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from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''torch''', '''torchsde''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["torch", "torchsde"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch", "torchsde"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch", "torchsde"] )
| 62 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'speech_to_text_2'
lowerCamelCase_ = ['past_key_values']
lowerCamelCase_ = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : int , UpperCAmelCase__ : Dict=10000 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : str=2048 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]="relu" , UpperCAmelCase__ : List[str]=256 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : List[Any]=1024 , **UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : List[str] =vocab_size
lowercase : Optional[int] =d_model
lowercase : Optional[Any] =decoder_ffn_dim
lowercase : Any =decoder_layers
lowercase : Dict =decoder_attention_heads
lowercase : List[Any] =dropout
lowercase : List[Any] =attention_dropout
lowercase : Any =activation_dropout
lowercase : Optional[Any] =activation_function
lowercase : Optional[int] =init_std
lowercase : Dict =decoder_layerdrop
lowercase : Optional[int] =use_cache
lowercase : Optional[Any] =decoder_layers
lowercase : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase : str =max_target_positions
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 92 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A__ ( A : List[str]):
'''simple docstring'''
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
def A__ ( A : str):
'''simple docstring'''
for char in word:
UpperCamelCase : Optional[int] = ord(A)
if not _is_chinese_char(A):
return 0
return 1
def A__ ( A : List[str]):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = set()
for token in tokens:
UpperCamelCase : Dict = len(A) > 1 and is_chinese(A)
if chinese_word:
word_set.add(A)
UpperCamelCase : int = list(A)
return word_list
def A__ ( A : List[str] , A : set()):
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
UpperCamelCase : str = max([len(A) for w in chinese_word_set])
UpperCamelCase : str = bert_tokens
UpperCamelCase : Optional[int] = 0, len(A)
while start < end:
UpperCamelCase : List[str] = True
if is_chinese(bert_word[start]):
UpperCamelCase : Tuple = min(end - start , A)
for i in range(A , 1 , -1):
UpperCamelCase : str = "".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
UpperCamelCase : Optional[int] = "##" + bert_word[j]
UpperCamelCase : List[str] = start + i
UpperCamelCase : Optional[Any] = False
break
if single_word:
start += 1
return bert_word
def A__ ( A : List[str] , A : LTP , A : BertTokenizer):
'''simple docstring'''
UpperCamelCase : List[Any] = []
for i in range(0 , len(A) , 1_00):
UpperCamelCase : Any = ltp_tokenizer.seg(lines[i : i + 1_00])[0]
UpperCamelCase : Dict = [get_chinese_word(A) for r in res]
ltp_res.extend(A)
assert len(A) == len(A)
UpperCamelCase : Dict = []
for i in range(0 , len(A) , 1_00):
UpperCamelCase : Optional[int] = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=A , truncation=A , max_length=5_12)
bert_res.extend(res["input_ids"])
assert len(A) == len(A)
UpperCamelCase : int = []
for input_ids, chinese_word in zip(A , A):
UpperCamelCase : int = []
for id in input_ids:
UpperCamelCase : Tuple = bert_tokenizer._convert_id_to_token(A)
input_tokens.append(A)
UpperCamelCase : Tuple = add_sub_symbol(A , A)
UpperCamelCase : Optional[Any] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(A):
if token[:2] == "##":
UpperCamelCase : Dict = token[2:]
# save chinese tokens' pos
if len(A) == 1 and _is_chinese_char(ord(A)):
ref_id.append(A)
ref_ids.append(A)
assert len(A) == len(A)
return ref_ids
def A__ ( A : int):
'''simple docstring'''
with open(args.file_name , "r" , encoding="utf-8") as f:
UpperCamelCase : int = f.readlines()
UpperCamelCase : int = [line.strip() for line in data if len(A) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
UpperCamelCase : List[Any] = LTP(args.ltp) # faster in GPU device
UpperCamelCase : int = BertTokenizer.from_pretrained(args.bert)
UpperCamelCase : Tuple = prepare_ref(A , A , A)
with open(args.save_path , "w" , encoding="utf-8") as f:
UpperCamelCase : Any = [json.dumps(A) + "\n" for ref in ref_ids]
f.writelines(A)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
)
parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')
lowerCAmelCase_ = parser.parse_args()
main(args)
| 710 |
'''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.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def A__ ( A : List[str]):
'''simple docstring'''
UpperCamelCase : List[Any] = botoa.client("iam")
UpperCamelCase : Optional[int] = {
"Version": "2012-10-17",
"Statement": [
{"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=A , AssumeRolePolicyDocument=json.dumps(A , indent=2))
UpperCamelCase : Dict = {
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"sagemaker:*",
"ecr:GetDownloadUrlForLayer",
"ecr:BatchGetImage",
"ecr:BatchCheckLayerAvailability",
"ecr:GetAuthorizationToken",
"cloudwatch:PutMetricData",
"cloudwatch:GetMetricData",
"cloudwatch:GetMetricStatistics",
"cloudwatch:ListMetrics",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:DescribeLogStreams",
"logs:PutLogEvents",
"logs:GetLogEvents",
"s3:CreateBucket",
"s3:ListBucket",
"s3:GetBucketLocation",
"s3:GetObject",
"s3:PutObject",
],
"Resource": "*",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=A , PolicyName=F'''{role_name}_policy_permission''' , PolicyDocument=json.dumps(A , indent=2) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F'''role {role_name} already exists. Using existing one''')
def A__ ( A : Tuple):
'''simple docstring'''
UpperCamelCase : Dict = botoa.client("iam")
return iam_client.get_role(RoleName=A)["Role"]["Arn"]
def A__ ( ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = _ask_options(
"How do you want to authorize?" , ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "] , A , )
UpperCamelCase : List[str] = None
if credentials_configuration == 0:
UpperCamelCase : int = _ask_field("Enter your AWS Profile name: [default] " , default="default")
UpperCamelCase : int = aws_profile
else:
print(
"Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
"`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`")
UpperCamelCase : Any = _ask_field("AWS Access Key ID: ")
UpperCamelCase : Any = aws_access_key_id
UpperCamelCase : Dict = _ask_field("AWS Secret Access Key: ")
UpperCamelCase : int = aws_secret_access_key
UpperCamelCase : int = _ask_field("Enter your AWS Region: [us-east-1]" , default="us-east-1")
UpperCamelCase : Optional[Any] = aws_region
UpperCamelCase : Optional[Any] = _ask_options(
"Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?" , ["Provide IAM Role name", "Create new IAM role using credentials"] , A , )
if role_management == 0:
UpperCamelCase : Tuple = _ask_field("Enter your IAM role name: ")
else:
UpperCamelCase : Optional[int] = "accelerate_sagemaker_execution_role"
print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''')
_create_iam_role_for_sagemaker(A)
UpperCamelCase : Union[str, Any] = _ask_field(
"Do you want to use custom Docker image? [yes/NO]: " , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
UpperCamelCase : Union[str, Any] = None
if is_custom_docker_image:
UpperCamelCase : Union[str, Any] = _ask_field("Enter your Docker image: " , lambda A: str(A).lower())
UpperCamelCase : Optional[int] = _ask_field(
"Do you want to provide SageMaker input channels with data locations? [yes/NO]: " , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
UpperCamelCase : List[Any] = None
if is_sagemaker_inputs_enabled:
UpperCamelCase : Union[str, Any] = _ask_field(
"Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): " , lambda A: str(A).lower() , )
UpperCamelCase : str = _ask_field(
"Do you want to enable SageMaker metrics? [yes/NO]: " , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
UpperCamelCase : Optional[Any] = None
if is_sagemaker_metrics_enabled:
UpperCamelCase : Any = _ask_field(
"Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): " , lambda A: str(A).lower() , )
UpperCamelCase : int = _ask_options(
"What is the distributed mode?" , ["No distributed training", "Data parallelism"] , _convert_sagemaker_distributed_mode , )
UpperCamelCase : int = {}
UpperCamelCase : List[Any] = _ask_field(
"Do you wish to optimize your script with torch dynamo?[yes/NO]:" , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
if use_dynamo:
UpperCamelCase : Optional[Any] = "dynamo_"
UpperCamelCase : Tuple = _ask_options(
"Which dynamo backend would you like to use?" , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
UpperCamelCase : Optional[Any] = _ask_field(
"Do you want to customize the defaults sent to torch.compile? [yes/NO]: " , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
if use_custom_options:
UpperCamelCase : Any = _ask_options(
"Which mode do you want to use?" , A , lambda A: TORCH_DYNAMO_MODES[int(A)] , default="default" , )
UpperCamelCase : Dict = _ask_field(
"Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: " , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
UpperCamelCase : List[str] = _ask_field(
"Do you want to enable dynamic shape tracing? [yes/NO]: " , _convert_yes_no_to_bool , default=A , error_message="Please enter yes or no." , )
UpperCamelCase : List[str] = "Which EC2 instance type you want to use for your training?"
if distributed_type != SageMakerDistributedType.NO:
UpperCamelCase : Union[str, Any] = _ask_options(
A , A , lambda A: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(A)])
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
UpperCamelCase : Tuple = _ask_field(A , lambda A: str(A).lower() , default="ml.p3.2xlarge")
UpperCamelCase : Optional[Any] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
UpperCamelCase : Any = _ask_field(
"How many machines do you want use? [1]: " , A , default=1 , )
UpperCamelCase : List[str] = _ask_options(
"Do you wish to use FP16 or BF16 (mixed precision)?" , ["no", "fp16", "bf16", "fp8"] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
"Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.")
return SageMakerConfig(
image_uri=A , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=A , use_cpu=A , dynamo_config=A , eca_instance_type=A , profile=A , region=A , iam_role_name=A , mixed_precision=A , num_machines=A , sagemaker_inputs_file=A , sagemaker_metrics_file=A , )
| 435 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__magic_name__ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Union[str, Any] = '''ernie_m'''
lowercase_ : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , a_ = 250002 , a_ = 768 , a_ = 12 , a_ = 12 , a_ = 3072 , a_ = "gelu" , a_ = 0.1 , a_ = 0.1 , a_ = 514 , a_ = 0.02 , a_ = 1 , a_ = 1e-05 , a_=None , a_=False , a_=0.0 , **a_ , ) -> Optional[int]:
super().__init__(pad_token_id=a_ , **a_ )
_UpperCAmelCase = vocab_size
_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 = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = classifier_dropout
_UpperCAmelCase = is_decoder
_UpperCAmelCase = act_dropout
| 657 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Union[str, Any] = '''convbert'''
def __init__( self , a_=30522 , a_=768 , a_=12 , a_=12 , a_=3072 , a_="gelu" , a_=0.1 , a_=0.1 , a_=512 , a_=2 , a_=0.02 , a_=1e-12 , a_=1 , a_=0 , a_=2 , a_=768 , a_=2 , a_=9 , a_=1 , a_=None , **a_ , ) -> Tuple:
super().__init__(
pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ , )
_UpperCAmelCase = vocab_size
_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 = layer_norm_eps
_UpperCAmelCase = embedding_size
_UpperCAmelCase = head_ratio
_UpperCAmelCase = conv_kernel_size
_UpperCAmelCase = num_groups
_UpperCAmelCase = classifier_dropout
class _lowerCAmelCase ( lowerCamelCase ):
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 657 | 1 |
"""simple docstring"""
from typing import Any
class snake_case :
def __init__( self : Optional[int] , a__ : Any ) -> str:
'''simple docstring'''
_A = data
_A = None
class snake_case :
def __init__( self : Optional[int] ) -> str:
'''simple docstring'''
_A = None
def a_ ( self : List[Any] ) -> Any:
'''simple docstring'''
_A = self.head
while temp is not None:
print(temp.data , end=" " )
_A = temp.next
print()
def a_ ( self : Optional[Any] , a__ : Any ) -> Dict:
'''simple docstring'''
_A = Node(a__ )
_A = self.head
_A = new_node
def a_ ( self : List[str] , a__ : Optional[Any] , a__ : List[Any] ) -> Tuple:
'''simple docstring'''
if node_data_a == node_data_a:
return
else:
_A = self.head
while node_a is not None and node_a.data != node_data_a:
_A = node_a.next
_A = self.head
while node_a is not None and node_a.data != node_data_a:
_A = node_a.next
if node_a is None or node_a is None:
return
_A , _A = node_a.data, node_a.data
if __name__ == "__main__":
a_ = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list() | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
parser.add_argument("--model_name", default="roberta-large", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
a_ = parser.parse_args()
if args.model_type == "roberta":
a_ = RobertaForMaskedLM.from_pretrained(args.model_name)
a_ = "roberta"
elif args.model_type == "gpt2":
a_ = GPTaLMHeadModel.from_pretrained(args.model_name)
a_ = "transformer"
a_ = model.state_dict()
a_ = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
a_ = state_dict[f'''{prefix}.{param_name}''']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
a_ = f'''{prefix}.embeddings.{w}.weight'''
a_ = state_dict[param_name]
for w in ["weight", "bias"]:
a_ = f'''{prefix}.embeddings.LayerNorm.{w}'''
a_ = state_dict[param_name]
# Transformer Blocks #
a_ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
a_ = state_dict[
f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
]
a_ = state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
a_ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
a_ = state_dict[f'''{layer}''']
if args.vocab_transform:
for w in ["weight", "bias"]:
a_ = state_dict[f'''lm_head.dense.{w}''']
a_ = state_dict[f'''lm_head.layer_norm.{w}''']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
a_ = state_dict[f'''{prefix}.ln_f.{w}''']
a_ = state_dict["lm_head.weight"]
print(f'''N layers selected for distillation: {std_idx}''')
print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint) | 621 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _snake_case ( unittest.TestCase ):
_lowercase : Optional[int] = inspect.getfile(accelerate.test_utils )
_lowercase : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
_lowercase : int = ['''accelerate''', '''launch''']
_lowercase : int = Path.home() / '''.cache/huggingface/accelerate'''
_lowercase : int = '''default_config.yaml'''
_lowercase : List[Any] = config_folder / config_file
_lowercase : str = config_folder / '''_default_config.yaml'''
_lowercase : str = Path('''tests/test_configs''' )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls) -> List[str]:
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path)
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls) -> str:
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path)
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy())
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
for config in sorted(self.test_config_path.glob('**/*.yaml')):
with self.subTest(config_file=a):
execute_subprocess_async(
self.base_cmd + ['--config_file', str(a), self.test_file_path] , env=os.environ.copy())
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
execute_subprocess_async(['accelerate', 'test'] , env=os.environ.copy())
class _snake_case ( unittest.TestCase ):
_lowercase : Optional[Any] = '''test-tpu'''
_lowercase : Optional[Any] = '''us-central1-a'''
_lowercase : List[str] = '''ls'''
_lowercase : Union[str, Any] = ['''accelerate''', '''tpu-config''']
_lowercase : Tuple = '''cd /usr/share'''
_lowercase : Optional[Any] = '''tests/test_samples/test_command_file.sh'''
_lowercase : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def SCREAMING_SNAKE_CASE__ ( self) -> int:
SCREAMING_SNAKE_CASE = run_command(
self.cmd
+ ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug'] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/0_12_0.yaml',
'--command',
self.command,
'--tpu_zone',
self.tpu_zone,
'--tpu_name',
self.tpu_name,
'--debug',
] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
SCREAMING_SNAKE_CASE = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug'] , return_stdout=a)
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug'] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/latest.yaml',
'--command',
self.command,
'--command',
'echo "Hello World"',
'--debug',
] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
SCREAMING_SNAKE_CASE = run_command(
self.cmd
+ ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug'] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/0_12_0.yaml',
'--command_file',
self.command_file,
'--tpu_zone',
self.tpu_zone,
'--tpu_name',
self.tpu_name,
'--debug',
] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug'] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , a , )
def SCREAMING_SNAKE_CASE__ ( self) -> str:
SCREAMING_SNAKE_CASE = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/latest.yaml',
'--install_accelerate',
'--accelerate_version',
'12.0.0',
'--debug',
] , return_stdout=a , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , a , )
| 73 |
"""simple docstring"""
import os
import sys
UpperCamelCase__ :Union[str, Any] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase__ :List[Any] = [
"""torch""",
"""numpy""",
"""tokenizers""",
"""filelock""",
"""requests""",
"""tqdm""",
"""regex""",
"""sentencepiece""",
"""sacremoses""",
"""importlib_metadata""",
"""huggingface_hub""",
]
@add_start_docstrings(AutoConfig.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> int:
return AutoConfig.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> int:
return AutoTokenizer.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModel.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModel.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModelForCausalLM.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModelForMaskedLM.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Dict:
return AutoModelForSequenceClassification.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def A_ ( *snake_case__ , **snake_case__ ) -> Union[str, Any]:
return AutoModelForQuestionAnswering.from_pretrained(*snake_case__ , **snake_case__ )
| 355 | 0 |
from __future__ import annotations
lowercase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
class __A:
def __init__( self : List[Any] , __UpperCamelCase : dict[str, list[str]] , __UpperCamelCase : str ):
lowerCamelCase_ = graph
# mapping node to its parent in resulting breadth first tree
lowerCamelCase_ = {}
lowerCamelCase_ = source_vertex
def lowercase__ ( self : Tuple ):
lowerCamelCase_ = {self.source_vertex}
lowerCamelCase_ = None
lowerCamelCase_ = [self.source_vertex] # first in first out queue
while queue:
lowerCamelCase_ = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(_snake_case )
lowerCamelCase_ = vertex
queue.append(_snake_case )
def lowercase__ ( self : str , __UpperCamelCase : str ):
if target_vertex == self.source_vertex:
return self.source_vertex
lowerCamelCase_ = self.parent.get(_snake_case )
if target_vertex_parent is None:
lowerCamelCase_ = (
F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(_snake_case )
return self.shortest_path(_snake_case ) + F'''->{target_vertex}'''
if __name__ == "__main__":
lowercase = Graph(graph, '''G''')
g.breath_first_search()
print(g.shortest_path('''D'''))
print(g.shortest_path('''G'''))
print(g.shortest_path('''Foo'''))
| 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 103 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = df.shape[:1][0]
# If you're using some other dataset input the target column
A = df.iloc[:, 1:2]
A = actual_data.values.reshape(len_data, 1)
A = MinMaxScaler().fit_transform(actual_data)
A = 10
A = 5
A = 20
A = len_data - periods * look_back
A = actual_data[:division]
A = actual_data[division - look_back :]
A , A = [], []
A , A = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A = np.array(train_x)
A = np.array(test_x)
A = np.array([list(i.ravel()) for i in train_y])
A = np.array([list(i.ravel()) for i in test_y])
A = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
A = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
A = model.predict(x_test)
| 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
lowercase_ = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
lowercase_ = {
"gpt-neox-20b": 2_048,
}
class __A ( UpperCamelCase__ ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Dict = ['input_ids', 'attention_mask']
def __init__(self , A=None , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , **A , ) -> Tuple:
"""simple docstring"""
super().__init__(
__A , __A , tokenizer_file=__A , unk_token=__A , bos_token=__A , eos_token=__A , add_prefix_space=__A , **__A , )
_a = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __A ) != add_prefix_space:
_a = getattr(__A , pre_tok_state.pop('''type''' ) )
_a = add_prefix_space
_a = pre_tok_class(**__A )
_a = add_prefix_space
def a__ (self , A , A = None ) -> Tuple[str]:
"""simple docstring"""
_a = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
def a__ (self , A ) -> List[int]:
"""simple docstring"""
_a = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__A , add_special_tokens=__A ) + [self.eos_token_id] )
if len(__A ) > self.model_max_length:
_a = input_ids[-self.model_max_length :]
return input_ids
| 715 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
_a = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_a = set()
return any(
node not in visited and depth_first_search(__A , __A , __A , __A)
for node in graph)
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
visited.add(__A)
rec_stk.add(__A)
for node in graph[vertex]:
if node not in visited:
if depth_first_search(__A , __A , __A , __A):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(__A)
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 352 | 0 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(_SCREAMING_SNAKE_CASE )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , _SCREAMING_SNAKE_CASE ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
__A : Dict = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
__A : List[Any] = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 602 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__A : Any = True
except (ImportError, AttributeError):
__A : str = object
def lowercase ( *_SCREAMING_SNAKE_CASE : int , **_SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
pass
__A : Any = False
__A : Optional[int] = logging.get_logger("transformers-cli/serving")
def lowercase ( _SCREAMING_SNAKE_CASE : Namespace ):
'''simple docstring'''
_UpperCAmelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(_SCREAMING_SNAKE_CASE , args.host , args.port , args.workers )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : ArgumentParser )->List[str]:
_UpperCAmelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=__UpperCamelCase , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=__UpperCamelCase , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=__UpperCamelCase , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=__UpperCamelCase , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=__UpperCamelCase , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=__UpperCamelCase , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=__UpperCamelCase , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=__UpperCamelCase , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=__UpperCamelCase )
def __init__( self : int , __UpperCamelCase : Pipeline , __UpperCamelCase : str , __UpperCamelCase : int , __UpperCamelCase : int )->Any:
_UpperCAmelCase = pipeline
_UpperCAmelCase = host
_UpperCAmelCase = port
_UpperCAmelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F'Serving model over {host}:{port}' )
_UpperCAmelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def lowercase__ ( self : Optional[int] )->Union[str, Any]:
run(self._app , host=self.host , port=self.port , workers=self.workers )
def lowercase__ ( self : int )->int:
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def lowercase__ ( self : List[Any] , __UpperCamelCase : str = Body(__UpperCamelCase , embed=__UpperCamelCase ) , __UpperCamelCase : bool = Body(__UpperCamelCase , embed=__UpperCamelCase ) )->Any:
try:
_UpperCAmelCase = self._pipeline.tokenizer.tokenize(__UpperCamelCase )
if return_ids:
_UpperCAmelCase = self._pipeline.tokenizer.convert_tokens_to_ids(__UpperCamelCase )
return ServeTokenizeResult(tokens=__UpperCamelCase , tokens_ids=__UpperCamelCase )
else:
return ServeTokenizeResult(tokens=__UpperCamelCase )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(__UpperCamelCase )} )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : List[int] = Body(__UpperCamelCase , embed=__UpperCamelCase ) , __UpperCamelCase : bool = Body(__UpperCamelCase , embed=__UpperCamelCase ) , __UpperCamelCase : bool = Body(__UpperCamelCase , embed=__UpperCamelCase ) , )->List[str]:
try:
_UpperCAmelCase = self._pipeline.tokenizer.decode(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return ServeDeTokenizeResult(model='''''' , text=__UpperCamelCase )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(__UpperCamelCase )} )
async def lowercase__ ( self : int , __UpperCamelCase : List[Any]=Body(__UpperCamelCase , embed=__UpperCamelCase ) )->Tuple:
# Check we don't have empty string
if len(__UpperCamelCase ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_UpperCAmelCase = self._pipeline(__UpperCamelCase )
return ServeForwardResult(output=__UpperCamelCase )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(__UpperCamelCase )} )
| 602 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {"vocab_file": "spiece.model"}
SCREAMING_SNAKE_CASE = {
"vocab_file": {
"bert_for_seq_generation": (
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
),
}
}
SCREAMING_SNAKE_CASE = {"bert_for_seq_generation": 512}
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = VOCAB_FILES_NAMES
lowerCAmelCase_ : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ : List[int] = []
lowerCAmelCase_ : List[str] = ['input_ids', 'attention_mask']
def __init__( self , lowerCAmelCase , lowerCAmelCase="<s>" , lowerCAmelCase="</s>" , lowerCAmelCase="<unk>" , lowerCAmelCase="<pad>" , lowerCAmelCase="<::::>" , lowerCAmelCase = None , **lowerCAmelCase , ):
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=lowerCAmelCase , eos_token=lowerCAmelCase , unk_token=lowerCAmelCase , pad_token=lowerCAmelCase , sep_token=lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase , )
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase )
@property
def A__ ( self ):
return self.sp_model.get_piece_size()
def A__ ( self ):
UpperCAmelCase_ = {self.convert_ids_to_tokens(lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self , lowerCAmelCase ):
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A__ ( self , lowerCAmelCase ):
return self.sp_model.encode(lowerCAmelCase , out_type=lowerCAmelCase )
def A__ ( self , lowerCAmelCase ):
return self.sp_model.piece_to_id(lowerCAmelCase )
def A__ ( self , lowerCAmelCase ):
UpperCAmelCase_ = self.sp_model.IdToPiece(lowerCAmelCase )
return token
def A__ ( self , lowerCAmelCase ):
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowerCAmelCase ) + token
UpperCAmelCase_ = []
else:
current_sub_tokens.append(lowerCAmelCase )
out_string += self.sp_model.decode(lowerCAmelCase )
return out_string.strip()
def A__ ( self , lowerCAmelCase , lowerCAmelCase = 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"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase )
return (out_vocab_file,)
| 713 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : torch.FloatTensor
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , lowerCAmelCase = 32 , lowerCAmelCase = 64 , lowerCAmelCase = 20 , lowerCAmelCase = 768 , lowerCAmelCase=77 , lowerCAmelCase=4 , lowerCAmelCase = 0.0 , lowerCAmelCase = "silu" , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = "linear" , lowerCAmelCase = "prd" , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , ):
super().__init__()
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = attention_head_dim
UpperCAmelCase_ = num_attention_heads * attention_head_dim
UpperCAmelCase_ = additional_embeddings
UpperCAmelCase_ = time_embed_dim or inner_dim
UpperCAmelCase_ = embedding_proj_dim or embedding_dim
UpperCAmelCase_ = clip_embed_dim or embedding_dim
UpperCAmelCase_ = Timesteps(lowerCAmelCase , lowerCAmelCase , 0 )
UpperCAmelCase_ = TimestepEmbedding(lowerCAmelCase , lowerCAmelCase , out_dim=lowerCAmelCase , act_fn=lowerCAmelCase )
UpperCAmelCase_ = nn.Linear(lowerCAmelCase , lowerCAmelCase )
if embedding_proj_norm_type is None:
UpperCAmelCase_ = None
elif embedding_proj_norm_type == "layer":
UpperCAmelCase_ = nn.LayerNorm(lowerCAmelCase )
else:
raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )
UpperCAmelCase_ = nn.Linear(lowerCAmelCase , lowerCAmelCase )
if encoder_hid_proj_type is None:
UpperCAmelCase_ = None
elif encoder_hid_proj_type == "linear":
UpperCAmelCase_ = nn.Linear(lowerCAmelCase , lowerCAmelCase )
else:
raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )
UpperCAmelCase_ = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCAmelCase ) )
if added_emb_type == "prd":
UpperCAmelCase_ = nn.Parameter(torch.zeros(1 , 1 , lowerCAmelCase ) )
elif added_emb_type is None:
UpperCAmelCase_ = None
else:
raise ValueError(
f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )
UpperCAmelCase_ = nn.ModuleList(
[
BasicTransformerBlock(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , dropout=lowerCAmelCase , activation_fn="gelu" , attention_bias=lowerCAmelCase , )
for d in range(lowerCAmelCase )
] )
if norm_in_type == "layer":
UpperCAmelCase_ = nn.LayerNorm(lowerCAmelCase )
elif norm_in_type is None:
UpperCAmelCase_ = None
else:
raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )
UpperCAmelCase_ = nn.LayerNorm(lowerCAmelCase )
UpperCAmelCase_ = nn.Linear(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase_ = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -10000.0 )
causal_attention_mask.triu_(1 )
UpperCAmelCase_ = causal_attention_mask[None, ...]
self.register_buffer("causal_attention_mask" , lowerCAmelCase , persistent=lowerCAmelCase )
UpperCAmelCase_ = nn.Parameter(torch.zeros(1 , lowerCAmelCase ) )
UpperCAmelCase_ = nn.Parameter(torch.zeros(1 , lowerCAmelCase ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def A__ ( self ):
UpperCAmelCase_ = {}
def fn_recursive_add_processors(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
if hasattr(lowerCAmelCase , "set_processor" ):
UpperCAmelCase_ = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''' , lowerCAmelCase , lowerCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return processors
def A__ ( self , lowerCAmelCase ):
UpperCAmelCase_ = len(self.attn_processors.keys() )
if isinstance(lowerCAmelCase , lowerCAmelCase ) and len(lowerCAmelCase ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(lowerCAmelCase )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
if hasattr(lowerCAmelCase , "set_processor" ):
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
module.set_processor(lowerCAmelCase )
else:
module.set_processor(processor.pop(f'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'''{name}.{sub_name}''' , lowerCAmelCase , lowerCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def A__ ( self ):
self.set_attn_processor(AttnProcessor() )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = True , ):
UpperCAmelCase_ = hidden_states.shape[0]
UpperCAmelCase_ = timestep
if not torch.is_tensor(lowerCAmelCase ):
UpperCAmelCase_ = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCAmelCase ) and len(timesteps.shape ) == 0:
UpperCAmelCase_ = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCAmelCase_ = timesteps * torch.ones(lowerCAmelCase , dtype=timesteps.dtype , device=timesteps.device )
UpperCAmelCase_ = self.time_proj(lowerCAmelCase )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
UpperCAmelCase_ = timesteps_projected.to(dtype=self.dtype )
UpperCAmelCase_ = self.time_embedding(lowerCAmelCase )
if self.embedding_proj_norm is not None:
UpperCAmelCase_ = self.embedding_proj_norm(lowerCAmelCase )
UpperCAmelCase_ = self.embedding_proj(lowerCAmelCase )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
UpperCAmelCase_ = self.encoder_hidden_states_proj(lowerCAmelCase )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set" )
UpperCAmelCase_ = self.proj_in(lowerCAmelCase )
UpperCAmelCase_ = self.positional_embedding.to(hidden_states.dtype )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCAmelCase )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
UpperCAmelCase_ = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
UpperCAmelCase_ = hidden_states[:, None, :]
UpperCAmelCase_ = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
UpperCAmelCase_ = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCAmelCase , -1 , -1 )
additional_embeds.append(lowerCAmelCase )
UpperCAmelCase_ = torch.cat(
lowerCAmelCase , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
UpperCAmelCase_ = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
UpperCAmelCase_ = F.pad(
lowerCAmelCase , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
UpperCAmelCase_ = hidden_states + positional_embeddings
if attention_mask is not None:
UpperCAmelCase_ = (1 - attention_mask.to(hidden_states.dtype )) * -10000.0
UpperCAmelCase_ = F.pad(lowerCAmelCase , (0, self.additional_embeddings) , value=0.0 )
UpperCAmelCase_ = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
UpperCAmelCase_ = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
UpperCAmelCase_ = self.norm_in(lowerCAmelCase )
for block in self.transformer_blocks:
UpperCAmelCase_ = block(lowerCAmelCase , attention_mask=lowerCAmelCase )
UpperCAmelCase_ = self.norm_out(lowerCAmelCase )
if self.prd_embedding is not None:
UpperCAmelCase_ = hidden_states[:, -1]
else:
UpperCAmelCase_ = hidden_states[:, additional_embeddings_len:]
UpperCAmelCase_ = self.proj_to_clip_embeddings(lowerCAmelCase )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCAmelCase )
def A__ ( self , lowerCAmelCase ):
UpperCAmelCase_ = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 23 | 0 |
import math
import tensorflow as tf
from packaging import version
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] ) -> Any:
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tf.cast(math.pi , x.dtype )
SCREAMING_SNAKE_CASE_ = tf.cast(0.0_4_4_7_1_5 , x.dtype )
SCREAMING_SNAKE_CASE_ = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tf.cast(0.0_4_4_7_1_5 , x.dtype )
SCREAMING_SNAKE_CASE_ = tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] ) -> int:
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -10 , 10 )
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : List[str]=-1 ) -> List[Any]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('2.4'):
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] ) -> Dict:
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
lowerCamelCase__ : List[Any] = tf.keras.activations.gelu
lowerCamelCase__ : Tuple = approximate_gelu_wrap
else:
lowerCamelCase__ : Optional[Any] = _gelu
lowerCamelCase__ : Optional[Any] = _gelu_new
lowerCamelCase__ : Optional[int] = {
'gelu': gelu,
'gelu_10': gelu_aa,
'gelu_fast': gelu_fast,
'gelu_new': gelu_new,
'glu': glu,
'mish': mish,
'quick_gelu': quick_gelu,
'relu': tf.keras.activations.relu,
'sigmoid': tf.keras.activations.sigmoid,
'silu': tf.keras.activations.swish,
'swish': tf.keras.activations.swish,
'tanh': tf.keras.activations.tanh,
}
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] ) -> Tuple:
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" ) | 31 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False ) -> list[float]:
if radian_mode:
return [magnitude * cos(_UpperCAmelCase ), magnitude * sin(_UpperCAmelCase )]
return [magnitude * cos(radians(_UpperCAmelCase ) ), magnitude * sin(radians(_UpperCAmelCase ) )]
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 10**-1 ) -> bool:
lowerCamelCase__ : NDArray[floataa] = cross(_UpperCAmelCase , _UpperCAmelCase )
lowerCamelCase__ : float = sum(_UpperCAmelCase )
return abs(_UpperCAmelCase ) < eps
if __name__ == "__main__":
# Test to check if it works
_UpperCAmelCase : Any = array(
[
polar_force(718.4, 1_80 - 30),
polar_force(879.54, 45),
polar_force(1_00, -90),
]
)
_UpperCAmelCase : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
_UpperCAmelCase : List[Any] = array(
[
polar_force(30 * 9.81, 15),
polar_force(2_15, 1_80 - 45),
polar_force(2_64, 90 - 30),
]
)
_UpperCAmelCase : Any = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
_UpperCAmelCase : Optional[Any] = array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])
_UpperCAmelCase : Optional[Any] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 188 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_UpperCAmelCase : int = Mapping[str, np.ndarray]
_UpperCAmelCase : List[Any] = Mapping[str, Any] # Is a nested dict.
