAKE_CASE__ ):
for a in range(1 , n - chain_length + 1 ):
UpperCamelCase :Optional[Any] = a + chain_length - 1
UpperCamelCase :int = sys.maxsize
for c in range(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Any = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
UpperCamelCase :int = cost
UpperCamelCase :List[str] = c
return matrix, sol
def _A ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] ):
if i == j:
print('''A''' + str(SCREAMING_SNAKE_CASE__ ) , end=''' ''' )
else:
print('''(''' , end=''' ''' )
print_optiomal_solution(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , optimal_solution[i][j] )
print_optiomal_solution(SCREAMING_SNAKE_CASE__ , optimal_solution[i][j] + 1 , SCREAMING_SNAKE_CASE__ )
print(''')''' , end=''' ''' )
def _A ( ):
UpperCamelCase :Optional[int] = [30, 35, 15, 5, 10, 20, 25]
UpperCamelCase :Optional[Any] = len(SCREAMING_SNAKE_CASE__ )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
UpperCamelCase , UpperCamelCase :Dict = matrix_chain_order(SCREAMING_SNAKE_CASE__ )
print('''No. of Operation required: ''' + str(matrix[1][n - 1] ) )
print_optiomal_solution(SCREAMING_SNAKE_CASE__ , 1 , n - 1 )
if __name__ == "__main__":
main()
| 259 | 0 |
import math
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__UpperCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 1_0001 ) -> int:
try:
UpperCAmelCase_ = int(__UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('''Parameter nth must be int or castable to int.''' ) from None
if nth <= 0:
raise ValueError('''Parameter nth must be greater than or equal to one.''' )
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
while len(__UpperCamelCase ) < nth:
if is_prime(__UpperCamelCase ):
primes.append(__UpperCamelCase )
num += 1
else:
num += 1
return primes[len(__UpperCamelCase ) - 1]
if __name__ == "__main__":
print(F"{solution() = }")
| 355 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase = logging.get_logger(__name__)
class a ( _A ):
'''simple docstring'''
lowerCAmelCase : str = ['input_values', 'padding_mask']
def __init__( self : Optional[Any] , __snake_case : int = 1 , __snake_case : int = 2_40_00 , __snake_case : float = 0.0 , __snake_case : float = None , __snake_case : float = None , **__snake_case : Dict , ):
super().__init__(feature_size=__snake_case , sampling_rate=__snake_case , padding_value=__snake_case , **__snake_case )
UpperCAmelCase_ = chunk_length_s
UpperCAmelCase_ = overlap
@property
def lowerCamelCase_ ( self : List[str] ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def lowerCamelCase_ ( self : List[str] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self : List[str] , __snake_case : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __snake_case : Optional[Union[bool, str, PaddingStrategy]] = None , __snake_case : Optional[bool] = False , __snake_case : Optional[int] = None , __snake_case : Optional[Union[str, TensorType]] = None , __snake_case : Optional[int] = None , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
if padding and truncation:
raise ValueError('''Both padding and truncation were set. Make sure you only set one.''' )
elif padding is None:
# by default let's pad the inputs
UpperCAmelCase_ = True
UpperCAmelCase_ = bool(
isinstance(__snake_case , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
UpperCAmelCase_ = [np.asarray(__snake_case , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(__snake_case , np.ndarray ):
UpperCAmelCase_ = np.asarray(__snake_case , dtype=np.floataa )
elif isinstance(__snake_case , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
UpperCAmelCase_ = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase_ = [np.asarray(__snake_case ).T]
# verify inputs are valid
for idx, example in enumerate(__snake_case ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
UpperCAmelCase_ = None
UpperCAmelCase_ = BatchFeature({'''input_values''': raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
UpperCAmelCase_ = min(array.shape[0] for array in raw_audio )
UpperCAmelCase_ = int(np.floor(max_length / self.chunk_stride ) )
UpperCAmelCase_ = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
UpperCAmelCase_ = max(array.shape[0] for array in raw_audio )
UpperCAmelCase_ = int(np.ceil(max_length / self.chunk_stride ) )
UpperCAmelCase_ = (nb_step - 1) * self.chunk_stride + self.chunk_length
UpperCAmelCase_ = '''max_length'''
else:
UpperCAmelCase_ = input_values
# normal padding on batch
if padded_inputs is None:
UpperCAmelCase_ = self.pad(
__snake_case , max_length=__snake_case , truncation=__snake_case , padding=__snake_case , return_attention_mask=__snake_case , )
if padding:
UpperCAmelCase_ = padded_inputs.pop('''attention_mask''' )
UpperCAmelCase_ = []
for example in padded_inputs.pop('''input_values''' ):
if self.feature_size == 1:
UpperCAmelCase_ = example[..., None]
input_values.append(example.T )
UpperCAmelCase_ = input_values
if return_tensors is not None:
UpperCAmelCase_ = padded_inputs.convert_to_tensors(__snake_case )
return padded_inputs
| 177 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__a :Any = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__a :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 312 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __snake_case ( ):
"""simple docstring"""
A_ = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.py", "test_2.py", "unit_test.py"],
"content": ["a " * 20, "a " * 30, "b " * 7],
}
A_ = Dataset.from_dict(__UpperCamelCase )
return dataset
class _a ( snake_case_ ):
"""simple docstring"""
def __A ( self : Union[str, Any] ):
A_ = get_dataset()
A_ = make_duplicate_clusters(UpperCAmelCase , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def __A ( self : List[Any] ):
A_ = get_dataset()
A_ , A_ = deduplicate_dataset(UpperCAmelCase )
self.assertEqual(len(UpperCAmelCase ) , 2 )
print(UpperCAmelCase )
self.assertEqual(duplicate_clusters[0][0]["copies"] , 2 )
self.assertEqual(duplicate_clusters[0][0]["is_extreme"] , UpperCAmelCase ) | 312 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : Tuple = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
__UpperCAmelCase : List[Any] = hex_num[0] == "-"
if is_negative:
__UpperCAmelCase : int = hex_num[1:]
try:
__UpperCAmelCase : List[Any] = int(_UpperCAmelCase, 16 )
except ValueError:
raise ValueError("Invalid value was passed to the function" )
__UpperCAmelCase : Any = ""
while int_num > 0:
__UpperCAmelCase : int = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(("-" + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowerCAmelCase__ : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[Any] , **UpperCAmelCase_ : Dict ):
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : List[str] , UpperCAmelCase_ : Union[str, List[str], "Image", List["Image"]] , **UpperCAmelCase_ : Tuple ):
"""simple docstring"""
return super().__call__(UpperCAmelCase_ , **UpperCAmelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = {}
if "candidate_labels" in kwargs:
__UpperCAmelCase : Union[str, Any] = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
__UpperCAmelCase : int = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]="This is a photo of {}." ):
"""simple docstring"""
__UpperCAmelCase : Tuple = load_image(UpperCAmelCase_ )
__UpperCAmelCase : List[Any] = self.image_processor(images=[image] , return_tensors=self.framework )
__UpperCAmelCase : Dict = candidate_labels
__UpperCAmelCase : Any = [hypothesis_template.format(UpperCAmelCase_ ) for x in candidate_labels]
__UpperCAmelCase : Optional[int] = self.tokenizer(UpperCAmelCase_ , return_tensors=self.framework , padding=UpperCAmelCase_ )
__UpperCAmelCase : List[Any] = [text_inputs]
return inputs
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase_ : Tuple ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = model_inputs.pop("candidate_labels" )
__UpperCAmelCase : str = model_inputs.pop("text_inputs" )
if isinstance(text_inputs[0] , UpperCAmelCase_ ):
__UpperCAmelCase : Tuple = text_inputs[0]
else:
# Batching case.
__UpperCAmelCase : Optional[int] = text_inputs[0][0]
__UpperCAmelCase : Any = self.model(**UpperCAmelCase_ , **UpperCAmelCase_ )
__UpperCAmelCase : Dict = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_image,
}
return model_outputs
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase_ : Dict ):
"""simple docstring"""
__UpperCAmelCase : Any = model_outputs.pop("candidate_labels" )
__UpperCAmelCase : Tuple = model_outputs["logits"][0]
if self.framework == "pt":
__UpperCAmelCase : Union[str, Any] = logits.softmax(dim=-1 ).squeeze(-1 )
__UpperCAmelCase : Dict = probs.tolist()
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
__UpperCAmelCase : List[Any] = [scores]
elif self.framework == "tf":
__UpperCAmelCase : Union[str, Any] = stable_softmax(UpperCAmelCase_ , axis=-1 )
__UpperCAmelCase : List[str] = probs.numpy().tolist()
else:
raise ValueError(f"Unsupported framework: {self.framework}" )
__UpperCAmelCase : Dict = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(UpperCAmelCase_ , UpperCAmelCase_ ) , key=lambda UpperCAmelCase_ : -x[0] )
]
return result
| 37 | 0 |
import torch
from diffusers import StableDiffusionPipeline
_lowerCamelCase : int = '''path-to-your-trained-model'''
_lowerCamelCase : str = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
_lowerCamelCase : List[Any] = '''A photo of sks dog in a bucket'''
_lowerCamelCase : Optional[int] = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save('''dog-bucket.png''') | 282 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
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 (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Dict , lowercase : str , lowercase : List[str]=13 , lowercase : Any=7 , lowercase : Dict=True , lowercase : str=True , lowercase : List[Any]=True , lowercase : Any=True , lowercase : Tuple=99 , lowercase : str=24 , lowercase : str=2 , lowercase : Any=6 , lowercase : Dict=37 , lowercase : List[str]="gelu" , lowercase : Dict=0.1 , lowercase : Tuple=0.1 , lowercase : Optional[Any]=512 , lowercase : List[Any]=16 , lowercase : str=2 , lowercase : int=0.02 , lowercase : List[Any]=3 , lowercase : List[Any]=None , lowercase : int=1_000 , ):
'''simple docstring'''
_snake_case = parent
_snake_case = batch_size
_snake_case = seq_length
_snake_case = is_training
_snake_case = use_input_mask
_snake_case = use_token_type_ids
_snake_case = use_labels
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = num_labels
_snake_case = scope
_snake_case = range_bbox
def A ( self : List[Any] ):
'''simple docstring'''
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_snake_case = bbox[i, j, 3]
_snake_case = bbox[i, j, 1]
_snake_case = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_snake_case = bbox[i, j, 2]
_snake_case = bbox[i, j, 0]
_snake_case = t
_snake_case = None
if self.use_input_mask:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_snake_case = None
if self.use_token_type_ids:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case = None
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def A ( self : List[str] ):
'''simple docstring'''
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , 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 A ( self : str , lowercase : Tuple , lowercase : Tuple , lowercase : str , lowercase : Any , lowercase : Union[str, Any] , lowercase : List[str] , lowercase : str , ):
'''simple docstring'''
_snake_case = LiltModel(config=lowercase )
model.to(lowercase )
model.eval()
_snake_case = model(lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase )
_snake_case = model(lowercase , bbox=lowercase , token_type_ids=lowercase )
_snake_case = model(lowercase , bbox=lowercase )
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 A ( self : List[Any] , lowercase : int , lowercase : int , lowercase : Any , lowercase : Optional[int] , lowercase : Union[str, Any] , lowercase : Optional[Any] , lowercase : Optional[int] , ):
'''simple docstring'''
_snake_case = self.num_labels
_snake_case = LiltForTokenClassification(config=lowercase )
model.to(lowercase )
model.eval()
_snake_case = model(
lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : List[str] , lowercase : Union[str, Any] , lowercase : str , lowercase : Dict , lowercase : Optional[int] , lowercase : List[str] , lowercase : int , lowercase : int , ):
'''simple docstring'''
_snake_case = LiltForQuestionAnswering(config=lowercase )
model.to(lowercase )
model.eval()
_snake_case = model(
lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : Optional[Any] ):
'''simple docstring'''
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = config_and_inputs
_snake_case = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
_UpperCAmelCase : List[str] = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase : Optional[Any] = False
_UpperCAmelCase : Union[str, Any] = False
def A ( self : Dict , lowercase : Dict , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : List[str] , lowercase : Tuple ):
'''simple docstring'''
return True
def A ( self : Optional[Any] ):
'''simple docstring'''
_snake_case = LiltModelTester(self )
_snake_case = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def A ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : Dict ):
'''simple docstring'''
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
_snake_case = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_snake_case = type
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Any ):
'''simple docstring'''
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
def A ( self : Any ):
'''simple docstring'''
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase )
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = LiltModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_torch
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def A ( self : Tuple ):
'''simple docstring'''
_snake_case = LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base' ).to(lowercase )
_snake_case = torch.tensor([[1, 2]] , device=lowercase )
_snake_case = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=lowercase )
# forward pass
with torch.no_grad():
_snake_case = model(input_ids=lowercase , bbox=lowercase )
_snake_case = torch.Size([1, 2, 768] )
_snake_case = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=lowercase , )
self.assertTrue(outputs.last_hidden_state.shape , lowercase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , lowercase , atol=1E-3 ) ) | 282 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowerCamelCase_ = '''\
Text data.
Second line of data.'''
lowerCamelCase_ = '''file'''
@pytest.fixture(scope="""session""" )
def __magic_name__ ( __a : Dict ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
UpperCamelCase__ = bytes(__a , """utf-8""" )
with zstd.open(__a , """wb""" ) as f:
f.write(__a )
return path
@pytest.fixture
def __magic_name__ ( __a : List[Any] ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , __a ) , """w""" ) as f:
f.write(__a )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" , ["""gzip""", """xz""", """zstd"""] )
def __magic_name__ ( __a : List[str] , __a : Tuple , __a : Union[str, Any] , __a : List[str] , __a : List[Any] , __a : Dict ):
'''simple docstring'''
UpperCamelCase__ = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
UpperCamelCase__ = input_paths[compression_format]
UpperCamelCase__ = tmp_path / """cache"""
UpperCamelCase__ = DownloadConfig(cache_dir=__a , extract_compressed_file=__a )
UpperCamelCase__ = cached_path(__a , download_config=__a )
with open(__a ) as f:
UpperCamelCase__ = f.read()
with open(__a ) as f:
UpperCamelCase__ = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" , [True, False] )
@pytest.mark.parametrize("""default_cache_dir""" , [True, False] )
def __magic_name__ ( __a : List[str] , __a : Dict , __a : int , __a : Optional[Any] , __a : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ = """custom_cache"""
UpperCamelCase__ = """custom_extracted_dir"""
UpperCamelCase__ = tmp_path / """custom_extracted_path"""
if default_extracted:
UpperCamelCase__ = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" , __a )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(__a ) )
UpperCamelCase__ = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
UpperCamelCase__ = xz_file
UpperCamelCase__ = (
DownloadConfig(extract_compressed_file=__a )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=__a )
)
UpperCamelCase__ = cached_path(__a , download_config=__a )
assert Path(__a ).parent.parts[-2:] == expected
def __magic_name__ ( __a : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ = str(Path(__a ).resolve() )
assert cached_path(__a ) == text_file
# relative path
UpperCamelCase__ = str(Path(__a ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(__a ) == text_file
def __magic_name__ ( __a : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(__a ):
cached_path(__a )
# relative path
UpperCamelCase__ = """./__missing_file__.txt"""
with pytest.raises(__a ):
cached_path(__a )
def __magic_name__ ( __a : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ = get_from_cache(f"tmp://{tmpfs_file}" )
with open(__a ) as f:
UpperCamelCase__ = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __a )
def __magic_name__ ( ):
'''simple docstring'''
with pytest.raises(__a ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __a )
def __magic_name__ ( __a : int ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(__a ):
http_get("""https://huggingface.co""" , temp_file=__a )
with pytest.raises(__a ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __a )
def __magic_name__ ( __a : int ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(__a ):
ftp_get("""ftp://huggingface.co""" , temp_file=__a )
with pytest.raises(__a ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __a )
def __magic_name__ ( __a : List[str] ):
'''simple docstring'''
UpperCamelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(__a ):
fsspec_get("""s3://huggingface.co""" , temp_file=__a )
with pytest.raises(__a ):
fsspec_head("""s3://huggingface.co""" )
| 178 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __magic_name__ ( __a : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def __magic_name__ ( __a : np.ndarray , __a : np.ndarray , __a : np.ndarray ):
'''simple docstring'''
UpperCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__a , __a )
# Predict target for test data
UpperCamelCase__ = xgb.predict(__a )
UpperCamelCase__ = predictions.reshape(len(__a ) , 1 )
return predictions
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = fetch_california_housing()
UpperCamelCase__ , UpperCamelCase__ = data_handling(__a )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = train_test_split(
__a , __a , test_size=0.25 , random_state=1 )
UpperCamelCase__ = xgboost(__a , __a , __a )
# Error printing
print(f"Mean Absolute Error : {mean_absolute_error(__a , __a )}" )
print(f"Mean Square Error : {mean_squared_error(__a , __a )}" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 178 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( _a, unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = TransfoXLTokenizer
__lowerCAmelCase = False
__lowerCAmelCase = False
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
super().setUp()
a =[
"""<unk>""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""unwanted""",
"""wa""",
"""un""",
"""running""",
""",""",
"""low""",
"""l""",
]
a =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 SCREAMING_SNAKE_CASE ( self , **__A ) -> str:
a =True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def SCREAMING_SNAKE_CASE ( self , __A ) -> List[str]:
a ="""<unk> UNwanted , running"""
a ="""<unk> unwanted, running"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
a =TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=__lowerCamelCase )
a =tokenizer.tokenize('''<unk> UNwanted , running''' )
self.assertListEqual(__lowerCamelCase , ['''<unk>''', '''unwanted''', ''',''', '''running'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [0, 4, 8, 7] )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
a =TransfoXLTokenizer(lower_case=__lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
a =TransfoXLTokenizer(lower_case=__lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def SCREAMING_SNAKE_CASE ( self ) -> str:
a =TransfoXLTokenizer(lower_case=__lowerCamelCase )
a ="""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
a =[
"""Hello""",
"""(""",
"""bracket""",
""")""",
"""and""",
"""side""",
"""@-@""",
"""scrolled""",
"""[""",
"""and""",
"""]""",
"""Henry""",
"""'s""",
"""$""",
"""5""",
"""@,@""",
"""000""",
"""with""",
"""3""",
"""@.@""",
"""34""",
"""m""",
""".""",
"""What""",
"""'s""",
"""up""",
"""!""",
"""?""",
]
self.assertListEqual(tokenizer.tokenize(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(tokenizer.convert_tokens_to_string(__lowerCamelCase ) , __lowerCamelCase )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
a =self.get_tokenizer()
a =len(__lowerCamelCase )
tokenizer.add_tokens(['''new1''', '''new2'''] )
tokenizer.move_added_token('''new1''' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(__lowerCamelCase ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('''new1''' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , '''new1''' ) | 81 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( _a , _a , unittest.TestCase ):
snake_case__ : Any = StableDiffusionXLImgaImgPipeline
snake_case__ : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