_UpperCAmelCase : Dict = 0.01
@dataclasses.dataclass(frozen=__UpperCamelCase )
class lowerCAmelCase :
UpperCAmelCase__ = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
UpperCAmelCase__ = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
UpperCAmelCase__ = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
UpperCAmelCase__ = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
UpperCAmelCase__ = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
UpperCAmelCase__ = None
# Optional remark about the protein. Included as a comment in output PDB
# files
UpperCAmelCase__ = None
# Templates used to generate this protein (prediction-only)
UpperCAmelCase__ = None
# Chain corresponding to each parent
UpperCAmelCase__ = None
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Protein:
lowerCamelCase__ : Optional[int] = r'(\[[A-Z]+\]\n)'
lowerCamelCase__ : List[str] = [tag.strip() for tag in re.split(_UpperCAmelCase , _UpperCAmelCase ) if len(_UpperCAmelCase ) > 0]
lowerCamelCase__ : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split('\n' ) for l in tags[1::2]] )
lowerCamelCase__ : List[str] = ["N", "CA", "C"]
lowerCamelCase__ : Dict = None
lowerCamelCase__ : str = None
lowerCamelCase__ : int = None
for g in groups:
if "[PRIMARY]" == g[0]:
lowerCamelCase__ : int = g[1][0].strip()
for i in range(len(_UpperCAmelCase ) ):
if seq[i] not in residue_constants.restypes:
lowerCamelCase__ : Union[str, Any] = 'X' # FIXME: strings are immutable
lowerCamelCase__ : Union[str, Any] = np.array(
[residue_constants.restype_order.get(_UpperCAmelCase , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
lowerCamelCase__ : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_UpperCAmelCase , g[1][axis].split() ) ) )
lowerCamelCase__ : int = np.array(_UpperCAmelCase )
lowerCamelCase__ : Tuple = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_UpperCAmelCase ):
lowerCamelCase__ : int = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
lowerCamelCase__ : int = np.array(list(map({'-': 0, '+': 1}.get , g[1][0].strip() ) ) )
lowerCamelCase__ : List[Any] = np.zeros(
(
len(_UpperCAmelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_UpperCAmelCase ):
lowerCamelCase__ : Union[str, Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_UpperCAmelCase , atom_mask=_UpperCAmelCase , aatype=_UpperCAmelCase , residue_index=np.arange(len(_UpperCAmelCase ) ) , b_factors=_UpperCAmelCase , )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase = 0 ) -> List[str]:
lowerCamelCase__ : List[str] = []
lowerCamelCase__ : Dict = prot.remark
if remark is not None:
pdb_headers.append(F"""REMARK {remark}""" )
lowerCamelCase__ : str = prot.parents
lowerCamelCase__ : Union[str, Any] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
lowerCamelCase__ : Any = [p for i, p in zip(_UpperCAmelCase , _UpperCAmelCase ) if i == chain_id]
if parents is None or len(_UpperCAmelCase ) == 0:
lowerCamelCase__ : List[Any] = ['N/A']
pdb_headers.append(F"""PARENT {" ".join(_UpperCAmelCase )}""" )
return pdb_headers
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
lowerCamelCase__ : List[str] = []
lowerCamelCase__ : str = pdb_str.split('\n' )
lowerCamelCase__ : int = prot.remark
if remark is not None:
out_pdb_lines.append(F"""REMARK {remark}""" )
lowerCamelCase__ : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
lowerCamelCase__ : List[Any] = []
if prot.parents_chain_index is not None:
lowerCamelCase__ : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_UpperCAmelCase ) , [] )
parent_dict[str(_UpperCAmelCase )].append(_UpperCAmelCase )
lowerCamelCase__ : str = max([int(_UpperCAmelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
lowerCamelCase__ : Optional[Any] = parent_dict.get(str(_UpperCAmelCase ) , ['N/A'] )
parents_per_chain.append(_UpperCAmelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
lowerCamelCase__ : Union[str, Any] = [['N/A']]
def make_parent_line(_UpperCAmelCase ) -> str:
return F"""PARENT {" ".join(_UpperCAmelCase )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
lowerCamelCase__ : List[Any] = 0
for i, l in enumerate(_UpperCAmelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_UpperCAmelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_UpperCAmelCase ):
lowerCamelCase__ : Union[str, Any] = parents_per_chain[chain_counter]
else:
lowerCamelCase__ : Optional[Any] = ['N/A']
out_pdb_lines.append(make_parent_line(_UpperCAmelCase ) )
return "\n".join(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Tuple = residue_constants.restypes + ['X']
def res_atoa(_UpperCAmelCase ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , 'UNK' )
lowerCamelCase__ : int = residue_constants.atom_types
lowerCamelCase__ : List[str] = []
lowerCamelCase__ : Union[str, Any] = prot.atom_mask
lowerCamelCase__ : Union[str, Any] = prot.aatype
lowerCamelCase__ : int = prot.atom_positions
lowerCamelCase__ : List[Any] = prot.residue_index.astype(np.intaa )
lowerCamelCase__ : Optional[int] = prot.b_factors
lowerCamelCase__ : Any = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError('Invalid aatypes.' )
lowerCamelCase__ : List[Any] = get_pdb_headers(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
pdb_lines.extend(_UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = aatype.shape[0]
lowerCamelCase__ : Optional[Any] = 1
lowerCamelCase__ : str = 0
lowerCamelCase__ : Tuple = string.ascii_uppercase
lowerCamelCase__ : str = None
# Add all atom sites.
for i in range(_UpperCAmelCase ):
lowerCamelCase__ : List[Any] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_UpperCAmelCase , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
lowerCamelCase__ : Union[str, Any] = 'ATOM'
lowerCamelCase__ : Optional[int] = atom_name if len(_UpperCAmelCase ) == 4 else F""" {atom_name}"""
lowerCamelCase__ : Any = ''
lowerCamelCase__ : Optional[Any] = ''
lowerCamelCase__ : str = 1.00
lowerCamelCase__ : str = atom_name[0] # Protein supports only C, N, O, S, this works.
lowerCamelCase__ : List[str] = ''
lowerCamelCase__ : str = 'A'
if chain_index is not None:
lowerCamelCase__ : List[Any] = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
lowerCamelCase__ : Union[str, Any] = (
F"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
F"""{res_name_a:>3} {chain_tag:>1}"""
F"""{residue_index[i]:>4}{insertion_code:>1} """
F"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
F"""{occupancy:>6.2f}{b_factor:>6.2f} """
F"""{element:>2}{charge:>2}"""
)
pdb_lines.append(_UpperCAmelCase )
atom_index += 1
lowerCamelCase__ : Dict = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
lowerCamelCase__ : List[Any] = True
lowerCamelCase__ : List[str] = chain_index[i + 1]
if should_terminate:
# Close the chain.
lowerCamelCase__ : int = 'TER'
lowerCamelCase__ : Any = (
F"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(_UpperCAmelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_UpperCAmelCase , _UpperCAmelCase ) )
pdb_lines.append('END' )
pdb_lines.append('' )
return "\n".join(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> np.ndarray:
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , ) -> Protein:
return Protein(
aatype=features['aatype'] , atom_positions=result['final_atom_positions'] , atom_mask=result['final_atom_mask'] , residue_index=features['residue_index'] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask'] ) , chain_index=_UpperCAmelCase , remark=_UpperCAmelCase , parents=_UpperCAmelCase , parents_chain_index=_UpperCAmelCase , )
| 188 | 1 |
# Imports
import numpy as np
class _UpperCamelCase :
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None )-> List[Any]:
self.set_matricies(red=__UpperCamelCase , green=__UpperCamelCase , blue=__UpperCamelCase , red_edge=__UpperCamelCase , nir=__UpperCamelCase )
def __UpperCAmelCase ( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None )-> Any:
if red is not None:
__lowerCAmelCase = red
if green is not None:
__lowerCAmelCase = green
if blue is not None:
__lowerCAmelCase = blue
if red_edge is not None:
__lowerCAmelCase = red_edge
if nir is not None:
__lowerCAmelCase = nir
return True
def __UpperCAmelCase ( self , __UpperCamelCase="" , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None )-> Union[str, Any]:
self.set_matricies(red=__UpperCamelCase , green=__UpperCamelCase , blue=__UpperCamelCase , red_edge=__UpperCamelCase , nir=__UpperCamelCase )
__lowerCAmelCase = {
"ARVI2": self.arvaa,
"CCCI": self.ccci,
"CVI": self.cvi,
"GLI": self.gli,
"NDVI": self.ndvi,
"BNDVI": self.bndvi,
"redEdgeNDVI": self.red_edge_ndvi,
"GNDVI": self.gndvi,
"GBNDVI": self.gbndvi,
"GRNDVI": self.grndvi,
"RBNDVI": self.rbndvi,
"PNDVI": self.pndvi,
"ATSAVI": self.atsavi,
"BWDRVI": self.bwdrvi,
"CIgreen": self.ci_green,
"CIrededge": self.ci_rededge,
"CI": self.ci,
"CTVI": self.ctvi,
"GDVI": self.gdvi,
"EVI": self.evi,
"GEMI": self.gemi,
"GOSAVI": self.gosavi,
"GSAVI": self.gsavi,
"Hue": self.hue,
"IVI": self.ivi,
"IPVI": self.ipvi,
"I": self.i,
"RVI": self.rvi,
"MRVI": self.mrvi,
"MSAVI": self.m_savi,
"NormG": self.norm_g,
"NormNIR": self.norm_nir,
"NormR": self.norm_r,
"NGRDI": self.ngrdi,
"RI": self.ri,
"S": self.s,
"IF": self._if,
"DVI": self.dvi,
"TVI": self.tvi,
"NDRE": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("Index not in the list!" )
return False
def __UpperCAmelCase ( self )-> Any:
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def __UpperCAmelCase ( self )-> int:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def __UpperCAmelCase ( self )-> Optional[int]:
return self.nir * (self.red / (self.green**2))
def __UpperCAmelCase ( self )-> Any:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def __UpperCAmelCase ( self )-> Optional[Any]:
return (self.nir - self.red) / (self.nir + self.red)
def __UpperCAmelCase ( self )-> Optional[int]:
return (self.nir - self.blue) / (self.nir + self.blue)
def __UpperCAmelCase ( self )-> int:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def __UpperCAmelCase ( self )-> Dict:
return (self.nir - self.green) / (self.nir + self.green)
def __UpperCAmelCase ( self )-> str:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def __UpperCAmelCase ( self )-> Optional[int]:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def __UpperCAmelCase ( self )-> List[str]:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def __UpperCAmelCase ( self )-> List[Any]:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def __UpperCAmelCase ( self , __UpperCamelCase=0.0_8 , __UpperCamelCase=1.2_2 , __UpperCamelCase=0.0_3 )-> List[str]:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def __UpperCAmelCase ( self )-> Tuple:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def __UpperCAmelCase ( self )-> Optional[Any]:
return (self.nir / self.green) - 1
def __UpperCAmelCase ( self )-> Any:
return (self.nir / self.redEdge) - 1
def __UpperCAmelCase ( self )-> int:
return (self.red - self.blue) / self.red
def __UpperCAmelCase ( self )-> Optional[int]:
__lowerCAmelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def __UpperCAmelCase ( self )-> Tuple:
return self.nir - self.green
def __UpperCAmelCase ( self )-> Optional[int]:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def __UpperCAmelCase ( self )-> Tuple:
__lowerCAmelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def __UpperCAmelCase ( self , __UpperCamelCase=0.1_6 )-> Optional[int]:
return (self.nir - self.green) / (self.nir + self.green + y)
def __UpperCAmelCase ( self , __UpperCamelCase=0.5 )-> Optional[Any]:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def __UpperCAmelCase ( self )-> str:
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def __UpperCAmelCase ( self , __UpperCamelCase=None , __UpperCamelCase=None )-> Optional[int]:
return (self.nir - b) / (a * self.red)
def __UpperCAmelCase ( self )-> List[Any]:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def __UpperCAmelCase ( self )-> Any:
return (self.red + self.green + self.blue) / 3_0.5
def __UpperCAmelCase ( self )-> Any:
return self.nir / self.red
def __UpperCAmelCase ( self )-> int:
return (self.rvi() - 1) / (self.rvi() + 1)
def __UpperCAmelCase ( self )-> List[str]:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def __UpperCAmelCase ( self )-> str:
return self.green / (self.nir + self.red + self.green)
def __UpperCAmelCase ( self )-> Any:
return self.nir / (self.nir + self.red + self.green)
def __UpperCAmelCase ( self )-> int:
return self.red / (self.nir + self.red + self.green)
def __UpperCAmelCase ( self )-> Optional[int]:
return (self.green - self.red) / (self.green + self.red)
def __UpperCAmelCase ( self )-> Optional[int]:
return (self.red - self.green) / (self.red + self.green)
def __UpperCAmelCase ( self )-> Dict:
__lowerCAmelCase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
__lowerCAmelCase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def __UpperCAmelCase ( self )-> List[Any]:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def __UpperCAmelCase ( self )-> Optional[int]:
return self.nir / self.red
def __UpperCAmelCase ( self )-> Any:
return (self.ndvi() + 0.5) ** (1 / 2)
def __UpperCAmelCase ( self )-> Any:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 367 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Any = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
lowerCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def __lowerCAmelCase ( __snake_case ):
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
__lowerCAmelCase = model_type_to_module_name(__snake_case )
__lowerCAmelCase = importlib.import_module(F""".{module_name}""" , "transformers.models" )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__snake_case , "__name__" , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
__lowerCAmelCase = importlib.import_module("transformers" )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def __lowerCAmelCase ( __snake_case , __snake_case = None , __snake_case = False , __snake_case = False , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = False , **__snake_case , ):
__lowerCAmelCase = get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
"Could not locate the feature extractor configuration file, will try to use the model config instead." )
return {}
with open(__snake_case , encoding="utf-8" ) as reader:
return json.load(__snake_case )
class _UpperCamelCase :
def __init__( self )-> Union[str, Any]:
raise EnvironmentError(
"AutoFeatureExtractor is designed to be instantiated "
"using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(__UpperCamelCase )
def __UpperCAmelCase ( cls , __UpperCamelCase , **__UpperCamelCase )-> Optional[int]:
__lowerCAmelCase = kwargs.pop("config" , __UpperCamelCase )
__lowerCAmelCase = kwargs.pop("trust_remote_code" , __UpperCamelCase )
__lowerCAmelCase = True
__lowerCAmelCase , __lowerCAmelCase = FeatureExtractionMixin.get_feature_extractor_dict(__UpperCamelCase , **__UpperCamelCase )
__lowerCAmelCase = config_dict.get("feature_extractor_type" , __UpperCamelCase )
__lowerCAmelCase = None
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
__lowerCAmelCase = config_dict["auto_map"]["AutoFeatureExtractor"]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
__lowerCAmelCase = AutoConfig.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
# It could be in `config.feature_extractor_type``
__lowerCAmelCase = getattr(__UpperCamelCase , "feature_extractor_type" , __UpperCamelCase )
if hasattr(__UpperCamelCase , "auto_map" ) and "AutoFeatureExtractor" in config.auto_map:
__lowerCAmelCase = config.auto_map["AutoFeatureExtractor"]
if feature_extractor_class is not None:
__lowerCAmelCase = feature_extractor_class_from_name(__UpperCamelCase )
__lowerCAmelCase = feature_extractor_auto_map is not None
__lowerCAmelCase = feature_extractor_class is not None or type(__UpperCamelCase ) in FEATURE_EXTRACTOR_MAPPING
__lowerCAmelCase = resolve_trust_remote_code(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if has_remote_code and trust_remote_code:
__lowerCAmelCase = get_class_from_dynamic_module(
__UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
__lowerCAmelCase = kwargs.pop("code_revision" , __UpperCamelCase )
if os.path.isdir(__UpperCamelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__UpperCamelCase , **__UpperCamelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__UpperCamelCase , **__UpperCamelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__UpperCamelCase ) in FEATURE_EXTRACTOR_MAPPING:
__lowerCAmelCase = FEATURE_EXTRACTOR_MAPPING[type(__UpperCamelCase )]
return feature_extractor_class.from_dict(__UpperCamelCase , **__UpperCamelCase )
raise ValueError(
F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase , __UpperCamelCase )
| 367 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __UpperCamelCase (unittest.TestCase ):
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
lowercase = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
lowercase = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 1_6000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
lowercase = tempfile.mkdtemp()
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowercase = os.path.join(self.tmpdirname , _lowerCAmelCase )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
with open(self.feature_extraction_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