snake_case__ : Tuple = PipelineTesterMixin.required_optional_params - {"""latents"""}
snake_case__ : Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ : List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case__ : Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _A ( self : int ):
torch.manual_seed(0 )
UpperCamelCase :Any = 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""") , attention_head_dim=(2, 4) , use_linear_projection=__lowerCamelCase , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
UpperCamelCase :Tuple = EulerDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
UpperCamelCase :Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCamelCase :Optional[int] = CLIPTextConfig(
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=1_000 , hidden_act="""gelu""" , projection_dim=32 , )
UpperCamelCase :Any = CLIPTextModel(__lowerCamelCase )
UpperCamelCase :List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=__lowerCamelCase )
UpperCamelCase :List[Any] = CLIPTextModelWithProjection(__lowerCamelCase )
UpperCamelCase :int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=__lowerCamelCase )
UpperCamelCase :Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def _A ( self : Tuple , __lowerCamelCase : Any , __lowerCamelCase : Optional[Any]=0 ):
UpperCamelCase :Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
UpperCamelCase :List[str] = image / 2 + 0.5
if str(__lowerCamelCase ).startswith("""mps""" ):
UpperCamelCase :Any = torch.manual_seed(__lowerCamelCase )
else:
UpperCamelCase :List[Any] = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
UpperCamelCase :str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.75,
}
return inputs
def _A ( self : str ):
UpperCamelCase :List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase :Optional[Any] = self.get_dummy_components()
UpperCamelCase :List[Any] = StableDiffusionXLImgaImgPipeline(**__lowerCamelCase )
UpperCamelCase :Any = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
UpperCamelCase :Optional[Any] = self.get_dummy_inputs(__lowerCamelCase )
UpperCamelCase :Union[str, Any] = sd_pipe(**__lowerCamelCase ).images
UpperCamelCase :Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCamelCase :List[Any] = np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _A ( self : Dict ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _A ( self : Optional[Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _A ( self : Union[str, Any] ):
pass
def _A ( self : Optional[int] ):
UpperCamelCase :Union[str, Any] = self.get_dummy_components()
UpperCamelCase :Dict = StableDiffusionXLImgaImgPipeline(**__lowerCamelCase )
UpperCamelCase :List[Any] = sd_pipe.to(__lowerCamelCase )
UpperCamelCase :List[str] = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
# forward without prompt embeds
UpperCamelCase :List[Any] = self.get_dummy_inputs(__lowerCamelCase )
UpperCamelCase :int = 3 * ["""this is a negative prompt"""]
UpperCamelCase :Union[str, Any] = negative_prompt
UpperCamelCase :Union[str, Any] = 3 * [inputs["""prompt"""]]
UpperCamelCase :Dict = sd_pipe(**__lowerCamelCase )
UpperCamelCase :Union[str, Any] = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
UpperCamelCase :Union[str, Any] = self.get_dummy_inputs(__lowerCamelCase )
UpperCamelCase :Optional[int] = 3 * ["""this is a negative prompt"""]
UpperCamelCase :Union[str, Any] = 3 * [inputs.pop("""prompt""" )]
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) :Union[str, Any] = sd_pipe.encode_prompt(__lowerCamelCase , negative_prompt=__lowerCamelCase )
UpperCamelCase :Dict = sd_pipe(
**__lowerCamelCase , prompt_embeds=__lowerCamelCase , negative_prompt_embeds=__lowerCamelCase , pooled_prompt_embeds=__lowerCamelCase , negative_pooled_prompt_embeds=__lowerCamelCase , )
UpperCamelCase :Union[str, Any] = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def _A ( self : Tuple ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self : List[Any] , __lowerCamelCase : Any , __lowerCamelCase : Dict="cpu" , __lowerCamelCase : List[Any]=torch.floataa , __lowerCamelCase : Tuple=0 ):
UpperCamelCase :Optional[int] = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
UpperCamelCase :Optional[Any] = np.random.RandomState(__lowerCamelCase ).standard_normal((1, 4, 64, 64) )
UpperCamelCase :Dict = torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase , dtype=__lowerCamelCase )
UpperCamelCase :str = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _A ( self : Optional[Any] ):
UpperCamelCase :Any = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
UpperCamelCase :Optional[Any] = self.get_inputs(__lowerCamelCase )
UpperCamelCase :Optional[int] = pipe(**__lowerCamelCase ).images
UpperCamelCase :Dict = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
UpperCamelCase :Union[str, Any] = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 38 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
a__ : str = TypeVar('T')
class UpperCAmelCase__ ( Generic[T]):
def __init__( self , lowercase ) -> None:
__UpperCamelCase = data
__UpperCamelCase = self
__UpperCamelCase = 0
class UpperCAmelCase__ ( Generic[T]):
def __init__( self ) -> None:
# map from node name to the node object
__UpperCamelCase = {}
def __lowerCamelCase ( self , lowercase ) -> None:
# create a new set with x as its member
__UpperCamelCase = DisjointSetTreeNode(lowercase )
def __lowerCamelCase ( self , lowercase ) -> DisjointSetTreeNode[T]:
# find the set x belongs to (with path-compression)
__UpperCamelCase = self.map[data]
if elem_ref != elem_ref.parent:
__UpperCamelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def __lowerCamelCase ( self , lowercase , lowercase ) -> None:
# helper function for union operation
if nodea.rank > nodea.rank:
__UpperCamelCase = nodea
else:
__UpperCamelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def __lowerCamelCase ( self , lowercase , lowercase ) -> None:
# merge 2 disjoint sets
self.link(self.find_set(lowercase ) , self.find_set(lowercase ) )
class UpperCAmelCase__ ( Generic[T]):
def __init__( self ) -> None:
# connections: map from the node to the neighbouring nodes (with weights)
__UpperCamelCase = {}
def __lowerCamelCase ( self , lowercase ) -> None:
# add a node ONLY if its not present in the graph
if node not in self.connections:
__UpperCamelCase = {}
def __lowerCamelCase ( self , lowercase , lowercase , lowercase ) -> None:
# add an edge with the given weight
self.add_node(lowercase )
self.add_node(lowercase )
__UpperCamelCase = weight
__UpperCamelCase = weight
def __lowerCamelCase ( self ) -> GraphUndirectedWeighted[T]:
__UpperCamelCase = []
__UpperCamelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda lowercase : x[2] )
# creating the disjoint set
__UpperCamelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(lowercase )
# MST generation
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = edges[index]
index += 1
__UpperCamelCase = disjoint_set.find_set(lowercase )
__UpperCamelCase = disjoint_set.find_set(lowercase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(lowercase , lowercase , lowercase )
disjoint_set.union(lowercase , lowercase )
return graph
| 243 |
'''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):
def __lowerCamelCase ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __lowerCamelCase ( self ) -> Any:
torch.manual_seed(0 )
__UpperCamelCase = UNetaDModel(
sample_size=(3_2, 6_4) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=("""AttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """AttnUpBlock2D""") , )
return model
@property
def __lowerCamelCase ( self ) -> List[Any]:
torch.manual_seed(0 )
__UpperCamelCase = UNetaDConditionModel(
sample_size=(6_4, 3_2) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , cross_attention_dim=1_0 , )
return model
@property
def __lowerCamelCase ( self ) -> Optional[Any]:
torch.manual_seed(0 )
__UpperCamelCase = AutoencoderKL(
sample_size=(1_2_8, 6_4) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""") , up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""") , )
__UpperCamelCase = UNetaDModel(
sample_size=(6_4, 3_2) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=("""AttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """AttnUpBlock2D""") , )
return vqvae, unet
@slow
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
__UpperCamelCase = DDPMScheduler()
__UpperCamelCase = AudioDiffusionPipeline(vqvae=lowercase , unet=self.dummy_unet , mel=lowercase , scheduler=lowercase )
__UpperCamelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = torch.Generator(device=lowercase ).manual_seed(4_2 )
__UpperCamelCase = pipe(generator=lowercase , steps=4 )
__UpperCamelCase = output.audios[0]
__UpperCamelCase = output.images[0]
__UpperCamelCase = torch.Generator(device=lowercase ).manual_seed(4_2 )
__UpperCamelCase = pipe(generator=lowercase , steps=4 , return_dict=lowercase )
__UpperCamelCase = 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]
)
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:1_0]
__UpperCamelCase = np.frombuffer(image_from_tuple.tobytes() , dtype="""uint8""" )[:1_0]
__UpperCamelCase = np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
__UpperCamelCase = 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] , )
__UpperCamelCase = DDIMScheduler()
__UpperCamelCase = self.dummy_vqvae_and_unet
__UpperCamelCase = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=lowercase , scheduler=lowercase )
__UpperCamelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
np.random.seed(0 )
__UpperCamelCase = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
__UpperCamelCase = torch.Generator(device=lowercase ).manual_seed(4_2 )
__UpperCamelCase = pipe(raw_audio=lowercase , generator=lowercase , start_step=5 , steps=1_0 )
__UpperCamelCase = 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]
)
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:1_0]
__UpperCamelCase = np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
__UpperCamelCase = self.dummy_unet_condition
__UpperCamelCase = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=lowercase , mel=lowercase , scheduler=lowercase )
__UpperCamelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
np.random.seed(0 )
__UpperCamelCase = torch.rand((1, 1, 1_0) )
__UpperCamelCase = pipe(generator=lowercase , encoding=lowercase )
__UpperCamelCase = output.images[0]
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:1_0]
__UpperCamelCase = np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase):
def __lowerCamelCase ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = torch_device
__UpperCamelCase = DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256""" )
__UpperCamelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = torch.Generator(device=lowercase ).manual_seed(4_2 )
__UpperCamelCase = pipe(generator=lowercase )
__UpperCamelCase = output.audios[0]
__UpperCamelCase = 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]
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:1_0]
__UpperCamelCase = np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 243 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> list[list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
if len(_SCREAMING_SNAKE_CASE ) == 1:
return [nums.copy()]
for _ in range(len(_SCREAMING_SNAKE_CASE ) ):
SCREAMING_SNAKE_CASE = nums.pop(0 )
SCREAMING_SNAKE_CASE = permute(_SCREAMING_SNAKE_CASE )
for perm in permutations:
perm.append(_SCREAMING_SNAKE_CASE )
result.extend(_SCREAMING_SNAKE_CASE )
nums.append(_SCREAMING_SNAKE_CASE )
return result
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
def backtrack(_SCREAMING_SNAKE_CASE ):
if start == len(_SCREAMING_SNAKE_CASE ) - 1:
output.append(nums[:] )
else:
for i in range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = nums[i], nums[start]
backtrack(start + 1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = nums[i], nums[start] # backtrack
SCREAMING_SNAKE_CASE = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
SCREAMING_SNAKE_CASE_ = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 296 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 | 1 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
__A : Dict = logging.get_logger(__name__)
@add_end_docstrings(_A )
class __UpperCamelCase ( _A ):
def __init__(self : Optional[Any] , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SNAKE_CASE : int):
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
requires_backends(self , "vision")
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING)
def SCREAMING_SNAKE_CASE__ (self : str , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : int=None):
A = {}
A = {}
if prompt is not None:
A = prompt
if generate_kwargs is not None:
A = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
A = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
" please use only one")
A = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__(self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__SCREAMING_SNAKE_CASE : Union[str, Any]):
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
def SCREAMING_SNAKE_CASE__ (self : List[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : str=None):
A = load_image(__SCREAMING_SNAKE_CASE)
if prompt is not None:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
raise ValueError(
F"""Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - but expected a single string. """
"Note also that one single text can be provided for conditional image to text generation.")
A = self.model.config.model_type
if model_type == "git":
A = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
A = self.tokenizer(text=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
A = [self.tokenizer.cls_token_id] + input_ids
A = torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
model_inputs.update({"input_ids": input_ids})
elif model_type == "pix2struct":
A = self.image_processor(images=__SCREAMING_SNAKE_CASE , header_text=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
A = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
A = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
model_inputs.update(__SCREAMING_SNAKE_CASE)
else:
raise ValueError(F"""Model type {model_type} does not support conditional text generation""")
else:
A = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
if self.model.config.model_type == "git" and prompt is None:
A = None
return model_inputs
def SCREAMING_SNAKE_CASE__ (self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str=None):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["input_ids"] , __SCREAMING_SNAKE_CASE)
and all(x is None for x in model_inputs["input_ids"])
):
A = None
if generate_kwargs is None:
A = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
A = model_inputs.pop(self.model.main_input_name)
A = self.model.generate(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
return model_outputs
def SCREAMING_SNAKE_CASE__ (self : Optional[int] , __SCREAMING_SNAKE_CASE : str):
A = []
for output_ids in model_outputs:
A = {
"generated_text": self.tokenizer.decode(
__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE , )
}
records.append(__SCREAMING_SNAKE_CASE)
return records
| 362 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ (self : Dict):
A = tempfile.mkdtemp()
A = BlipImageProcessor()
A = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
A = BlipaProcessor(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
processor.save_pretrained(self.tmpdirname)
def SCREAMING_SNAKE_CASE__ (self : Dict , **__SCREAMING_SNAKE_CASE : Any):
return AutoProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE).tokenizer
def SCREAMING_SNAKE_CASE__ (self : Tuple , **__SCREAMING_SNAKE_CASE : int):
return AutoProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE).image_processor
def SCREAMING_SNAKE_CASE__ (self : Optional[int]):
shutil.rmtree(self.tmpdirname)
def SCREAMING_SNAKE_CASE__ (self : Any):
A = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta)]
A = [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1)) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ (self : Any):
A = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
A = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)")
A = self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0)
A = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , __SCREAMING_SNAKE_CASE)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE)
def SCREAMING_SNAKE_CASE__ (self : Optional[Any]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = self.prepare_image_inputs()
A = image_processor(__SCREAMING_SNAKE_CASE , return_tensors="np")
A = processor(images=__SCREAMING_SNAKE_CASE , return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2)
def SCREAMING_SNAKE_CASE__ (self : Tuple):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = "lower newer"
A = processor(text=__SCREAMING_SNAKE_CASE)
A = tokenizer(__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def SCREAMING_SNAKE_CASE__ (self : Optional[int]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = "lower newer"
A = self.prepare_image_inputs()
A = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE)
self.assertListEqual(list(inputs.keys()) , ["pixel_values", "input_ids", "attention_mask"])
# test if it raises when no input is passed
with pytest.raises(__SCREAMING_SNAKE_CASE):
processor()
def SCREAMING_SNAKE_CASE__ (self : List[Any]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A = processor.batch_decode(__SCREAMING_SNAKE_CASE)
A = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
def SCREAMING_SNAKE_CASE__ (self : Optional[int]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = "lower newer"
A = self.prepare_image_inputs()
A = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE)
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys()) , ["pixel_values", "input_ids", "attention_mask"])
| 57 | 0 |
def _a ( UpperCAmelCase ) -> str:
"""simple docstring"""
lowerCamelCase__ : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCamelCase__ : Optional[Any] = ''''''
lowerCamelCase__ : Optional[Any] = ''''''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(UpperCAmelCase ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowerCamelCase__ , lowerCamelCase__ : Dict = 0, 0
# length[i] shows the length of palindromic substring with center i
lowerCamelCase__ : Tuple = [1 for i in range(len(UpperCAmelCase ) )]
# for each character in new_string find corresponding palindromic string
lowerCamelCase__ : List[Any] = 0
for j in range(len(UpperCAmelCase ) ):
lowerCamelCase__ : Union[str, Any] = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(UpperCAmelCase )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowerCamelCase__ : List[str] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowerCamelCase__ : Union[str, Any] = j - k + 1 # noqa: E741
lowerCamelCase__ : str = j + k - 1
# update max_length and start position
if max_length < length[j]:
lowerCamelCase__ : Optional[int] = length[j]
lowerCamelCase__ : Optional[Any] = j
# create that string
lowerCamelCase__ : str = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 142 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[int] = ["image_processor", "tokenizer"]
_UpperCAmelCase : Dict = "BridgeTowerImageProcessor"
_UpperCAmelCase : Dict = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self : Any , A : List[Any] , A : Tuple ) ->Dict:
super().__init__(A , A )
def __call__( self : str , A : int , A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , A : bool = True , A : Union[bool, str, PaddingStrategy] = False , A : Union[bool, str, TruncationStrategy] = None , A : Optional[int] = None , A : int = 0 , A : Optional[int] = None , A : Optional[bool] = None , A : Optional[bool] = None , A : bool = False , A : bool = False , A : bool = False , A : bool = False , A : bool = True , A : Optional[Union[str, TensorType]] = None , **A : Union[str, Any] , ) ->BatchEncoding:
lowerCamelCase__ : Optional[int] = self.tokenizer(
text=A , add_special_tokens=A , padding=A , truncation=A , max_length=A , stride=A , pad_to_multiple_of=A , return_token_type_ids=A , return_attention_mask=A , return_overflowing_tokens=A , return_special_tokens_mask=A , return_offsets_mapping=A , return_length=A , verbose=A , return_tensors=A , **A , )
# add pixel_values + pixel_mask
lowerCamelCase__ : int = self.image_processor(
A , return_tensors=A , do_normalize=A , do_center_crop=A , **A )
encoding.update(A )
return encoding
def __lowerCamelCase ( self : str , *A : Dict , **A : List[str] ) ->List[Any]:
return self.tokenizer.batch_decode(*A , **A )
def __lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Tuple ) ->Dict:
return self.tokenizer.decode(*A , **A )
@property
def __lowerCamelCase ( self : str ) ->Optional[Any]:
lowerCamelCase__ : Optional[int] = self.tokenizer.model_input_names
lowerCamelCase__ : Optional[int] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 142 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
__lowerCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Tuple = os.path.dirname(os.path.realpath(__a ) )
_a : int = os.path.join(__a , 'words.txt' )
_a : Union[str, Any] = ''
with open(__a ) as f:
_a : Dict = f.readline()
_a : Tuple = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )]
_a : str = [
word
for word in [sum(ord(__a ) - 6_4 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__a )
if __name__ == "__main__":
print(solution())
| 5 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
"""SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SqueezeBertForMaskedLM""",
"""SqueezeBertForMultipleChoice""",
"""SqueezeBertForQuestionAnswering""",
"""SqueezeBertForSequenceClassification""",
"""SqueezeBertForTokenClassification""",
"""SqueezeBertModel""",
"""SqueezeBertModule""",
"""SqueezeBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Dict , A : int , A : int , A : int , A : Union[str, Any]=0.0 , A : Optional[int] = None , A : str = "geglu" , A : Optional[int] = None , A : bool = False , A : bool = False , A : bool = False , A : bool = False , A : bool = True , A : str = "layer_norm" , A : bool = False , ) ->Any:
super().__init__()
lowerCamelCase__ : int = only_cross_attention
lowerCamelCase__ : Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
lowerCamelCase__ : Union[str, Any] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
F" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
lowerCamelCase__ : Optional[Any] = AdaLayerNorm(A , A )
elif self.use_ada_layer_norm_zero:
lowerCamelCase__ : int = AdaLayerNormZero(A , A )
else:
lowerCamelCase__ : Dict = nn.LayerNorm(A , elementwise_affine=A )
lowerCamelCase__ : Any = Attention(
query_dim=A , heads=A , dim_head=A , dropout=A , bias=A , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=A , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
lowerCamelCase__ : Tuple = (
AdaLayerNorm(A , A )
if self.use_ada_layer_norm
else nn.LayerNorm(A , elementwise_affine=A )
)
lowerCamelCase__ : int = Attention(
query_dim=A , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=A , dim_head=A , dropout=A , bias=A , upcast_attention=A , ) # is self-attn if encoder_hidden_states is none
else:
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Tuple = None
# 3. Feed-forward
lowerCamelCase__ : Optional[int] = nn.LayerNorm(A , elementwise_affine=A )
lowerCamelCase__ : Union[str, Any] = FeedForward(A , dropout=A , activation_fn=A , final_dropout=A )
# let chunk size default to None
lowerCamelCase__ : str = None
lowerCamelCase__ : Tuple = 0
def __lowerCamelCase ( self : Any , A : Optional[int] , A : int ) ->List[str]:
# Sets chunk feed-forward
lowerCamelCase__ : List[Any] = chunk_size
lowerCamelCase__ : List[str] = dim
def __lowerCamelCase ( self : str , A : torch.FloatTensor , A : Optional[torch.FloatTensor] = None , A : Optional[torch.FloatTensor] = None , A : Optional[torch.FloatTensor] = None , A : Optional[torch.LongTensor] = None , A : Dict[str, Any] = None , A : Optional[torch.LongTensor] = None , ) ->Tuple:
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
lowerCamelCase__ : Union[str, Any] = self.norma(A , A )
elif self.use_ada_layer_norm_zero:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Any = self.norma(
A , A , A , hidden_dtype=hidden_states.dtype )
else:
lowerCamelCase__ : List[str] = self.norma(A )
lowerCamelCase__ : str = cross_attention_kwargs if cross_attention_kwargs is not None else {}
lowerCamelCase__ : Any = self.attna(
A , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=A , **A , )
if self.use_ada_layer_norm_zero:
lowerCamelCase__ : Any = gate_msa.unsqueeze(1 ) * attn_output
lowerCamelCase__ : Optional[int] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
lowerCamelCase__ : int = (
self.norma(A , A ) if self.use_ada_layer_norm else self.norma(A )
)
lowerCamelCase__ : int = self.attna(
A , encoder_hidden_states=A , attention_mask=A , **A , )
lowerCamelCase__ : Any = attn_output + hidden_states
# 3. Feed-forward
lowerCamelCase__ : Union[str, Any] = self.norma(A )
if self.use_ada_layer_norm_zero:
lowerCamelCase__ : Optional[int] = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." )
lowerCamelCase__ : Optional[int] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
lowerCamelCase__ : Optional[int] = torch.cat(
[self.ff(A ) for hid_slice in norm_hidden_states.chunk(A , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
lowerCamelCase__ : Optional[int] = self.ff(A )
if self.use_ada_layer_norm_zero:
lowerCamelCase__ : Optional[Any] = gate_mlp.unsqueeze(1 ) * ff_output
lowerCamelCase__ : List[Any] = ff_output + hidden_states
return hidden_states
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Any , A : int , A : Optional[int] = None , A : int = 4 , A : float = 0.0 , A : str = "geglu" , A : bool = False , ) ->int:
super().__init__()
lowerCamelCase__ : List[Any] = int(dim * mult )
lowerCamelCase__ : List[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
lowerCamelCase__ : int = GELU(A , A )
if activation_fn == "gelu-approximate":
lowerCamelCase__ : Optional[int] = GELU(A , A , approximate='''tanh''' )
elif activation_fn == "geglu":
lowerCamelCase__ : Any = GEGLU(A , A )
elif activation_fn == "geglu-approximate":
lowerCamelCase__ : int = ApproximateGELU(A , A )
lowerCamelCase__ : Union[str, Any] = nn.ModuleList([] )
# project in
self.net.append(A )
# project dropout
self.net.append(nn.Dropout(A ) )
# project out
self.net.append(nn.Linear(A , A ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(A ) )
def __lowerCamelCase ( self : Dict , A : List[Any] ) ->Optional[Any]:
for module in self.net:
lowerCamelCase__ : int = module(A )
return hidden_states
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Tuple , A : int , A : int , A : str = "none" ) ->Optional[Any]:
super().__init__()
lowerCamelCase__ : List[Any] = nn.Linear(A , A )
lowerCamelCase__ : Any = approximate
def __lowerCamelCase ( self : List[str] , A : Tuple ) ->str:
if gate.device.type != "mps":
return F.gelu(A , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def __lowerCamelCase ( self : List[str] , A : str ) ->Optional[int]:
lowerCamelCase__ : List[str] = self.proj(A )
lowerCamelCase__ : Optional[int] = self.gelu(A )
return hidden_states
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Tuple , A : int , A : int ) ->Dict:
super().__init__()
lowerCamelCase__ : Optional[Any] = nn.Linear(A , dim_out * 2 )
def __lowerCamelCase ( self : List[Any] , A : List[Any] ) ->Tuple:
if gate.device.type != "mps":
return F.gelu(A )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def __lowerCamelCase ( self : Any , A : Union[str, Any] ) ->Any:
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.proj(A ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(A )
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Any , A : int , A : int ) ->str:
super().__init__()
lowerCamelCase__ : Optional[int] = nn.Linear(A , A )
def __lowerCamelCase ( self : Union[str, Any] , A : Dict ) ->Optional[Any]:
lowerCamelCase__ : List[str] = self.proj(A )
return x * torch.sigmoid(1.7_02 * x )
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : int , A : Dict , A : Optional[Any] ) ->str:
super().__init__()
lowerCamelCase__ : List[str] = nn.Embedding(A , A )
lowerCamelCase__ : str = nn.SiLU()
lowerCamelCase__ : int = nn.Linear(A , embedding_dim * 2 )
lowerCamelCase__ : Optional[Any] = nn.LayerNorm(A , elementwise_affine=A )
def __lowerCamelCase ( self : int , A : Union[str, Any] , A : Union[str, Any] ) ->Union[str, Any]:
lowerCamelCase__ : Union[str, Any] = self.linear(self.silu(self.emb(A ) ) )
lowerCamelCase__ , lowerCamelCase__ : List[str] = torch.chunk(A , 2 )
lowerCamelCase__ : Any = self.norm(A ) * (1 + scale) + shift
return x
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : str , A : Optional[Any] , A : int ) ->str:
super().__init__()
lowerCamelCase__ : Union[str, Any] = CombinedTimestepLabelEmbeddings(A , A )
lowerCamelCase__ : int = nn.SiLU()
lowerCamelCase__ : List[str] = nn.Linear(A , 6 * embedding_dim , bias=A )
lowerCamelCase__ : str = nn.LayerNorm(A , elementwise_affine=A , eps=1e-6 )
def __lowerCamelCase ( self : List[str] , A : Any , A : List[Any] , A : Tuple , A : Dict=None ) ->Union[str, Any]:
lowerCamelCase__ : List[Any] = self.linear(self.silu(self.emb(A , A , hidden_dtype=A ) ) )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = emb.chunk(6 , dim=1 )
lowerCamelCase__ : List[Any] = self.norm(A ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Any , A : int , A : int , A : int , A : Optional[str] = None , A : float = 1e-5 ) ->Any:
super().__init__()
lowerCamelCase__ : int = num_groups
lowerCamelCase__ : List[str] = eps
if act_fn is None:
lowerCamelCase__ : Tuple = None
else:
lowerCamelCase__ : Dict = get_activation(A )
lowerCamelCase__ : Any = nn.Linear(A , out_dim * 2 )
def __lowerCamelCase ( self : List[str] , A : Optional[int] , A : str ) ->Tuple:
if self.act:
lowerCamelCase__ : Union[str, Any] = self.act(A )
lowerCamelCase__ : Optional[Any] = self.linear(A )
lowerCamelCase__ : Optional[Any] = emb[:, :, None, None]
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = emb.chunk(2 , dim=1 )
lowerCamelCase__ : str = F.group_norm(A , self.num_groups , eps=self.eps )
lowerCamelCase__ : Dict = x * (1 + scale) + shift
return x
| 142 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _a ( UpperCAmelCase ) -> List[str]:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = 384
if "tiny" in model_name:
lowerCamelCase__ : Optional[int] = [3, 3, 9, 3]
lowerCamelCase__ : Tuple = [96, 192, 384, 768]
if "small" in model_name:
lowerCamelCase__ : Dict = [3, 3, 27, 3]
lowerCamelCase__ : Any = [96, 192, 384, 768]
if "base" in model_name:
lowerCamelCase__ : Optional[int] = [3, 3, 27, 3]
lowerCamelCase__ : Optional[Any] = [128, 256, 512, 1024]
lowerCamelCase__ : List[Any] = 512
if "large" in model_name:
lowerCamelCase__ : List[str] = [3, 3, 27, 3]
lowerCamelCase__ : int = [192, 384, 768, 1536]
lowerCamelCase__ : str = 768
if "xlarge" in model_name:
lowerCamelCase__ : Any = [3, 3, 27, 3]
lowerCamelCase__ : str = [256, 512, 1024, 2048]
lowerCamelCase__ : Optional[Any] = 1024
# set label information
lowerCamelCase__ : Optional[int] = 150
lowerCamelCase__ : Any = '''huggingface/label-files'''
lowerCamelCase__ : Any = '''ade20k-id2label.json'''
lowerCamelCase__ : str = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
lowerCamelCase__ : Optional[Any] = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ : List[Any] = {v: k for k, v in idalabel.items()}
lowerCamelCase__ : Any = ConvNextConfig(
depths=UpperCAmelCase , hidden_sizes=UpperCAmelCase , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
lowerCamelCase__ : Dict = UperNetConfig(
backbone_config=UpperCAmelCase , auxiliary_in_channels=UpperCAmelCase , num_labels=UpperCAmelCase , idalabel=UpperCAmelCase , labelaid=UpperCAmelCase , )
return config
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
lowerCamelCase__ : Dict = []
# fmt: off
# stem
rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''') )
rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''') )
rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''') )
rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"backbone.stages.{i}.{j}.gamma", f"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter") )
rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.weight", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight") )
rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.bias", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias") )
rename_keys.append((f"backbone.stages.{i}.{j}.norm.weight", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight") )
rename_keys.append((f"backbone.stages.{i}.{j}.norm.bias", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias") )
rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight") )
rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias") )
rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight") )
rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias") )
if i > 0:
rename_keys.append((f"backbone.downsample_layers.{i}.0.weight", f"backbone.encoder.stages.{i}.downsampling_layer.0.weight") )
rename_keys.append((f"backbone.downsample_layers.{i}.0.bias", f"backbone.encoder.stages.{i}.downsampling_layer.0.bias") )
rename_keys.append((f"backbone.downsample_layers.{i}.1.weight", f"backbone.encoder.stages.{i}.downsampling_layer.1.weight") )
rename_keys.append((f"backbone.downsample_layers.{i}.1.bias", f"backbone.encoder.stages.{i}.downsampling_layer.1.bias") )
rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight") )
rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias") )
# decode head
rename_keys.extend(
[
('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
] )
# fmt: on
return rename_keys
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__ : str = dct.pop(UpperCAmelCase )
lowerCamelCase__ : List[Any] = val
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : str = {
'''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
'''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
'''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
'''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
'''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
}
lowerCamelCase__ : Union[str, Any] = model_name_to_url[model_name]
lowerCamelCase__ : int = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''state_dict''']
lowerCamelCase__ : List[str] = get_upernet_config(UpperCAmelCase )
lowerCamelCase__ : Tuple = UperNetForSemanticSegmentation(UpperCAmelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
lowerCamelCase__ : Optional[int] = state_dict.pop(UpperCAmelCase )
if "bn" in key:
lowerCamelCase__ : str = key.replace('''bn''' , '''batch_norm''' )
lowerCamelCase__ : List[Any] = val
# rename keys
lowerCamelCase__ : List[str] = create_rename_keys(UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
model.load_state_dict(UpperCAmelCase )
# verify on image
lowerCamelCase__ : Any = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
lowerCamelCase__ : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw ).convert('''RGB''' )
lowerCamelCase__ : Optional[int] = SegformerImageProcessor()
lowerCamelCase__ : Any = processor(UpperCAmelCase , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
lowerCamelCase__ : List[Any] = model(UpperCAmelCase )
if model_name == "upernet-convnext-tiny":
lowerCamelCase__ : Any = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] )
elif model_name == "upernet-convnext-small":
lowerCamelCase__ : List[str] = torch.tensor(
[[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]] )
elif model_name == "upernet-convnext-base":
lowerCamelCase__ : str = torch.tensor(
[[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]] )
elif model_name == "upernet-convnext-large":
lowerCamelCase__ : Optional[int] = torch.tensor(
[[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]] )
elif model_name == "upernet-convnext-xlarge":
lowerCamelCase__ : Tuple = torch.tensor(
[[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCAmelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCAmelCase )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
print(f"Pushing model and processor for {model_name} to hub" )
model.push_to_hub(f"openmmlab/{model_name}" )
processor.push_to_hub(f"openmmlab/{model_name}" )
if __name__ == "__main__":
_A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-convnext-tiny',
type=str,
choices=[F'''upernet-convnext-{size}''' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
help='Name of the ConvNext UperNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
_A : Tuple = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 142 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__A = data_utils.TransfoXLTokenizer
__A = data_utils.TransfoXLCorpus
__A = data_utils
__A = data_utils
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(UpperCamelCase__ , 'rb' ) as fp:
__lowerCamelCase = pickle.load(UpperCamelCase__ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
__lowerCamelCase = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F"""Save vocabulary to {pytorch_vocab_dump_path}""" )
__lowerCamelCase = corpus.vocab.__dict__
torch.save(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , UpperCamelCase__ )
__lowerCamelCase = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(UpperCamelCase__ , UpperCamelCase__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
__lowerCamelCase = os.path.abspath(UpperCamelCase__ )
__lowerCamelCase = os.path.abspath(UpperCamelCase__ )
print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
__lowerCamelCase = TransfoXLConfig()
else:
__lowerCamelCase = TransfoXLConfig.from_json_file(UpperCamelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
__lowerCamelCase = TransfoXLLMHeadModel(UpperCamelCase__ )
__lowerCamelCase = load_tf_weights_in_transfo_xl(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Save pytorch-model
__lowerCamelCase = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
print(F"""Save PyTorch model to {os.path.abspath(UpperCamelCase__ )}""" )
torch.save(model.state_dict() , UpperCamelCase__ )
print(F"""Save configuration file to {os.path.abspath(UpperCamelCase__ )}""" )
with open(UpperCamelCase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--tf_checkpoint_path",
default="",
type=str,
help="An optional path to a TensorFlow checkpoint path to be converted.",
)
parser.add_argument(
"--transfo_xl_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--transfo_xl_dataset_file",
default="",
type=str,
help="An optional dataset file to be converted in a vocabulary.",
)
__A = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
__lowercase ,__lowercase : List[str] = grid.shape
__lowercase : str = [-1, 1, 0, 0]
__lowercase : Union[str, Any] = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
__lowercase ,__lowercase : str = [(0, source)], set()
__lowercase : Any = np.full((rows, cols) , np.inf )
__lowercase : Dict = 0
__lowercase : Optional[int] = np.empty((rows, cols) , dtype=__UpperCamelCase )
__lowercase : int = None
while queue:
((__lowercase) ,(__lowercase)) : List[Any] = heappop(__UpperCamelCase )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
__lowercase : Dict = []
while (x, y) != source:
path.append((x, y) )
__lowercase ,__lowercase : Optional[Any] = predecessors[x, y]
path.append(__UpperCamelCase ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(__UpperCamelCase ) ):
__lowercase ,__lowercase : int = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
__lowercase : List[Any] = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(__UpperCamelCase , (dist + 1, (nx, ny)) )
__lowercase : Optional[int] = dist + 1
__lowercase : List[Any] = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 249 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case )
class UpperCAmelCase_ ( snake_case ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
UpperCamelCase =field(default="question-answering-extractive" , metadata={"include_in_asdict_even_if_is_default": True} )
UpperCamelCase =Features({"question": Value("string" ), "context": Value("string" )} )
UpperCamelCase =Features(
{
"answers": Sequence(
{
"text": Value("string" ),
"answer_start": Value("int32" ),
} )
} )
UpperCamelCase ="question"
UpperCamelCase ="context"
UpperCamelCase ="answers"
@property
def _lowerCamelCase ( self ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 249 | 1 |
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 ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = image.size
lowercase__ = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
lowercase__ = image.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] )
lowercase__ = np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_5_5.0
lowercase__ = image[None].transpose(0 , 3 , 1 , 2 )
lowercase__ = torch.from_numpy(lowerCamelCase_ )
return 2.0 * image - 1.0
class A ( lowerCamelCase__ ):
'''simple docstring'''
def __init__(self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , ) -> Dict:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=__lowerCamelCase , unet=__lowerCamelCase , scheduler=__lowerCamelCase )
@torch.no_grad()
def __call__(self : Tuple , _UpperCAmelCase : Optional[Any] = None , _UpperCAmelCase : Tuple = 1 , _UpperCAmelCase : Any = 100 , _UpperCAmelCase : List[str] = 0.0 , _UpperCAmelCase : Optional[Any] = None , _UpperCAmelCase : Union[str, Any] = "pil" , _UpperCAmelCase : Any = True , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
if isinstance(__lowerCamelCase , PIL.Image.Image ):
lowercase__ = 1
elif isinstance(__lowerCamelCase , torch.Tensor ):
lowercase__ = image.shape[0]
else:
raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase )}''' )
if isinstance(__lowerCamelCase , PIL.Image.Image ):
lowercase__ = preprocess(__lowerCamelCase )
lowercase__ = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
lowercase__ = (batch_size, self.unet.config.in_channels // 2, height, width)
lowercase__ = next(self.unet.parameters() ).dtype
lowercase__ = randn_tensor(__lowerCamelCase , generator=__lowerCamelCase , device=self.device , dtype=__lowerCamelCase )
lowercase__ = image.to(device=self.device , dtype=__lowerCamelCase )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(__lowerCamelCase , device=self.device )
lowercase__ = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
lowercase__ = 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]
lowercase__ = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowercase__ = {}
if accepts_eta:
lowercase__ = eta
for t in self.progress_bar(__lowerCamelCase ):