# load decoder from hub
lowercase = """hf-internal-testing/ngram-beam-search-decoder"""
def _a ( self , **_lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase = self.add_kwargs_tokens_map.copy()
kwargs.update(_lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self , **_lowerCAmelCase ) -> Any:
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self , **_lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **_lowerCAmelCase )
def _a ( self ) -> int:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.get_tokenizer()
lowercase = self.get_feature_extractor()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowercase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , _lowerCAmelCase )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
lowercase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def _a ( self ) -> Dict:
'''simple docstring'''
lowercase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(_lowerCAmelCase , """include""" ):
WavaVecaProcessorWithLM(
tokenizer=_lowerCAmelCase , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def _a ( self ) -> Tuple:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
lowercase = floats_list((3, 1000) )
lowercase = feature_extractor(_lowerCAmelCase , return_tensors="""np""" )
lowercase = processor(_lowerCAmelCase , 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 _a ( self ) -> int:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
lowercase = """This is a test string"""
lowercase = processor(text=_lowerCAmelCase )
lowercase = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self , _lowerCAmelCase=(2, 10, 16) , _lowerCAmelCase=77 ) -> List[Any]:
'''simple docstring'''
np.random.seed(_lowerCAmelCase )
return np.random.rand(*_lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
lowercase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
lowercase = processor.decode(_lowerCAmelCase )
lowercase = decoder.decode_beams(_lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def _a ( self , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
lowercase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
lowercase = processor.batch_decode(_lowerCAmelCase )
else:
with get_context(_lowerCAmelCase ).Pool() as pool:
lowercase = processor.batch_decode(_lowerCAmelCase , _lowerCAmelCase )
lowercase = list(_lowerCAmelCase )
with get_context("""fork""" ).Pool() as p:
lowercase = decoder.decode_beams_batch(_lowerCAmelCase , _lowerCAmelCase )
lowercase , lowercase , lowercase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(_lowerCAmelCase , decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] , decoded_processor.text )
self.assertListEqual(_lowerCAmelCase , decoded_processor.logit_score )
self.assertListEqual(_lowerCAmelCase , decoded_processor.lm_score )
def _a ( self ) -> int:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
lowercase = self._get_dummy_logits()
lowercase = 15
lowercase = -20.0
lowercase = -4.0
lowercase = processor.batch_decode(
_lowerCAmelCase , beam_width=_lowerCAmelCase , beam_prune_logp=_lowerCAmelCase , token_min_logp=_lowerCAmelCase , )
lowercase = decoded_processor_out.text
lowercase = list(_lowerCAmelCase )
with get_context("""fork""" ).Pool() as pool:
lowercase = decoder.decode_beams_batch(
_lowerCAmelCase , _lowerCAmelCase , beam_width=_lowerCAmelCase , beam_prune_logp=_lowerCAmelCase , token_min_logp=_lowerCAmelCase , )
lowercase = [d[0][0] for d in decoded_decoder_out]
lowercase = [d[0][2] for d in decoded_decoder_out]
lowercase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] , _lowerCAmelCase )
self.assertTrue(np.array_equal(_lowerCAmelCase , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , _lowerCAmelCase , atol=1E-3 ) )
self.assertTrue(np.array_equal(_lowerCAmelCase , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , _lowerCAmelCase , atol=1E-3 ) )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
lowercase = self._get_dummy_logits()
lowercase = 2.0
lowercase = 5.0
lowercase = -20.0
lowercase = True
lowercase = processor.batch_decode(
_lowerCAmelCase , alpha=_lowerCAmelCase , beta=_lowerCAmelCase , unk_score_offset=_lowerCAmelCase , lm_score_boundary=_lowerCAmelCase , )
lowercase = decoded_processor_out.text
lowercase = list(_lowerCAmelCase )
decoder.reset_params(
alpha=_lowerCAmelCase , beta=_lowerCAmelCase , unk_score_offset=_lowerCAmelCase , lm_score_boundary=_lowerCAmelCase , )
with get_context("""fork""" ).Pool() as pool:
lowercase = decoder.decode_beams_batch(
_lowerCAmelCase , _lowerCAmelCase , )
lowercase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] , _lowerCAmelCase )
lowercase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , _lowerCAmelCase )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
lowercase = processor.decoder.model_container[processor.decoder._model_key]
lowercase = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
lowercase = os.listdir(_lowerCAmelCase )
lowercase = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
lowercase = snapshot_download("""hf-internal-testing/processor_with_lm""" )
lowercase = WavaVecaProcessorWithLM.from_pretrained(_lowerCAmelCase )
lowercase = processor.decoder.model_container[processor.decoder._model_key]
lowercase = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
lowercase = os.listdir(_lowerCAmelCase )
lowercase = os.listdir(_lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self ) -> Any:
'''simple docstring'''
lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
lowercase = floats_list((3, 1000) )
lowercase = processor_wavaveca(_lowerCAmelCase , return_tensors="""np""" )
lowercase = processor_auto(_lowerCAmelCase , return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
lowercase = self._get_dummy_logits()
lowercase = processor_wavaveca.batch_decode(_lowerCAmelCase )
lowercase = processor_auto.batch_decode(_lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def _a ( self ) -> List[Any]:
'''simple docstring'''
lowercase = self.get_feature_extractor()
lowercase = self.get_tokenizer()
lowercase = self.get_decoder()
lowercase = WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , decoder=_lowerCAmelCase )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
@staticmethod
def _a ( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase = [d[key] for d in offsets]
return retrieved_list
def _a ( self ) -> str:
'''simple docstring'''
lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
lowercase = self._get_dummy_logits()[0]
lowercase = processor.decode(_lowerCAmelCase , output_word_offsets=_lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """end_offset""" ) , [1, 3, 5] )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
lowercase = self._get_dummy_logits()
lowercase = processor.batch_decode(_lowerCAmelCase , output_word_offsets=_lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) for o in outputs["""word_offsets"""]] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """end_offset""" ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def _a ( self ) -> Optional[int]:
'''simple docstring'''
import torch
lowercase = load_dataset("""common_voice""" , """en""" , split="""train""" , streaming=_lowerCAmelCase )
lowercase = ds.cast_column("""audio""" , datasets.Audio(sampling_rate=1_6000 ) )
lowercase = iter(_lowerCAmelCase )
lowercase = next(_lowerCAmelCase )
lowercase = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
lowercase = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
lowercase = processor(sample["""audio"""]["""array"""] , return_tensors="""pt""" ).input_values
with torch.no_grad():
lowercase = model(_lowerCAmelCase ).logits.cpu().numpy()
lowercase = processor.decode(logits[0] , output_word_offsets=_lowerCAmelCase )
lowercase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
lowercase = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
lowercase = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) , _lowerCAmelCase )
self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase , """word""" ) ) , output.text )
# output times
lowercase = torch.tensor(self.get_from_offsets(_lowerCAmelCase , """start_time""" ) )
lowercase = torch.tensor(self.get_from_offsets(_lowerCAmelCase , """end_time""" ) )
# fmt: off
lowercase = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
lowercase = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=0.01 ) )
self.assertTrue(torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=0.01 ) )
| 653 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : List[str] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
lowercase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
lowercase = [144, 192, 240]
lowercase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
lowercase = [96, 120, 144]
lowercase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
lowercase = [64, 80, 96]
lowercase = [16, 16, 24, 48, 64, 80, 320]
lowercase = 0.05
lowercase = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = 512
lowercase = 16
lowercase = 21
lowercase = """pascal-voc-id2label.json"""
else:
lowercase = 1000
lowercase = """imagenet-1k-id2label.json"""
lowercase = """huggingface/label-files"""
lowercase = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset""" ) , """r""" ) )
lowercase = {int(lowercase_ ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : Any=False ):
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
lowercase = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
lowercase = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
lowercase = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
lowercase = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
lowercase = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
lowercase = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
lowercase = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
lowercase = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
lowercase = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
lowercase = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowercase = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
lowercase = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
lowercase = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
lowercase = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
lowercase = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
lowercase = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
lowercase = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
lowercase = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
lowercase = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
lowercase = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
lowercase = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
lowercase = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
lowercase = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
lowercase = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
lowercase = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
lowercase = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
lowercase = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
lowercase = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
lowercase = """mobilevit.""" + name
return name
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : str=False ):
if base_model:
lowercase = """"""
else:
lowercase = """mobilevit."""
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowercase_ )
if key[:8] == "encoder.":
lowercase = key[8:]
if "qkv" in key:
lowercase = key.split(""".""" )
lowercase = int(key_split[0][6:] ) - 1
lowercase = int(key_split[3] )
lowercase = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
lowercase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
lowercase = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
lowercase = val[:dim, :]
lowercase = val[dim : dim * 2, :]
lowercase = val[-dim:, :]
else:
lowercase = val[:dim]
lowercase = val[dim : dim * 2]
lowercase = val[-dim:]
else:
lowercase = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( ):
lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : List[str]=False ):
lowercase = get_mobilevit_config(lowercase_ )
# load original state_dict
lowercase = torch.load(lowercase_ , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
lowercase = MobileViTForSemanticSegmentation(lowercase_ ).eval()
else:
lowercase = MobileViTForImageClassification(lowercase_ ).eval()
lowercase = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase = model(**lowercase_ )
lowercase = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
lowercase = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
lowercase = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
lowercase = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
lowercase = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
lowercase = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
lowercase = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , lowercase_ , atol=1E-4 )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase_ )
if push_to_hub:
lowercase = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
lowercase = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowercase_ , organization="""apple""" )
model.push_to_hub(lowercase_ , organization="""apple""" )
if __name__ == "__main__":
lowercase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, 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.'''
)
lowercase_ : List[str] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 653 | 1 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
assert isinstance(snake_case_ , snake_case_ ), F"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
__lowercase = F"The input value of [n={number}] has to be > 0"
raise ValueError(snake_case_ )
else:
__lowercase = sylvester(number - 1 )
__lowercase = num - 1
__lowercase = num
return lower * upper + 1
if __name__ == "__main__":
print(f'The 8th number in Sylvester\'s sequence: {sylvester(8)}')
| 41 |
import unittest
import numpy as np
from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class lowercase ( unittest.TestCase ):
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> Optional[int]:
_A : List[Any] = parent
_A : List[Any] = batch_size
_A : Dict = seq_length
_A : Optional[Any] = is_training
_A : int = use_attention_mask
_A : int = use_token_type_ids
_A : List[Any] = use_labels
_A : List[str] = vocab_size
_A : List[Any] = hidden_size
_A : str = num_hidden_layers
_A : Optional[Any] = num_attention_heads
_A : List[Any] = intermediate_size
_A : Any = hidden_act
_A : int = hidden_dropout_prob
_A : int = attention_probs_dropout_prob
_A : List[str] = max_position_embeddings
_A : Optional[int] = type_vocab_size
_A : List[str] = type_sequence_label_size
_A : Dict = initializer_range
_A : List[Any] = num_choices
def a__ ( self ) -> int:
_A : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A : Optional[Any] = None
if self.use_attention_mask:
_A : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
_A : Optional[int] = None
if self.use_token_type_ids:
_A : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A : Optional[int] = RobertaConfig(
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=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ ( self ) -> List[str]:
_A : Tuple = self.prepare_config_and_inputs()
_A , _A , _A , _A : str = config_and_inputs
_A : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def a__ ( self ) -> int:
_A : Any = self.prepare_config_and_inputs()
_A , _A , _A , _A : int = config_and_inputs
_A : int = True
_A : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_A : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class lowercase ( UpperCamelCase__,unittest.TestCase ):
_a = True
_a = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ ( self ) -> List[Any]:
_A : Optional[Any] = FlaxRobertaModelTester(self )
@slow
def a__ ( self ) -> Optional[int]:
for model_class_name in self.all_model_classes:
_A : Optional[int] = model_class_name.from_pretrained("""roberta-base""" , from_pt=_a )
_A : Union[str, Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
| 307 | 0 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
__lowerCamelCase : str =TaConfig.from_json_file(SCREAMING_SNAKE_CASE )
print(F'Building PyTorch model from configuration: {config}' )
__lowerCamelCase : Any =TaForConditionalGeneration(SCREAMING_SNAKE_CASE )
# Load weights from tf checkpoint
load_tf_weights_in_ta(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_UpperCamelCase = 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(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_UpperCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 363 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] =len(SCREAMING_SNAKE_CASE )
__lowerCamelCase : Union[str, Any] =[[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i in range(SCREAMING_SNAKE_CASE ):
__lowerCamelCase : Union[str, Any] =y_points[i]
for i in range(2 , SCREAMING_SNAKE_CASE ):
for j in range(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCamelCase : List[Any] =(
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( _lowerCamelCase , unittest.TestCase ):
a = VideoToVideoSDPipeline
a = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''} ) - {'''image''', '''width''', '''height'''}
a = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''} ) - {'''image'''}
a = PipelineTesterMixin.required_optional_params - {'''latents'''}
a = False
# No `output_type`.
a = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def _lowerCamelCase ( self ):
torch.manual_seed(0 )
A_ : Any = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") , up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") , cross_attention_dim=32 , attention_head_dim=4 , )
A_ : Optional[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
torch.manual_seed(0 )
A_ : Optional[int] = 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 )
A_ : List[str] = 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=1000 , hidden_act="""gelu""" , projection_dim=512 , )
A_ : Tuple = CLIPTextModel(_UpperCAmelCase )
A_ : List[str] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
A_ : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def _lowerCamelCase ( self , a__ , a__=0 ):
A_ : int = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_UpperCAmelCase )
else:
A_ : List[str] = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
A_ : Optional[int] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""video""": video,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def _lowerCamelCase ( self ):
A_ : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
A_ : Any = self.get_dummy_components()
A_ : List[str] = VideoToVideoSDPipeline(**_UpperCAmelCase )
A_ : Union[str, Any] = sd_pipe.to(_UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )
A_ : List[str] = self.get_dummy_inputs(_UpperCAmelCase )
A_ : Union[str, Any] = """np"""
A_ : List[str] = sd_pipe(**_UpperCAmelCase ).frames
A_ : Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
A_ : Optional[int] = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _lowerCamelCase ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_UpperCAmelCase , expected_max_diff=5E-3 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def _lowerCamelCase ( self ):
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def _lowerCamelCase ( self ):
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def _lowerCamelCase ( self ):
pass
def _lowerCamelCase ( self ):
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self ):
A_ : int = VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL""" , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
A_ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ : Tuple = torch.randn((1, 10, 3, 1024, 576) , generator=_UpperCAmelCase )
A_ : Optional[int] = video.to("""cuda""" )
A_ : Dict = """Spiderman is surfing"""
A_ : Optional[int] = pipe(_UpperCAmelCase , video=_UpperCAmelCase , generator=_UpperCAmelCase , num_inference_steps=3 , output_type="""pt""" ).frames
A_ : Dict = np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2
| 569 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowerCamelCase = 50_000
lowerCamelCase = 5_000
lowerCamelCase , lowerCamelCase = os.path.split(__file__)
lowerCamelCase = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
for i in range(lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i]
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
for i in range(0 , len(lowerCAmelCase__ ) , lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i : i + batch_size]
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
with dataset.formatted_as(type=lowerCAmelCase__ ):
for i in range(lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i]
@get_duration
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
with dataset.formatted_as(type=lowerCAmelCase__ ):
for i in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = dataset[i : i + batch_size]
def a__ ( ):
UpperCAmelCase_ = {"num examples": SPEED_TEST_N_EXAMPLES}
UpperCAmelCase_ = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted, {"type": "pandas", "length": SMALL_TEST}),
(read_formatted, {"type": "torch", "length": SMALL_TEST}),
(read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
]
UpperCAmelCase_ = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("generating dataset" )
UpperCAmelCase_ = datasets.Features(
{"list": datasets.Sequence(datasets.Value("float32" ) ), "numbers": datasets.Value("float32" )} )
UpperCAmelCase_ = generate_example_dataset(
os.path.join(lowerCAmelCase__ , "dataset.arrow" ) , lowerCAmelCase__ , num_examples=lowerCAmelCase__ , seq_shapes={"list": (100,)} , )
print("first set of iterations" )
for func, kwargs in functions:
print(func.__name__ , str(lowerCAmelCase__ ) )
UpperCAmelCase_ = func(lowerCAmelCase__ , **lowerCAmelCase__ )
print("shuffling dataset" )
UpperCAmelCase_ = dataset.shuffle()
print("Second set of iterations (after shuffling" )
for func, kwargs in functions_shuffled:
print("shuffled " , func.__name__ , str(lowerCAmelCase__ ) )
UpperCAmelCase_ = func(
lowerCAmelCase__ , **lowerCAmelCase__ )
with open(lowerCAmelCase__ , "wb" ) as f:
f.write(json.dumps(lowerCAmelCase__ ).encode("utf-8" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 82 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 711 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a ( _UpperCAmelCase ):
UpperCAmelCase__ : Dict = "Speech2TextFeatureExtractor"
UpperCAmelCase__ : str = "Speech2TextTokenizer"
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Any = self.feature_extractor
__lowerCamelCase: List[Any] = False
def __call__( self : List[str] , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCREAMING_SNAKE_CASE_ : List[Any] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
__lowerCamelCase: Dict = kwargs.pop("""raw_speech""" )
else:
__lowerCamelCase: int = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Dict = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: int = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
__lowerCamelCase: Dict = args[0]
__lowerCamelCase: Optional[Any] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
__lowerCamelCase: List[str] = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is not None:
__lowerCamelCase: Any = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
__lowerCamelCase: List[Any] = encodings["""input_ids"""]
return inputs
def SCREAMING_SNAKE_CASE__ ( self : List[str] , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : str ):
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : List[Any] ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
@contextmanager
def SCREAMING_SNAKE_CASE__ ( self : Any ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
__lowerCamelCase: Any = True
__lowerCamelCase: Any = self.tokenizer
yield
__lowerCamelCase: Any = self.feature_extractor
__lowerCamelCase: Optional[Any] = False
| 189 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> float:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_interest_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * daily_interest_rate * days_between_payments
def __snake_case ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> float:
"""simple docstring"""
if number_of_compounding_periods <= 0:
raise ValueError('''number_of_compounding_periods must be > 0''' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def __snake_case ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> float:
"""simple docstring"""
if number_of_years <= 0:
raise ValueError('''number_of_years must be > 0''' )
if nominal_annual_percentage_rate < 0:
raise ValueError('''nominal_annual_percentage_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return compound_interest(
SCREAMING_SNAKE_CASE_ , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 51 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ : Union[str, Any] = logging.getLogger(__name__)
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : List[Any] , lowerCAmelCase__ : Dict=-1 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = label_idx
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[Split, str] ):
"""simple docstring"""
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Any = mode.value
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCAmelCase__ , F"{mode}.txt" )
__SCREAMING_SNAKE_CASE : List[str] = 1
__SCREAMING_SNAKE_CASE : int = []
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
__SCREAMING_SNAKE_CASE : List[str] = []
__SCREAMING_SNAKE_CASE : str = []
for line in f:
if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
guid_index += 1
__SCREAMING_SNAKE_CASE : Dict = []
__SCREAMING_SNAKE_CASE : List[Any] = []
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = line.split(""" """ )
words.append(splits[0] )
if len(lowerCAmelCase__ ) > 1:
labels.append(splits[self.label_idx].replace("""\n""" , """""" ) )
else:
# Examples could have no label for mode = "test"
labels.append("""O""" )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
return examples
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : List ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for line in test_input_reader:
if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
writer.write(lowerCAmelCase__ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
__SCREAMING_SNAKE_CASE : List[str] = line.split()[0] + """ """ + preds_list[example_id].pop(0 ) + """\n"""
writer.write(lowerCAmelCase__ )
else:
logger.warning("""Maximum sequence length exceeded: No prediction for '%s'.""" , line.split()[0] )
def UpperCamelCase__ ( self : Union[str, Any] , lowerCAmelCase__ : str ):
"""simple docstring"""
if path:
with open(lowerCAmelCase__ , """r""" ) as f:
__SCREAMING_SNAKE_CASE : Any = f.read().splitlines()
if "O" not in labels:
__SCREAMING_SNAKE_CASE : Union[str, Any] = ["""O"""] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : List[str] ):
"""simple docstring"""
super().__init__(label_idx=-2 )
def UpperCamelCase__ ( self : List[str] , lowerCAmelCase__ : str ):
"""simple docstring"""
if path:
with open(lowerCAmelCase__ , """r""" ) as f:
__SCREAMING_SNAKE_CASE : Optional[Any] = f.read().splitlines()
if "O" not in labels:
__SCREAMING_SNAKE_CASE : Tuple = ["""O"""] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def UpperCamelCase__ ( self : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[Split, str] ):
"""simple docstring"""
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : List[str] = mode.value
__SCREAMING_SNAKE_CASE : Dict = os.path.join(lowerCAmelCase__ , F"{mode}.txt" )
__SCREAMING_SNAKE_CASE : Dict = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = []
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
for sentence in parse_incr(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = []
__SCREAMING_SNAKE_CASE : Optional[int] = []
for token in sentence:
words.append(token["""form"""] )
labels.append(token["""upos"""] )
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
guid_index += 1
return examples
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : TextIO , lowerCAmelCase__ : List ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = 0
for sentence in parse_incr(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = preds_list[example_id]
__SCREAMING_SNAKE_CASE : Union[str, Any] = """"""
for token in sentence:
out += F"{token['form']} ({token['upos']}|{s_p.pop(0 )}) "
out += "\n"
writer.write(lowerCAmelCase__ )
example_id += 1
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : str ):
"""simple docstring"""
if path:
with open(lowerCAmelCase__ , """r""" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
] | 578 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : str = """timesformer"""
def __init__( self : int , UpperCamelCase__ : Optional[int]=224 , UpperCamelCase__ : Dict=16 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Optional[Any]=8 , UpperCamelCase__ : str=768 , UpperCamelCase__ : int=12 , UpperCamelCase__ : Dict=12 , UpperCamelCase__ : Union[str, Any]=3072 , UpperCamelCase__ : str="gelu" , UpperCamelCase__ : Any=0.0 , UpperCamelCase__ : str=0.0 , UpperCamelCase__ : Optional[int]=0.02 , UpperCamelCase__ : List[Any]=1E-6 , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Optional[Any]="divided_space_time" , UpperCamelCase__ : Optional[int]=0 , **UpperCamelCase__ : int , ):
super().__init__(**UpperCamelCase__ )
A__ : Any =image_size
A__ : Union[str, Any] =patch_size
A__ : Tuple =num_channels
A__ : Dict =num_frames
A__ : Optional[Any] =hidden_size
A__ : Optional[int] =num_hidden_layers
A__ : List[str] =num_attention_heads
A__ : Dict =intermediate_size
A__ : str =hidden_act
A__ : List[Any] =hidden_dropout_prob
A__ : int =attention_probs_dropout_prob
A__ : List[str] =initializer_range
A__ : Optional[Any] =layer_norm_eps
A__ : Dict =qkv_bias
A__ : int =attention_type
A__ : Union[str, Any] =drop_path_rate
| 595 | """simple docstring"""
__A : int = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def lowercase ( UpperCamelCase : str ):
"""simple docstring"""
A__ : Union[str, Any] ={"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
A__ : Tuple =0
A__ : List[str] =0
while place < len(UpperCamelCase ):
if (place + 1 < len(UpperCamelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
A__ : Dict =[]
for arabic, roman in ROMAN:
((A__) , (A__)) : Union[str, Any] =divmod(UpperCamelCase , UpperCamelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 595 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'pipelines_utils',
'0.22.0',
'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
standard_warn=False,
stacklevel=3,
)
| 350 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a_ :
pass
| 350 | 1 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCAmelCase ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str=0.9_99 , UpperCamelCase__ : Dict="cosine" , ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(UpperCamelCase__ : Union[str, Any] ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(UpperCamelCase__ : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
__UpperCAmelCase = []
for i in range(UpperCamelCase__ ):
__UpperCAmelCase = i / num_diffusion_timesteps
__UpperCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCamelCase__ ) / alpha_bar_fn(UpperCamelCase__ ) , UpperCamelCase__ ) )
return torch.tensor(UpperCamelCase__ , dtype=torch.floataa )
class A ( UpperCAmelCase , UpperCAmelCase ):
a_ = [e.name for e in KarrasDiffusionSchedulers]
a_ = 2
@register_to_config
def __init__( self : Tuple , __a : int = 1_0_0_0 , __a : float = 0.0_0_0_8_5 , __a : float = 0.0_1_2 , __a : str = "linear" , __a : Optional[Union[np.ndarray, List[float]]] = None , __a : str = "epsilon" , __a : Optional[bool] = False , __a : Optional[bool] = False , __a : float = 1.0 , __a : str = "linspace" , __a : int = 0 , ) -> str:
if trained_betas is not None:
__UpperCAmelCase = torch.tensor(__a , dtype=torch.floataa )
elif beta_schedule == "linear":
__UpperCAmelCase = torch.linspace(__a , __a , __a , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__UpperCAmelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __a , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__UpperCAmelCase = betas_for_alpha_bar(__a , alpha_transform_type='''cosine''' )
elif beta_schedule == "exp":
__UpperCAmelCase = betas_for_alpha_bar(__a , alpha_transform_type='''exp''' )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
__UpperCAmelCase = 1.0 - self.betas
__UpperCAmelCase = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(__a , __a , __a )
__UpperCAmelCase = use_karras_sigmas
def snake_case__ ( self : Optional[int] , __a : int , __a : List[Any]=None ) -> Any:
if schedule_timesteps is None:
__UpperCAmelCase = self.timesteps
__UpperCAmelCase = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
__UpperCAmelCase = 1 if len(__a ) > 1 else 0
else:
__UpperCAmelCase = timestep.cpu().item() if torch.is_tensor(__a ) else timestep
__UpperCAmelCase = self._index_counter[timestep_int]
return indices[pos].item()
@property
def snake_case__ ( self : List[str] ) -> Tuple:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def snake_case__ ( self : int , __a : torch.FloatTensor , __a : Union[float, torch.FloatTensor] , ) -> torch.FloatTensor:
__UpperCAmelCase = self.index_for_timestep(__a )
__UpperCAmelCase = self.sigmas[step_index]
__UpperCAmelCase = sample / ((sigma**2 + 1) ** 0.5)
return sample
def snake_case__ ( self : str , __a : int , __a : Union[str, torch.device] = None , __a : Optional[int] = None , ) -> Union[str, Any]:
__UpperCAmelCase = num_inference_steps
__UpperCAmelCase = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
__UpperCAmelCase = np.linspace(0 , num_train_timesteps - 1 , __a , dtype=__a )[::-1].copy()
elif self.config.timestep_spacing == "leading":
__UpperCAmelCase = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__UpperCAmelCase = (np.arange(0 , __a ) * step_ratio).round()[::-1].copy().astype(__a )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
__UpperCAmelCase = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__UpperCAmelCase = (np.arange(__a , 0 , -step_ratio )).round().copy().astype(__a )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
__UpperCAmelCase = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
__UpperCAmelCase = np.log(__a )
__UpperCAmelCase = np.interp(__a , np.arange(0 , len(__a ) ) , __a )
if self.config.use_karras_sigmas:
__UpperCAmelCase = self._convert_to_karras(in_sigmas=__a , num_inference_steps=self.num_inference_steps )
__UpperCAmelCase = np.array([self._sigma_to_t(__a , __a ) for sigma in sigmas] )
__UpperCAmelCase = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
__UpperCAmelCase = torch.from_numpy(__a ).to(device=__a )
__UpperCAmelCase = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
__UpperCAmelCase = torch.from_numpy(__a )
__UpperCAmelCase = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(__a ).startswith('''mps''' ):
# mps does not support float64
__UpperCAmelCase = timesteps.to(__a , dtype=torch.floataa )
else:
__UpperCAmelCase = timesteps.to(device=__a )
# empty dt and derivative
__UpperCAmelCase = None
__UpperCAmelCase = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
__UpperCAmelCase = defaultdict(__a )
def snake_case__ ( self : Tuple , __a : Optional[int] , __a : Optional[Any] ) -> List[str]:
# get log sigma
__UpperCAmelCase = np.log(__a )
# get distribution
__UpperCAmelCase = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
__UpperCAmelCase = np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
__UpperCAmelCase = low_idx + 1
__UpperCAmelCase = log_sigmas[low_idx]
__UpperCAmelCase = log_sigmas[high_idx]
# interpolate sigmas
__UpperCAmelCase = (low - log_sigma) / (low - high)
__UpperCAmelCase = np.clip(__a , 0 , 1 )
# transform interpolation to time range
__UpperCAmelCase = (1 - w) * low_idx + w * high_idx
__UpperCAmelCase = t.reshape(sigma.shape )
return t
def snake_case__ ( self : List[str] , __a : torch.FloatTensor , __a : int ) -> torch.FloatTensor:
__UpperCAmelCase = in_sigmas[-1].item()
__UpperCAmelCase = in_sigmas[0].item()
__UpperCAmelCase = 7.0 # 7.0 is the value used in the paper
__UpperCAmelCase = np.linspace(0 , 1 , __a )
__UpperCAmelCase = sigma_min ** (1 / rho)
__UpperCAmelCase = sigma_max ** (1 / rho)
__UpperCAmelCase = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def snake_case__ ( self : List[Any] ) -> List[Any]:
return self.dt is None
def snake_case__ ( self : str , __a : Union[torch.FloatTensor, np.ndarray] , __a : Union[float, torch.FloatTensor] , __a : Union[torch.FloatTensor, np.ndarray] , __a : bool = True , ) -> Union[SchedulerOutput, Tuple]:
__UpperCAmelCase = self.index_for_timestep(__a )
# advance index counter by 1
__UpperCAmelCase = timestep.cpu().item() if torch.is_tensor(__a ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
__UpperCAmelCase = self.sigmas[step_index]
__UpperCAmelCase = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
__UpperCAmelCase = self.sigmas[step_index - 1]
__UpperCAmelCase = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
__UpperCAmelCase = 0
__UpperCAmelCase = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
__UpperCAmelCase = sigma_hat if self.state_in_first_order else sigma_next
__UpperCAmelCase = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
__UpperCAmelCase = sigma_hat if self.state_in_first_order else sigma_next
__UpperCAmelCase = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
__UpperCAmelCase = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.config.clip_sample:
__UpperCAmelCase = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
__UpperCAmelCase = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
__UpperCAmelCase = sigma_next - sigma_hat
# store for 2nd order step
__UpperCAmelCase = derivative
__UpperCAmelCase = dt
__UpperCAmelCase = sample
else:
# 2. 2nd order / Heun's method
__UpperCAmelCase = (sample - pred_original_sample) / sigma_next
__UpperCAmelCase = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
__UpperCAmelCase = self.dt
__UpperCAmelCase = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
__UpperCAmelCase = None
__UpperCAmelCase = None
__UpperCAmelCase = None
__UpperCAmelCase = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__a )
def snake_case__ ( self : Optional[int] , __a : torch.FloatTensor , __a : torch.FloatTensor , __a : torch.FloatTensor , ) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__UpperCAmelCase = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__a ):
# mps does not support float64
__UpperCAmelCase = self.timesteps.to(original_samples.device , dtype=torch.floataa )
__UpperCAmelCase = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
__UpperCAmelCase = self.timesteps.to(original_samples.device )
__UpperCAmelCase = timesteps.to(original_samples.device )
__UpperCAmelCase = [self.index_for_timestep(__a , __a ) for t in timesteps]
__UpperCAmelCase = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
__UpperCAmelCase = sigma.unsqueeze(-1 )
__UpperCAmelCase = original_samples + noise * sigma
return noisy_samples
def __len__( self : Union[str, Any] ) -> Tuple:
return self.config.num_train_timesteps
| 708 | '''simple docstring'''
import argparse
import os
import re
import packaging.version
__lowerCAmelCase : Optional[int] = "examples/"
__lowerCAmelCase : Dict = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
"doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCAmelCase : List[str] = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
__lowerCAmelCase : int = "README.md"
def lowerCAmelCase ( UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple ):
"""simple docstring"""
with open(UpperCamelCase__ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__UpperCAmelCase = f.read()
__UpperCAmelCase , __UpperCAmelCase = REPLACE_PATTERNS[pattern]
__UpperCAmelCase = replace.replace('''VERSION''' , UpperCamelCase__ )
__UpperCAmelCase = re_pattern.sub(UpperCamelCase__ , UpperCamelCase__ )
with open(UpperCamelCase__ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(UpperCamelCase__ )
def lowerCAmelCase ( UpperCamelCase__ : Optional[int] ):
"""simple docstring"""
for folder, directories, fnames in os.walk(UpperCamelCase__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ , pattern='''examples''' )
def lowerCAmelCase ( UpperCamelCase__ : Any , UpperCamelCase__ : Any=False ):
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not patch:
update_version_in_examples(UpperCamelCase__ )
def lowerCAmelCase ( ):
"""simple docstring"""