# concat latents and low resolution image in the channel dimension.
lowercase__ = torch.cat([latents, image] , dim=1 )
lowercase__ = self.scheduler.scale_model_input(__lowerCamelCase , __lowerCamelCase )
# predict the noise residual
lowercase__ = self.unet(__lowerCamelCase , __lowerCamelCase ).sample
# compute the previous noisy sample x_t -> x_t-1
lowercase__ = self.scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase ).prev_sample
# decode the image latents with the VQVAE
lowercase__ = self.vqvae.decode(__lowerCamelCase ).sample
lowercase__ = torch.clamp(__lowerCamelCase , -1.0 , 1.0 )
lowercase__ = image / 2 + 0.5
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(__lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCamelCase )
| 356 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
return f'''gaussian_noise_s={seed}_shape={"_".join([str(_UpperCAmelCase ) for s in shape] )}.npy'''
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Optional[Any]=(4, 4, 64, 64) , _UpperCAmelCase : Any=False ) -> List[Any]:
"""simple docstring"""
lowercase__ = jnp.bfloataa if fpaa else jnp.floataa
lowercase__ = jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase , _UpperCAmelCase ) ) , dtype=_UpperCAmelCase )
return image
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : str=False , _UpperCAmelCase : str="CompVis/stable-diffusion-v1-4" ) -> Dict:
"""simple docstring"""
lowercase__ = jnp.bfloataa if fpaa else jnp.floataa
lowercase__ = """bf16""" if fpaa else None
lowercase__ , lowercase__ = FlaxUNetaDConditionModel.from_pretrained(
_UpperCAmelCase , subfolder="""unet""" , dtype=_UpperCAmelCase , revision=_UpperCAmelCase )
return model, params
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int=0 , _UpperCAmelCase : Optional[int]=(4, 77, 768) , _UpperCAmelCase : Any=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = jnp.bfloataa if fpaa else jnp.floataa
lowercase__ = jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase , _UpperCAmelCase ) ) , dtype=_UpperCAmelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
[17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
[8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
[3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
# fmt: on
] )
def lowerCamelCase__ (self : int , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int ) -> str:
"""simple docstring"""
lowercase__ , lowercase__ = self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4""" , fpaa=_UpperCAmelCase )
lowercase__ = self.get_latents(_UpperCAmelCase , fpaa=_UpperCAmelCase )
lowercase__ = self.get_encoder_hidden_states(_UpperCAmelCase , fpaa=_UpperCAmelCase )
lowercase__ = model.apply(
{"""params""": params} , _UpperCAmelCase , jnp.array(_UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=_UpperCAmelCase , ).sample
assert sample.shape == latents.shape
lowercase__ = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
lowercase__ = jnp.array(_UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
[17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
[8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
[3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
# fmt: on
] )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
lowercase__ , lowercase__ = self.get_unet_model(model_id="""stabilityai/stable-diffusion-2""" , fpaa=_UpperCAmelCase )
lowercase__ = self.get_latents(_UpperCAmelCase , shape=(4, 4, 96, 96) , fpaa=_UpperCAmelCase )
lowercase__ = self.get_encoder_hidden_states(_UpperCAmelCase , shape=(4, 77, 1024) , fpaa=_UpperCAmelCase )
lowercase__ = model.apply(
{"""params""": params} , _UpperCAmelCase , jnp.array(_UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=_UpperCAmelCase , ).sample
assert sample.shape == latents.shape
lowercase__ = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
lowercase__ = jnp.array(_UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-2 )
| 146 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __snake_case ( lowerCamelCase_ ):
def __init__( self : List[Any] , _lowercase : pyspark.sql.DataFrame , _lowercase : Optional[NamedSplit] = None , _lowercase : Optional[Features] = None , _lowercase : bool = True , _lowercase : str = None , _lowercase : bool = False , _lowercase : str = None , _lowercase : bool = True , _lowercase : str = "arrow" , **_lowercase : Any , ):
"""simple docstring"""
super().__init__(
split=_lowercase , features=_lowercase , cache_dir=_lowercase , keep_in_memory=_lowercase , streaming=_lowercase , **_lowercase , )
SCREAMING_SNAKE_CASE__ = load_from_cache_file
SCREAMING_SNAKE_CASE__ = file_format
SCREAMING_SNAKE_CASE__ = Spark(
df=_lowercase , features=_lowercase , cache_dir=_lowercase , working_dir=_lowercase , **_lowercase , )
def __a ( self : int ):
"""simple docstring"""
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
SCREAMING_SNAKE_CASE__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=_lowercase , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 219 | import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase : str = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_sentencepiece_available():
import sentencepiece as sp
__lowerCamelCase : Any = 5
__lowerCamelCase : Dict = 10
@require_sentencepiece
@require_tokenizers
class __snake_case ( lowerCamelCase_ , unittest.TestCase ):
lowerCAmelCase_ = SpeechaTextTokenizer
lowerCAmelCase_ = False
lowerCAmelCase_ = True
def __a ( self : Tuple ):
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ = sp.SentencePieceProcessor()
spm_model.Load(_lowercase )
SCREAMING_SNAKE_CASE__ = ["""<s>""", """<pad>""", """</s>""", """<unk>"""]
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(_lowercase ) )]
SCREAMING_SNAKE_CASE__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ = Path(self.tmpdirname )
save_json(_lowercase , save_dir / VOCAB_FILES_NAMES["""vocab_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_lowercase , save_dir / VOCAB_FILES_NAMES["""spm_file"""] )
SCREAMING_SNAKE_CASE__ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __a ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """<pad>"""
SCREAMING_SNAKE_CASE__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_lowercase ) , 10_01 )
def __a ( self : List[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_01 )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowercase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [2_89, 50, 14, 1_74, 3_86] , )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowercase , [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""", """."""] , )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_tokens_to_ids(_lowercase )
self.assertListEqual(_lowercase , [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertListEqual(
_lowercase , [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>""", """."""] , )
@slow
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {"""input_ids""": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 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, 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, 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, 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, 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, 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=_lowercase , model_name="""facebook/s2t-small-mustc-en-de-st""" , revision="""a14f04cf0776c02f62a8cb800cf7909e15ea23ad""" , )
@require_sentencepiece
class __snake_case ( unittest.TestCase ):
lowerCAmelCase_ = "valhalla/s2t_mustc_multilinguial_medium"
lowerCAmelCase_ = "C'est trop cool"
lowerCAmelCase_ = "Esto es genial"
@classmethod
def __a ( cls : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def __a ( self : Dict ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id["""pt"""] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id["""ru"""] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id["""it"""] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id["""de"""] , 11 )
def __a ( self : Union[str, Any] ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size , 1_00_00 )
def __a ( self : int ):
"""simple docstring"""
self.assertIn(_lowercase , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE__ = [ES_CODE, 4, 16_01, 47, 76_47, 2]
SCREAMING_SNAKE_CASE__ = self.tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
SCREAMING_SNAKE_CASE__ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertNotIn(self.tokenizer.eos_token , _lowercase )
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """fr"""
SCREAMING_SNAKE_CASE__ = self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , _lowercase )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def __a ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """fr"""
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
SCREAMING_SNAKE_CASE__ = """es"""
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 219 | 1 |
'''simple docstring'''
class A__ :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase__ : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : str = arr.split("," )
def _lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(self.array[0] )] * len(self.array )
_UpperCAmelCase : str = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
_UpperCAmelCase : Optional[Any] = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
_UpperCAmelCase : Tuple = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
__a = input('please input some numbers:')
__a = SubArray(whole_array)
__a = array.solve_sub_array()
print(('the results is:', re)) | 367 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = '''time_series_transformer'''
UpperCamelCase_ : Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "student_t" , lowerCAmelCase__ : str = "nll" , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase__ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : int = 1_0_0 , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : Dict=True , **lowerCAmelCase__ : Tuple , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = prediction_length
_UpperCAmelCase : Optional[Any] = context_length or prediction_length
_UpperCAmelCase : Optional[Any] = distribution_output
_UpperCAmelCase : Union[str, Any] = loss
_UpperCAmelCase : Dict = input_size
_UpperCAmelCase : int = num_time_features
_UpperCAmelCase : Any = lags_sequence
_UpperCAmelCase : Dict = scaling
_UpperCAmelCase : Tuple = num_dynamic_real_features
_UpperCAmelCase : Dict = num_static_real_features
_UpperCAmelCase : Union[str, Any] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : Optional[int] = cardinality
else:
_UpperCAmelCase : Optional[Any] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