__UpperCAmelCase = '''🤗 Transformers currently provides the following architectures'''
__UpperCAmelCase = '''1. Want to contribute a new model?'''
with open(UpperCamelCase__ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__UpperCAmelCase = f.readlines()
# Find the start of the list.
__UpperCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__UpperCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
__UpperCAmelCase = lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(UpperCamelCase__ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(UpperCamelCase__ )
def lowerCAmelCase ( ):
"""simple docstring"""
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
__UpperCAmelCase = f.read()
__UpperCAmelCase = REPLACE_PATTERNS['''init'''][0].search(UpperCamelCase__ ).groups()[0]
return packaging.version.parse(UpperCamelCase__ )
def lowerCAmelCase ( UpperCamelCase__ : Any=False ):
"""simple docstring"""
__UpperCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
__UpperCAmelCase = default_version.base_version
elif patch:
__UpperCAmelCase = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
__UpperCAmelCase = f"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
__UpperCAmelCase = input(f"""Which version are you releasing? [{default_version}]""" )
if len(UpperCamelCase__ ) == 0:
__UpperCAmelCase = default_version
print(f"""Updating version to {version}.""" )
global_version_update(UpperCamelCase__ , patch=UpperCamelCase__ )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def lowerCAmelCase ( ):
"""simple docstring"""
__UpperCAmelCase = get_version()
__UpperCAmelCase = f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
__UpperCAmelCase = current_version.base_version
# Check with the user we got that right.
__UpperCAmelCase = input(f"""Which version are we developing now? [{dev_version}]""" )
if len(UpperCamelCase__ ) == 0:
__UpperCAmelCase = dev_version
print(f"""Updating version to {version}.""" )
global_version_update(UpperCamelCase__ )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__lowerCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
__lowerCAmelCase : Tuple = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 654 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : int = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class a__ ( _UpperCamelCase , unittest.TestCase ):
a : int = AlbertTokenizer
a : List[Any] = AlbertTokenizerFast
a : int = True
a : Dict = True
a : int = True
def lowerCAmelCase_ ( self ) -> Dict:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a = AlbertTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase_ ( self , A ) -> Union[str, Any]:
'''simple docstring'''
a = "this is a test"
a = "this is a test"
return input_text, output_text
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
a = "<pad>"
a = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "▁eloquent" )
self.assertEqual(len(a_ ) , 30000 )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 30000 )
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a = self.get_tokenizer()
a = self.get_rust_tokenizer()
a = "I was born in 92000, and this is falsé."
a = tokenizer.tokenize(a_ )
a = rust_tokenizer.tokenize(a_ )
self.assertListEqual(a_ , a_ )
a = tokenizer.encode(a_ , add_special_tokens=a_ )
a = rust_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
a = self.get_rust_tokenizer()
a = tokenizer.encode(a_ )
a = rust_tokenizer.encode(a_ )
self.assertListEqual(a_ , a_ )
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
a = AlbertTokenizer(a_ , keep_accents=a_ )
a = tokenizer.tokenize("This is a test" )
self.assertListEqual(a_ , ["▁this", "▁is", "▁a", "▁test"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ ) , [48, 25, 21, 1289] )
a = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
a_ , ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."] )
a = tokenizer.convert_tokens_to_ids(a_ )
self.assertListEqual(a_ , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
a = tokenizer.convert_ids_to_tokens(a_ )
self.assertListEqual(
a_ , ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."] , )
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
a = AlbertTokenizer(a_ )
a = tokenizer.encode("sequence builders" )
a = tokenizer.encode("multi-sequence build" )
a = tokenizer.build_inputs_with_special_tokens(a_ )
a = tokenizer.build_inputs_with_special_tokens(a_ , a_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
a = {"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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=a_ , model_name="albert-base-v2" , revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e" , )
| 515 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
__snake_case , __snake_case = 1, 1
for _ in range(number_of_steps - 1 ):
__snake_case , __snake_case = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 0 |
import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ) ->Any:
"""simple docstring"""
a__ :Optional[Any] = []
def _snake_case ( self : Optional[Any] , __A : List[Any] ) ->List[str]:
"""simple docstring"""
return self.node_position[vertex]
def _snake_case ( self : Optional[Any] , __A : str , __A : Any ) ->Dict:
"""simple docstring"""
a__ :Dict = pos
def _snake_case ( self : str , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[Any] , __A : Optional[int] ) ->List[Any]:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
a__ :str = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
a__ :Optional[int] = 2 * start + 1
else:
a__ :List[Any] = 2 * start + 2
if heap[smallest_child] < heap[start]:
a__ , a__ :Optional[Any] = heap[smallest_child], positions[smallest_child]
a__ , a__ :int = (
heap[start],
positions[start],
)
a__ , a__ :List[Any] = temp, tempa
a__ :Any = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , __A )
self.top_to_bottom(__A , __A , __A , __A )
def _snake_case ( self : List[str] , __A : Any , __A : List[str] , __A : Any , __A : str ) ->Optional[Any]:
"""simple docstring"""
a__ :Optional[Any] = position[index]
while index != 0:
a__ :str = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
a__ :int = heap[parent]
a__ :Optional[Any] = position[parent]
self.set_position(position[parent] , __A )
else:
a__ :List[Any] = val
a__ :List[Any] = temp
self.set_position(__A , __A )
break
a__ :Union[str, Any] = parent
else:
a__ :int = val
a__ :Dict = temp
self.set_position(__A , 0 )
def _snake_case ( self : Tuple , __A : int , __A : int ) ->Union[str, Any]:
"""simple docstring"""
a__ :Tuple = len(__A ) // 2 - 1
for i in range(__A , -1 , -1 ):
self.top_to_bottom(__A , __A , len(__A ) , __A )
def _snake_case ( self : List[Any] , __A : List[Any] , __A : int ) ->Optional[Any]:
"""simple docstring"""
a__ :Any = positions[0]
a__ :str = sys.maxsize
self.top_to_bottom(__A , 0 , len(__A ) , __A )
return temp
def lowerCamelCase__ ( a : Any ) -> Union[str, Any]:
"""simple docstring"""
a__ :Tuple = Heap()
a__ :List[Any] = [0] * len(a )
a__ :str = [-1] * len(a ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
a__ :Any = [] # Heap of Distance of vertices from their neighboring vertex
a__ :int = []
for vertex in range(len(a ) ):
distance_tv.append(sys.maxsize )
positions.append(a )
heap.node_position.append(a )
a__ :Tuple = []
a__ :Any = 1
a__ :int = sys.maxsize
for neighbor, distance in adjacency_list[0]:
a__ :int = 0
a__ :List[str] = distance
heap.heapify(a , a )
for _ in range(1 , len(a ) ):
a__ :Dict = heap.delete_minimum(a , a )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
a__ :Optional[int] = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(a )]
):
a__ :List[str] = distance
heap.bottom_to_top(
a , heap.get_position(a ) , a , a )
a__ :str = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
snake_case__ = int(input('''Enter number of edges: ''').strip())
snake_case__ = defaultdict(list)
for _ in range(edges_number):
snake_case__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 373 |
def lowerCamelCase__ ( a : int , a : int ) -> Any:
"""simple docstring"""
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
a__ :Optional[int] = (boundary[1] - boundary[0]) / steps
a__ :str = boundary[0]
a__ :Optional[int] = boundary[1]
a__ :str = make_points(a , a , a )
a__ :Any = 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 lowerCamelCase__ ( a : Any , a : str , a : Dict ) -> int:
"""simple docstring"""
a__ :Union[str, Any] = a + h
while x < (b - h):
yield x
a__ :str = x + h
def lowerCamelCase__ ( a : Optional[int] ) -> List[Any]: # enter your function here
"""simple docstring"""
a__ :Tuple = (x - 0) * (x - 0)
return y
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
a__ :Optional[Any] = 0.0 # Lower bound of integration
a__ :Optional[Any] = 1.0 # Upper bound of integration
a__ :List[str] = 1_0.0 # define number of steps or resolution
a__ :int = [a, b] # define boundary of integration
a__ :str = method_a(a , a )
print(F'''y = {y}''' )
if __name__ == "__main__":
main()
| 373 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( ) -> str:
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , 1_001 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 159 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class a__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
_A = TextToVideoSDPipeline
_A = TEXT_TO_IMAGE_PARAMS
_A = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
_A = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_: int = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") , up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") , cross_attention_dim=32 , attention_head_dim=4 , )
lowerCamelCase_: Dict = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=A_ , set_alpha_to_one=A_ , )
torch.manual_seed(0 )
lowerCamelCase_: Tuple = 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=1_28 , )
torch.manual_seed(0 )
lowerCamelCase_: str = 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=10_00 , hidden_act="""gelu""" , projection_dim=5_12 , )
lowerCamelCase_: Dict = CLIPTextModel(A_ )
lowerCamelCase_: Optional[int] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCamelCase_: Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def lowerCAmelCase ( self : Optional[int] , A_ : Union[str, Any] , A_ : Dict=0 ) -> List[Any]:
"""simple docstring"""
if str(A_ ).startswith("""mps""" ):
lowerCamelCase_: Dict = torch.manual_seed(A_ )
else:
lowerCamelCase_: Tuple = torch.Generator(device=A_ ).manual_seed(A_ )
lowerCamelCase_: Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_: List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_: Optional[int] = self.get_dummy_components()
lowerCamelCase_: Any = TextToVideoSDPipeline(**A_ )
lowerCamelCase_: int = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
lowerCamelCase_: Tuple = self.get_dummy_inputs(A_ )
lowerCamelCase_: str = """np"""
lowerCamelCase_: int = sd_pipe(**A_ ).frames
lowerCamelCase_: Optional[int] = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
lowerCamelCase_: Dict = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_ , expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_ , expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def lowerCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
return super().test_progress_bar()
@slow
@skip_mps
class a__ ( unittest.TestCase ):
def lowerCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
lowerCamelCase_: int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
lowerCamelCase_: str = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
lowerCamelCase_: Tuple = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowerCamelCase_: List[Any] = pipe.to("""cuda""" )
lowerCamelCase_: Optional[Any] = """Spiderman is surfing"""
lowerCamelCase_: int = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCamelCase_: List[Any] = pipe(A_ , generator=A_ , num_inference_steps=25 , output_type="""pt""" ).frames
lowerCamelCase_: Optional[Any] = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_: List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
lowerCamelCase_: int = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
lowerCamelCase_: Optional[Any] = pipe.to("""cuda""" )
lowerCamelCase_: Union[str, Any] = """Spiderman is surfing"""
lowerCamelCase_: Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCamelCase_: Any = pipe(A_ , generator=A_ , num_inference_steps=2 , output_type="""pt""" ).frames
lowerCamelCase_: int = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 423 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , )
return model
@property
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = 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=10_00 , )
return CLIPTextModel(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : int = DDIMScheduler()
SCREAMING_SNAKE_CASE : List[Any] = self.dummy_vq_model
SCREAMING_SNAKE_CASE : Dict = LDMPipeline(unet=lowerCamelCase_ , vqvae=lowerCamelCase_ , scheduler=lowerCamelCase_ )
ldm.to(lowerCamelCase_ )
ldm.set_progress_bar_config(disable=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = ldm(generator=lowerCamelCase_ , num_inference_steps=2 , output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[int] = ldm(generator=lowerCamelCase_ , num_inference_steps=2 , output_type="""numpy""" , return_dict=lowerCamelCase_ )[0]
SCREAMING_SNAKE_CASE : str = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : Optional[int] = np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172] )
SCREAMING_SNAKE_CASE : Dict = 1e-2 if torch_device != '''mps''' else 3e-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256""" )
ldm.to(lowerCamelCase_ )
ldm.set_progress_bar_config(disable=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = ldm(generator=lowerCamelCase_ , num_inference_steps=5 , output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447] )
SCREAMING_SNAKE_CASE : str = 1e-2 if torch_device != '''mps''' else 3e-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 718 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE : List[str] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : DDPMScheduler , lowerCamelCase_ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , movq=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : int ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : Tuple = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Dict = latents.to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE : List[Any] = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Tuple=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.""" )
SCREAMING_SNAKE_CASE : 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)
SCREAMING_SNAKE_CASE : Union[str, Any] = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = cpu_offload_with_hook(lowerCamelCase_ , lowerCamelCase_ , prev_module_hook=lowerCamelCase_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase_ ( self : str ):
'''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, List[torch.FloatTensor]] , lowerCamelCase_ : torch.FloatTensor , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 1_00 , lowerCamelCase_ : float = 4.0 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._execution_device
SCREAMING_SNAKE_CASE : Optional[int] = guidance_scale > 1.0
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(lowerCamelCase_ , dim=0 )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[Any] = image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Optional[int] = negative_image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Dict = hint.repeat_interleave(lowerCamelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase_ )
self.scheduler.set_timesteps(lowerCamelCase_ , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Any = self.movq.config.latent_channels
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = downscale_height_and_width(lowerCamelCase_ , lowerCamelCase_ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Union[str, Any] = {"""image_embeds""": image_embeds, """hint""": hint}
SCREAMING_SNAKE_CASE : Dict = self.unet(
sample=lowerCamelCase_ , timestep=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , added_cond_kwargs=lowerCamelCase_ , return_dict=lowerCamelCase_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : 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"]
):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : str = self.scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ , )[0]
# post-processing
SCREAMING_SNAKE_CASE : List[str] = 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"]:
SCREAMING_SNAKE_CASE : Optional[int] = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase_ )
| 79 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
SCREAMING_SNAKE_CASE : int = get_logger()
SCREAMING_SNAKE_CASE : Optional[dict] = None
class UpperCamelCase ( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
'''simple docstring'''
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , **UpperCamelCase_ ):
super().__init__(features=UpperCamelCase_ )
import jax
from jaxlib.xla_client import Device
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(
f"Expected {device} to be a `str` not {type(UpperCamelCase_ )}, as `jaxlib.xla_extension.Device` "
'''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
'''the device with `str()` to get its string identifier that will be internally mapped '''
'''to the actual `jaxlib.xla_extension.Device`.''' )
lowercase_ :Optional[Any] = device if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
lowercase_ :Union[str, Any] = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"Device with string identifier {self.device} not listed among the available "
f"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
f"device: {str(jax.devices()[0] )}." )
lowercase_ :Tuple = str(jax.devices()[0] )
lowercase_ :List[Any] = jnp_array_kwargs
@staticmethod
def UpperCamelCase ( ):
import jax
return {str(UpperCamelCase_ ): device for device in jax.devices()}
def UpperCamelCase ( self , UpperCamelCase_ ):
import jax
import jax.numpy as jnp
if isinstance(UpperCamelCase_ , UpperCamelCase_ ) and column:
if all(
isinstance(UpperCamelCase_ , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(UpperCamelCase_ , axis=0 )
return column
def UpperCamelCase ( self , UpperCamelCase_ ):
import jax
import jax.numpy as jnp
if isinstance(UpperCamelCase_ , (str, bytes, type(UpperCamelCase_ )) ):
return value
elif isinstance(UpperCamelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase_ :Optional[int] = {}
if isinstance(UpperCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
lowercase_ :Optional[Any] = {'''dtype''': jnp.intaa}
else:
lowercase_ :List[Any] = {'''dtype''': jnp.intaa}
elif isinstance(UpperCamelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase_ :Union[str, Any] = {'''dtype''': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(UpperCamelCase_ , PIL.Image.Image ):
lowercase_ :List[Any] = np.asarray(UpperCamelCase_ )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
lowercase_ :List[str] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(UpperCamelCase_ , **{**default_dtype, **self.jnp_array_kwargs} )
def UpperCamelCase ( self , UpperCamelCase_ ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(UpperCamelCase_ , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(UpperCamelCase_ , '''__array__''' ) and not isinstance(UpperCamelCase_ , jax.Array ):
lowercase_ :Any = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(UpperCamelCase_ , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(UpperCamelCase_ ) for substruct in data_struct] )
elif isinstance(UpperCamelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(UpperCamelCase_ ) for substruct in data_struct] )
return self._tensorize(UpperCamelCase_ )
def UpperCamelCase ( self , UpperCamelCase_ ):
return map_nested(self._recursive_tensorize , UpperCamelCase_ , map_list=UpperCamelCase_ )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :List[str] = self.numpy_arrow_extractor().extract_row(UpperCamelCase_ )
lowercase_ :Optional[Any] = self.python_features_decoder.decode_row(UpperCamelCase_ )
return self.recursive_tensorize(UpperCamelCase_ )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :int = self.numpy_arrow_extractor().extract_column(UpperCamelCase_ )
lowercase_ :Any = self.python_features_decoder.decode_column(UpperCamelCase_ , pa_table.column_names[0] )
lowercase_ :Optional[Any] = self.recursive_tensorize(UpperCamelCase_ )
lowercase_ :Dict = self._consolidate(UpperCamelCase_ )
return column
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :Dict = self.numpy_arrow_extractor().extract_batch(UpperCamelCase_ )
lowercase_ :Optional[int] = self.python_features_decoder.decode_batch(UpperCamelCase_ )
lowercase_ :List[Any] = self.recursive_tensorize(UpperCamelCase_ )
for column_name in batch:
lowercase_ :Any = self._consolidate(batch[column_name] )
return batch
| 257 |
from __future__ import annotations
from typing import Any
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 0 ):
lowercase_ , lowercase_ :Optional[Any] = row, column
lowercase_ :Dict = [[default_value for c in range(UpperCamelCase_ )] for r in range(UpperCamelCase_ )]
def __str__( self ):
lowercase_ :Union[str, Any] = f"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
lowercase_ :List[str] = 0
for row_vector in self.array:
for obj in row_vector:
lowercase_ :int = max(UpperCamelCase_ , len(str(UpperCamelCase_ ) ) )
lowercase_ :int = f"%{max_element_length}s"
# Make string and return
def single_line(UpperCamelCase_ ) -> str:
nonlocal string_format_identifier
lowercase_ :Union[str, Any] = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(UpperCamelCase_ ) for row_vector in self.array )
return s
def __repr__( self ):
return str(self )
def UpperCamelCase ( self , UpperCamelCase_ ):
if not (isinstance(UpperCamelCase_ , (list, tuple) ) and len(UpperCamelCase_ ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self , UpperCamelCase_ ):
assert self.validate_indicies(UpperCamelCase_ )
return self.array[loc[0]][loc[1]]
def __setitem__( self , UpperCamelCase_ , UpperCamelCase_ ):
assert self.validate_indicies(UpperCamelCase_ )
lowercase_ :Union[str, Any] = value
def __add__( self , UpperCamelCase_ ):
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert self.row == another.row and self.column == another.column
# Add
lowercase_ :Tuple = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
lowercase_ :str = self[r, c] + another[r, c]
return result
def __neg__( self ):
lowercase_ :str = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
lowercase_ :str = -self[r, c]
return result
def __sub__( self , UpperCamelCase_ ):
return self + (-another)
def __mul__( self , UpperCamelCase_ ):
if isinstance(UpperCamelCase_ , (int, float) ): # Scalar multiplication
lowercase_ :Any = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
lowercase_ :Optional[int] = self[r, c] * another
return result
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ): # Matrix multiplication
assert self.column == another.row
lowercase_ :Any = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
lowercase_ :int = f"Unsupported type given for another ({type(UpperCamelCase_ )})"
raise TypeError(UpperCamelCase_ )
def UpperCamelCase ( self ):
lowercase_ :str = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
lowercase_ :Optional[int] = self[r, c]
return result
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ):
assert isinstance(UpperCamelCase_ , UpperCamelCase_ ) and isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
lowercase_ :Tuple = v.transpose()
lowercase_ :List[str] = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase_ :Any = Matrix(3 , 3 , 0 )
for i in range(3 ):
lowercase_ :List[str] = 1
print(f"a^(-1) is {ainv}" )
# u, v
lowercase_ :Tuple = Matrix(3 , 1 , 0 )
lowercase_ , lowercase_ , lowercase_ :List[Any] = 1, 2, -3
lowercase_ :int = Matrix(3 , 1 , 0 )
lowercase_ , lowercase_ , lowercase_ :Dict = 4, -2, 5
print(f"u is {u}" )
print(f"v is {v}" )
print(f"uv^T is {u * v.transpose()}" )
# Sherman Morrison
print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(_a , _a )}" )
def UpperCamelCase ( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
testa()
| 257 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase = logging.getLogger()
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = {}
UpperCAmelCase = os.path.join(_snake_case , """all_results.json""" )
if os.path.exists(_snake_case ):
with open(_snake_case , """r""" ) as f:
UpperCAmelCase = json.load(_snake_case )
else:
raise ValueError(F'''can\'t find {path}''' )
return results
_UpperCamelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCamelCase__ ( snake_case ):
def _UpperCamelCase ( self ):
import xla_spawn
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = F'''
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(A ,"""argv""" ,A ):
UpperCAmelCase = time()
xla_spawn.main()
UpperCAmelCase = time()
UpperCAmelCase = get_results(A )
self.assertGreaterEqual(result["""eval_accuracy"""] ,0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start ,500 )
def _UpperCamelCase ( self ):
import xla_spawn
UpperCAmelCase = """
./tests/test_trainer_tpu.py
--num_cores=8
./tests/test_trainer_tpu.py
""".split()
with patch.object(A ,"""argv""" ,A ):
xla_spawn.main()
| 74 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCamelCase__ :
def __init__( self ,A = 6 ):
UpperCAmelCase = None
UpperCAmelCase = None
self.create_linked_list(A )
def _UpperCamelCase ( self ,A ):
UpperCAmelCase = Node()
UpperCAmelCase = current_node
UpperCAmelCase = current_node
UpperCAmelCase = current_node
for _ in range(1 ,A ):
UpperCAmelCase = Node()
UpperCAmelCase = current_node
UpperCAmelCase = previous_node
UpperCAmelCase = current_node
UpperCAmelCase = self.front
UpperCAmelCase = previous_node
def _UpperCamelCase ( self ):
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCamelCase ( self ):
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCamelCase ( self ,A ):
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
UpperCAmelCase = self.rear.next
if self.rear:
UpperCAmelCase = data
def _UpperCamelCase ( self ):
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
UpperCAmelCase = self.front.data
UpperCAmelCase = None
return data
UpperCAmelCase = self.front
UpperCAmelCase = old_front.next
UpperCAmelCase = old_front.data
UpperCAmelCase = None
return data
def _UpperCamelCase ( self ):
if self.is_empty():
raise Exception("""Empty Queue""" )
def _UpperCamelCase ( self ):
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class lowerCamelCase__ :
def __init__( self ):
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__lowercase = TypeVar("""_T""")
class _lowercase ( Generic[_T] ):
def __init__( self : Any , lowerCamelCase__ : Iterable[_T] | None = None ) -> None:
"""simple docstring"""
A_ = list(iterable or [] )
A_ = []
def __len__( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self : Dict ) -> str:
"""simple docstring"""
return F"Queue({tuple(self._stacka[::-1] + self._stacka )})"
def UpperCamelCase ( self : Union[str, Any] , lowerCamelCase__ : _T ) -> None:
"""simple docstring"""
self._stacka.append(lowerCamelCase__ )
def UpperCamelCase ( self : str ) -> _T:
"""simple docstring"""
A_ = self._stacka.pop
A_ = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('''Queue is empty''' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 203 |
__lowercase = """Alexander Joslin"""
import operator as op
from .stack import Stack
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
A_ = Stack()
A_ = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(SCREAMING_SNAKE_CASE ) )
elif i in operators:
# RULE 2
operator_stack.push(SCREAMING_SNAKE_CASE )
elif i == ")":
# RULE 4
A_ = operator_stack.peek()
operator_stack.pop()
A_ = operand_stack.peek()
operand_stack.pop()
A_ = operand_stack.peek()
operand_stack.pop()
A_ = operators[opr](SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
operand_stack.push(SCREAMING_SNAKE_CASE )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__lowercase = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')
| 203 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : List[str] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'SqueezeBertOnnxConfig',
],
'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[Any] = ['SqueezeBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : int = [
'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
__snake_case : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 687 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Optional[int] = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class lowerCamelCase ( lowercase_ , lowercase_ ):
'''simple docstring'''
__snake_case = 'bit'
__snake_case = ['preactivation', 'bottleneck']
__snake_case = ['SAME', 'VALID']
def __init__( self : List[str] , lowerCAmelCase_ : Any=3 , lowerCAmelCase_ : int=64 , lowerCAmelCase_ : Optional[int]=[2_56, 5_12, 10_24, 20_48] , lowerCAmelCase_ : str=[3, 4, 6, 3] , lowerCAmelCase_ : Optional[Any]="preactivation" , lowerCAmelCase_ : str="relu" , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=32 , lowerCAmelCase_ : Tuple=0.0 , lowerCAmelCase_ : int=False , lowerCAmelCase_ : Optional[Any]=32 , lowerCAmelCase_ : Tuple=1 , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , **lowerCAmelCase_ : int , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**lowerCAmelCase_ )
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
A__ : List[Any] =global_padding.upper()
else:
raise ValueError(f"Padding strategy {global_padding} not supported" )
A__ : List[Any] =num_channels
A__ : Tuple =embedding_size
A__ : Union[str, Any] =hidden_sizes
A__ : List[str] =depths
A__ : Optional[Any] =layer_type
A__ : int =hidden_act
A__ : int =global_padding
A__ : int =num_groups
A__ : str =drop_path_rate
A__ : str =embedding_dynamic_padding
A__ : Dict =output_stride
A__ : Optional[int] =width_factor
A__ : List[str] =["""stem"""] + [f"stage{idx}" for idx in range(1 , len(lowerCAmelCase_ ) + 1 )]
A__ , A__ : Union[str, Any] =get_aligned_output_features_output_indices(
out_features=lowerCAmelCase_ , out_indices=lowerCAmelCase_ , stage_names=self.stage_names )
| 687 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE_: Dict = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE_: Any = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE_: str = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE_: Tuple = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE_: List[str] = [64, 80, 96]
SCREAMING_SNAKE_CASE_: Union[str, Any] = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE_: str = 0.0_5
SCREAMING_SNAKE_CASE_: Any = 2.0
if mobilevit_name.startswith("deeplabv3_" ):
SCREAMING_SNAKE_CASE_: List[str] = 5_12
SCREAMING_SNAKE_CASE_: Optional[Any] = 16
SCREAMING_SNAKE_CASE_: Optional[Any] = 21
SCREAMING_SNAKE_CASE_: List[Any] = "pascal-voc-id2label.json"
else:
SCREAMING_SNAKE_CASE_: Optional[Any] = 10_00
SCREAMING_SNAKE_CASE_: str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE_: Optional[int] = "huggingface/label-files"
SCREAMING_SNAKE_CASE_: Tuple = json.load(open(hf_hub_download(_A , _A , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE_: List[Any] = {int(_A ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_: List[str] = idalabel
SCREAMING_SNAKE_CASE_: Tuple = {v: k for k, v in idalabel.items()}
return config
def A_ ( _UpperCAmelCase , _UpperCAmelCase=False ):
for i in range(1 , 6 ):
if f"layer_{i}." in name:
SCREAMING_SNAKE_CASE_: List[str] = name.replace(f"layer_{i}." , f"encoder.layer.{i - 1}." )
if "conv_1." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace("conv_1." , "conv_stem." )
if ".block." in name:
SCREAMING_SNAKE_CASE_: List[Any] = name.replace(".block." , "." )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE_: int = name.replace("exp_1x1" , "expand_1x1" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE_: Union[str, Any] = name.replace("red_1x1" , "reduce_1x1" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE_: Optional[Any] = name.replace(".local_rep.conv_3x3." , ".conv_kxk." )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE_: Optional[Any] = name.replace(".local_rep.conv_1x1." , ".conv_1x1." )
if ".norm." in name:
SCREAMING_SNAKE_CASE_: Union[str, Any] = name.replace(".norm." , ".normalization." )
if ".conv." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace(".conv." , ".convolution." )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE_: List[str] = name.replace(".conv_proj." , ".conv_projection." )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if f".{i}.{j}." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace(f".{i}.{j}." , f".{i}.layer.{j}." )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if f".{i}.{j}." in name:
SCREAMING_SNAKE_CASE_: Tuple = name.replace(f".{i}.{j}." , f".{i}." )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE_: int = name.replace("expand_1x1" , "downsampling_layer.expand_1x1" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE_: List[str] = name.replace("conv_3x3" , "downsampling_layer.conv_3x3" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE_: Tuple = name.replace("reduce_1x1" , "downsampling_layer.reduce_1x1" )
for i in range(2 , 5 ):
if f".global_rep.{i}.weight" in name:
SCREAMING_SNAKE_CASE_: Optional[Any] = name.replace(f".global_rep.{i}.weight" , ".layernorm.weight" )
if f".global_rep.{i}.bias" in name:
SCREAMING_SNAKE_CASE_: List[str] = name.replace(f".global_rep.{i}.bias" , ".layernorm.bias" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE_: Tuple = name.replace(".global_rep." , ".transformer." )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE_: List[Any] = name.replace(".pre_norm_mha.0." , ".layernorm_before." )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace(".pre_norm_mha.1.out_proj." , ".attention.output.dense." )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace(".pre_norm_ffn.0." , ".layernorm_after." )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE_: str = name.replace(".pre_norm_ffn.1." , ".intermediate.dense." )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE_: str = name.replace(".pre_norm_ffn.4." , ".output.dense." )
if ".transformer." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace(".transformer." , ".transformer.layer." )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE_: str = name.replace(".aspp_layer." , "." )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE_: Union[str, Any] = name.replace(".aspp_pool." , "." )
if "seg_head." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace("seg_head." , "segmentation_head." )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE_: Union[str, Any] = name.replace("segmentation_head.classifier.classifier." , "segmentation_head.classifier." )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE_: Optional[int] = name.replace("classifier.fc." , "classifier." )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE_: List[Any] = "mobilevit." + name
return name
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ):
if base_model:
SCREAMING_SNAKE_CASE_: Optional[Any] = ""
else:
SCREAMING_SNAKE_CASE_: Any = "mobilevit."