_UpperCAmelCase : List[Any] = embedding_dimension
else:
_UpperCAmelCase : Optional[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_UpperCAmelCase : str = num_parallel_samples
# Transformer architecture configuration
_UpperCAmelCase : Union[str, Any] = input_size * len(lowerCAmelCase__ ) + self._number_of_features
_UpperCAmelCase : str = d_model
_UpperCAmelCase : Optional[Any] = encoder_attention_heads
_UpperCAmelCase : Dict = decoder_attention_heads
_UpperCAmelCase : List[Any] = encoder_ffn_dim
_UpperCAmelCase : str = decoder_ffn_dim
_UpperCAmelCase : Dict = encoder_layers
_UpperCAmelCase : str = decoder_layers
_UpperCAmelCase : Any = dropout
_UpperCAmelCase : str = attention_dropout
_UpperCAmelCase : List[Any] = activation_dropout
_UpperCAmelCase : Dict = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Optional[Any] = activation_function
_UpperCAmelCase : Tuple = init_std
_UpperCAmelCase : List[str] = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def _lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
) | 17 | 0 |
def lowerCAmelCase__ ( lowerCamelCase_ : int = 1000000):
'''simple docstring'''
lowerCAmelCase__ : Tuple = 1
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : Optional[int] = {1: 1}
for inputa in range(2 ,lowerCamelCase_):
lowerCAmelCase__ : Union[str, Any] = 0
lowerCAmelCase__ : Dict = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
lowerCAmelCase__ : Tuple = (3 * number) + 1
counter += 1
if inputa not in counters:
lowerCAmelCase__ : Union[str, Any] = counter
if counter > pre_counter:
lowerCAmelCase__ : int = inputa
lowerCAmelCase__ : List[Any] = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 129 |
from jiwer import compute_measures
import datasets
__snake_case : Dict ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
__snake_case : Optional[Any] ='\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
__snake_case : Any ='\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCamelCase__ ( datasets.Metric):
'''simple docstring'''
def lowerCAmelCase__ (self ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/jitsi/jiwer/'''] ,reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] ,)
def lowerCAmelCase__ (self ,__lowerCamelCase=None ,__lowerCamelCase=None ,__lowerCamelCase=False ) -> Any:
"""simple docstring"""
if concatenate_texts:
return compute_measures(__lowerCamelCase ,__lowerCamelCase )["wer"]
else:
lowerCAmelCase__ : str = 0
lowerCAmelCase__ : Tuple = 0
for prediction, reference in zip(__lowerCamelCase ,__lowerCamelCase ):
lowerCAmelCase__ : Dict = compute_measures(__lowerCamelCase ,__lowerCamelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 129 | 1 |
def __lowercase ( a__ ) -> list:
def merge(a__ , a__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(a__ ) <= 1:
return collection
__SCREAMING_SNAKE_CASE = len(a__ ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ : Optional[Any] =input('''Enter numbers separated by a comma:\n''').strip()
lowerCAmelCase__ : Any =[int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 118 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
UpperCamelCase__ : str = field(
metadata={'''help''': '''The output directory where the model will be written.'''} , )
UpperCamelCase__ : str = field(
metadata={
'''help''': (
'''The encoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train an encoder model from scratch.'''
)
} , )
UpperCamelCase__ : str = field(
metadata={
'''help''': (
'''The decoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train a decoder model from scratch.'''
)
} , )
UpperCamelCase__ : Optional[str] = field(
default=UpperCamelCase_ , metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
UpperCamelCase__ : Optional[str] = field(
default=UpperCamelCase_ , metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )
def __lowercase ( ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments,) )
((__SCREAMING_SNAKE_CASE) , ) = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
__SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=a__ , decoder_config=a__ , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
__SCREAMING_SNAKE_CASE = decoder_config.decoder_start_token_id
__SCREAMING_SNAKE_CASE = decoder_config.pad_token_id
if decoder_start_token_id is None:
__SCREAMING_SNAKE_CASE = decoder_config.bos_token_id
if pad_token_id is None:
__SCREAMING_SNAKE_CASE = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
__SCREAMING_SNAKE_CASE = decoder_config.eos_token_id
__SCREAMING_SNAKE_CASE = decoder_start_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 118 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def UpperCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
_lowercase =os.path.dirname(os.path.realpath(__snake_case ) )
_lowercase =os.path.join(__snake_case , '''words.txt''' )
_lowercase =''''''
with open(__snake_case ) as f:
_lowercase =f.readline()
_lowercase =[word.strip('''"''' ) for word in words.strip('''\r\n''' ).split(''',''' )]
_lowercase =[
word
for word in [sum(ord(__snake_case ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__snake_case )
if __name__ == "__main__":
print(solution())
| 5 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCAmelCase__ = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 5 | 1 |
from math import ceil
def lowerCamelCase__ ( _lowercase = 1001 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase_ : List[Any] = 2 * i + 1
UpperCAmelCase_ : Optional[int] = 2 * i
UpperCAmelCase_ : Any = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
__a = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number') | 235 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__a = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : int = state_dict.pop(_lowercase )
UpperCAmelCase_ : Optional[int] = val
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : str = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCAmelCase_ : List[Any] = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCAmelCase_ : Optional[Any] = value
else:
UpperCAmelCase_ : Union[str, Any] = value
return new_state_dict
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : int = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase_ : Dict = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : Any = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[:256, :]
UpperCAmelCase_ : Optional[int] = in_proj_bias[:256]
UpperCAmelCase_ : Tuple = in_proj_weight[256:512, :]
UpperCAmelCase_ : List[Any] = in_proj_bias[256:512]
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[-256:, :]
UpperCAmelCase_ : str = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
UpperCAmelCase_ : Any = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : Optional[int] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[:256, :]
UpperCAmelCase_ : List[str] = in_proj_bias[:256]
UpperCAmelCase_ : Optional[int] = in_proj_weight[256:512, :]
UpperCAmelCase_ : str = in_proj_bias[256:512]
UpperCAmelCase_ : Optional[Any] = in_proj_weight[-256:, :]
UpperCAmelCase_ : Union[str, Any] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase_ : List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCAmelCase_ : List[Any] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
UpperCAmelCase_ : List[str] = in_proj_weight_cross_attn[:256, :]
UpperCAmelCase_ : str = in_proj_bias_cross_attn[:256]
UpperCAmelCase_ : int = in_proj_weight_cross_attn[256:512, :]
UpperCAmelCase_ : Tuple = in_proj_bias_cross_attn[256:512]
UpperCAmelCase_ : List[str] = in_proj_weight_cross_attn[-256:, :]
UpperCAmelCase_ : Dict = in_proj_bias_cross_attn[-256:]
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_ : List[Any] = image.size
UpperCAmelCase_ : List[Any] = max(_lowercase , _lowercase )
UpperCAmelCase_ : Dict = 800 if '''detection''' in checkpoint_url else 1000
UpperCAmelCase_ : Any = target_max_size / current_max_size
UpperCAmelCase_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = F.to_tensor(_lowercase )
UpperCAmelCase_ : Optional[Any] = F.normalize(_lowercase , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCAmelCase_ : Any = torch.hub.load_state_dict_from_url(_lowercase , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(_lowercase , _lowercase , _lowercase )
UpperCAmelCase_ : Optional[int] = rename_backbone_keys(_lowercase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowercase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_ : int = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCAmelCase_ : int = state_dict.pop(_lowercase )
UpperCAmelCase_ : Dict = val
# create HuggingFace model and load state dict
UpperCAmelCase_ : str = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCAmelCase_ : Any = 15
UpperCAmelCase_ : List[str] = 2
UpperCAmelCase_ : Union[str, Any] = {0: '''table''', 1: '''table rotated'''}
UpperCAmelCase_ : str = idalabel
UpperCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
UpperCAmelCase_ : Optional[Any] = 125
UpperCAmelCase_ : Optional[Any] = 6
UpperCAmelCase_ : Dict = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCAmelCase_ : Optional[Any] = idalabel
UpperCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ : Union[str, Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCAmelCase_ : Union[str, Any] = TableTransformerForObjectDetection(_lowercase )
model.load_state_dict(_lowercase )
model.eval()
# verify our conversion
UpperCAmelCase_ : str = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
UpperCAmelCase_ : Dict = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=_lowercase )
UpperCAmelCase_ : Dict = Image.open(_lowercase ).convert('''RGB''' )
UpperCAmelCase_ : Any = normalize(resize(_lowercase , _lowercase ) ).unsqueeze(0 )
UpperCAmelCase_ : Dict = model(_lowercase )
if "detection" in checkpoint_url:
UpperCAmelCase_ : Any = (1, 15, 3)
UpperCAmelCase_ : Optional[int] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
UpperCAmelCase_ : Any = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
UpperCAmelCase_ : Any = (1, 125, 7)
UpperCAmelCase_ : Any = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
UpperCAmelCase_ : str = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , _lowercase , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , _lowercase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
image_processor.save_pretrained(_lowercase )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCAmelCase_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(_lowercase )
image_processor.push_to_hub(_lowercase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__a = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 235 | 1 |
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