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE_: Optional[int] = orig_state_dict.pop(_A )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE_: Optional[int] = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE_: Tuple = key.split("." )
SCREAMING_SNAKE_CASE_: Optional[Any] = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE_: int = int(key_split[3] )
SCREAMING_SNAKE_CASE_: Optional[Any] = model.get_submodule(f"{model_prefix}encoder.layer.{layer_num}" )
SCREAMING_SNAKE_CASE_: Optional[Any] = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE_: int = (
f"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
)
if "weight" in key:
SCREAMING_SNAKE_CASE_: Optional[Any] = val[:dim, :]
SCREAMING_SNAKE_CASE_: str = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE_: Any = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_: Any = val[:dim]
SCREAMING_SNAKE_CASE_: Union[str, Any] = val[dim : dim * 2]
SCREAMING_SNAKE_CASE_: int = val[-dim:]
else:
SCREAMING_SNAKE_CASE_: Tuple = val
return orig_state_dict
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE_: str = Image.open(requests.get(_A , stream=_A ).raw )
return im
@torch.no_grad()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ):
SCREAMING_SNAKE_CASE_: List[Any] = get_mobilevit_config(_A )
# load original state_dict
SCREAMING_SNAKE_CASE_: List[Any] = torch.load(_A , map_location="cpu" )
# load 🤗 model
if mobilevit_name.startswith("deeplabv3_" ):
SCREAMING_SNAKE_CASE_: Dict = MobileViTForSemanticSegmentation(_A ).eval()
else:
SCREAMING_SNAKE_CASE_: Any = MobileViTForImageClassification(_A ).eval()
SCREAMING_SNAKE_CASE_: Union[str, Any] = convert_state_dict(_A , _A )
model.load_state_dict(_A )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE_: Optional[int] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE_: Dict = image_processor(images=prepare_img() , return_tensors="pt" )
SCREAMING_SNAKE_CASE_: Tuple = model(**_A )
SCREAMING_SNAKE_CASE_: Optional[int] = outputs.logits
if mobilevit_name.startswith("deeplabv3_" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE_: str = torch.tensor(
[
[[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
[[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
[[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE_: Optional[Any] = torch.tensor(
[
[[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
[[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
[[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE_: Tuple = torch.tensor(
[
[[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
[[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
[[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
] )
else:
raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3, :3, :3] , _A , atol=1e-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE_: Tuple = torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE_: List[Any] = torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE_: Union[str, Any] = torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] )
else:
raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3] , _A , atol=1e-4 )
Path(_A ).mkdir(exist_ok=_A )
print(f"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_A )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_A )
if push_to_hub:
SCREAMING_SNAKE_CASE_: Dict = {
"mobilevit_s": "mobilevit-small",
"mobilevit_xs": "mobilevit-x-small",
"mobilevit_xxs": "mobilevit-xx-small",
"deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
"deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
"deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
}
print("Pushing to the hub..." )
SCREAMING_SNAKE_CASE_: Any = model_mapping[mobilevit_name]
image_processor.push_to_hub(_A , organization="apple" )
model.push_to_hub(_A , organization="apple" )
if __name__ == "__main__":
lowerCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, 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 : List[str] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 671 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowercase__ : Union[str, Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = ["DPTFeatureExtractor"]
lowercase__ : Optional[Any] = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
"DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DPTForDepthEstimation",
"DPTForSemanticSegmentation",
"DPTModel",
"DPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
lowercase__ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 376 | 0 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : int = np.inf
def set_batch_size(_UpperCAmelCase ) -> None:
nonlocal batch_size
if isinstance(_UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : Tuple = min(_UpperCAmelCase, config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : Dict = min(_UpperCAmelCase, config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_UpperCAmelCase, _UpperCAmelCase ) and feature.dtype == "binary":
__UpperCAmelCase : Optional[int] = min(_UpperCAmelCase, config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_UpperCAmelCase, _UpperCAmelCase )
return None if batch_size is np.inf else batch_size
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCAmelCase_ : NestedDataStructureLike[PathLike] , UpperCAmelCase_ : Optional[NamedSplit] = None , UpperCAmelCase_ : Optional[Features] = None , UpperCAmelCase_ : str = None , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[int] = None , **UpperCAmelCase_ : Optional[int] , ):
"""simple docstring"""
super().__init__(
UpperCAmelCase_ , split=UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , keep_in_memory=UpperCAmelCase_ , streaming=UpperCAmelCase_ , num_proc=UpperCAmelCase_ , **UpperCAmelCase_ , )
__UpperCAmelCase : Tuple = path_or_paths if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else {self.split: path_or_paths}
__UpperCAmelCase : Optional[int] = _PACKAGED_DATASETS_MODULES["parquet"][1]
__UpperCAmelCase : int = Parquet(
cache_dir=UpperCAmelCase_ , data_files=UpperCAmelCase_ , features=UpperCAmelCase_ , hash=UpperCAmelCase_ , **UpperCAmelCase_ , )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
# Build iterable dataset
if self.streaming:
__UpperCAmelCase : int = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__UpperCAmelCase : int = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : str = None
__UpperCAmelCase : Dict = None
self.builder.download_and_prepare(
download_config=UpperCAmelCase_ , download_mode=UpperCAmelCase_ , verification_mode=UpperCAmelCase_ , base_path=UpperCAmelCase_ , num_proc=self.num_proc , )
__UpperCAmelCase : Tuple = self.builder.as_dataset(
split=self.split , verification_mode=UpperCAmelCase_ , in_memory=self.keep_in_memory )
return dataset
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Dataset , UpperCAmelCase_ : Union[PathLike, BinaryIO] , UpperCAmelCase_ : Optional[int] = None , **UpperCAmelCase_ : List[str] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = dataset
__UpperCAmelCase : Union[str, Any] = path_or_buf
__UpperCAmelCase : List[Any] = batch_size or get_writer_batch_size(dataset.features )
__UpperCAmelCase : List[Any] = parquet_writer_kwargs
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
__UpperCAmelCase : List[str] = self._write(file_obj=UpperCAmelCase_ , batch_size=UpperCAmelCase_ , **self.parquet_writer_kwargs )
else:
__UpperCAmelCase : Any = self._write(file_obj=self.path_or_buf , batch_size=UpperCAmelCase_ , **self.parquet_writer_kwargs )
return written
def lowerCamelCase_ ( self : str , UpperCAmelCase_ : BinaryIO , UpperCAmelCase_ : int , **UpperCAmelCase_ : List[str] ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : Union[str, Any] = parquet_writer_kwargs.pop("path_or_buf" , UpperCAmelCase_ )
__UpperCAmelCase : Dict = self.dataset.features.arrow_schema
__UpperCAmelCase : Optional[int] = pq.ParquetWriter(UpperCAmelCase_ , schema=UpperCAmelCase_ , **UpperCAmelCase_ )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , UpperCAmelCase_ ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
__UpperCAmelCase : Dict = query_table(
table=self.dataset._data , key=slice(UpperCAmelCase_ , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(UpperCAmelCase_ )
written += batch.nbytes
writer.close()
return written
| 709 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCAmelCase__ : int = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCAmelCase_ : int ):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Optional[Any] = torchvision.models.resnetaaa(pretrained=UpperCAmelCase_ )
__UpperCAmelCase : List[str] = list(model.children() )[:-2]
__UpperCAmelCase : int = nn.Sequential(*UpperCAmelCase_ )
__UpperCAmelCase : List[Any] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase_ : int ):
"""simple docstring"""
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
__UpperCAmelCase : Tuple = self.pool(self.model(UpperCAmelCase_ ) )
__UpperCAmelCase : Dict = torch.flatten(UpperCAmelCase_ , start_dim=2 )
__UpperCAmelCase : int = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase_ : Any , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase : List[str] = [json.loads(UpperCAmelCase_ ) for l in open(UpperCAmelCase_ )]
__UpperCAmelCase : List[Any] = os.path.dirname(UpperCAmelCase_ )
__UpperCAmelCase : Any = tokenizer
__UpperCAmelCase : Union[str, Any] = labels
__UpperCAmelCase : Any = len(UpperCAmelCase_ )
__UpperCAmelCase : Tuple = max_seq_length
__UpperCAmelCase : Any = transforms
def __len__( self : List[str] ):
"""simple docstring"""
return len(self.data )
def __getitem__( self : Tuple , UpperCAmelCase_ : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : Dict = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] , add_special_tokens=UpperCAmelCase_ ) )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[str] = sentence[0], sentence[1:-1], sentence[-1]
__UpperCAmelCase : Tuple = sentence[: self.max_seq_length]
__UpperCAmelCase : int = torch.zeros(self.n_classes )
__UpperCAmelCase : str = 1
__UpperCAmelCase : int = Image.open(os.path.join(self.data_dir , self.data[index]["img"] ) ).convert("RGB" )
__UpperCAmelCase : Dict = self.transforms(UpperCAmelCase_ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def __UpperCamelCase ( _UpperCAmelCase ):
__UpperCAmelCase : str = [len(row["sentence"] ) for row in batch]
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = len(_UpperCAmelCase ), max(_UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = torch.zeros(_UpperCAmelCase, _UpperCAmelCase, dtype=torch.long )
__UpperCAmelCase : List[Any] = torch.zeros(_UpperCAmelCase, _UpperCAmelCase, dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_UpperCAmelCase, _UpperCAmelCase ) ):
__UpperCAmelCase : str = input_row["sentence"]
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Dict = torch.stack([row["image"] for row in batch] )
__UpperCAmelCase : List[Any] = torch.stack([row["label"] for row in batch] )
__UpperCAmelCase : Tuple = torch.stack([row["image_start_token"] for row in batch] )
__UpperCAmelCase : Optional[Any] = torch.stack([row["image_end_token"] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCamelCase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def __UpperCamelCase ( ):
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46_777_044, 0.44_531_429, 0.40_661_017], std=[0.12_221_994, 0.12_145_835, 0.14_380_469], ),
] )
| 329 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class a__ ( a__ ):
'''simple docstring'''
def __init__( self , lowerCamelCase_=0.01 , lowerCamelCase_=10_00 ) -> Union[str, Any]:
lowerCAmelCase__ = p_stop
lowerCAmelCase__ = max_length
def __iter__( self ) -> Any:
lowerCAmelCase__ = 0
lowerCAmelCase__ = False
while not stop and count < self.max_length:
yield count
count += 1
lowerCAmelCase__ = random.random() < self.p_stop
class a__ ( unittest.TestCase ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False , lowerCamelCase_=True ) -> Optional[Any]:
lowerCAmelCase__ = [
BatchSamplerShard(lowerCamelCase_ , 2 , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
for i in range(2 )
]
lowerCAmelCase__ = [list(lowerCamelCase_ ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(lowerCamelCase_ ) for shard in batch_sampler_shards] , [len(lowerCamelCase_ ) for e in expected] )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
# Check the shards when the dataset is a round multiple of total batch size.
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCamelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
lowerCAmelCase__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
# Check the shards when the dataset is very small.
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[], []]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
# Check the shards when the dataset is a round multiple of batch size.
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCamelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size.
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
# Check the shards when the dataset is very small.
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[], []]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCamelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
lowerCAmelCase__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is very small.
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[[0, 1]], []]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[], []]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , even_batches=lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
# Check the shards when the dataset is a round multiple of batch size.
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCamelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size.
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
# Check the shards when the dataset is very small.
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[[0, 1]], []]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
lowerCAmelCase__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = [[], []]
self.check_batch_sampler_shards(lowerCamelCase_ , lowerCamelCase_ , split_batches=lowerCamelCase_ , even_batches=lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
lowerCAmelCase__ = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
lowerCAmelCase__ = [BatchSamplerShard(lowerCamelCase_ , 2 , lowerCamelCase_ , even_batches=lowerCamelCase_ ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=False , lowerCamelCase_=2 , lowerCamelCase_=False ) -> str:
random.seed(lowerCamelCase_ )
lowerCAmelCase__ = list(lowerCamelCase_ )
lowerCAmelCase__ = [
IterableDatasetShard(
lowerCamelCase_ , batch_size=lowerCamelCase_ , drop_last=lowerCamelCase_ , num_processes=lowerCamelCase_ , process_index=lowerCamelCase_ , split_batches=lowerCamelCase_ , )
for i in range(lowerCamelCase_ )
]
lowerCAmelCase__ = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(lowerCamelCase_ )
iterable_dataset_lists.append(list(lowerCamelCase_ ) )
lowerCAmelCase__ = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
lowerCAmelCase__ = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) )
self.assertTrue(len(lowerCamelCase_ ) % shard_batch_size == 0 )
lowerCAmelCase__ = []
for idx in range(0 , len(lowerCamelCase_ ) , lowerCamelCase_ ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(lowerCamelCase_ ) < len(lowerCamelCase_ ):
reference += reference
self.assertListEqual(lowerCamelCase_ , reference[: len(lowerCamelCase_ )] )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
lowerCAmelCase__ = 42
lowerCAmelCase__ = RandomIterableDataset()
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
# Edge case with a very small dataset
lowerCAmelCase__ = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
self.check_iterable_dataset_shards(lowerCamelCase_ , lowerCamelCase_ , batch_size=4 , drop_last=lowerCamelCase_ , split_batches=lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
lowerCAmelCase__ = BatchSampler(range(16 ) , batch_size=4 , drop_last=lowerCamelCase_ )
lowerCAmelCase__ = SkipBatchSampler(lowerCamelCase_ , 2 )
self.assertListEqual(list(lowerCamelCase_ ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
lowerCAmelCase__ = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
lowerCAmelCase__ = DataLoader(list(range(16 ) ) , batch_size=4 )
lowerCAmelCase__ = skip_first_batches(lowerCamelCase_ , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
lowerCAmelCase__ = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(lowerCamelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowerCamelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
Accelerator()
lowerCAmelCase__ = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(lowerCamelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowerCamelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 ) | 90 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = str(A )
return len(A ) == 9 and set(A ) == set('''123456789''' )
def _snake_case ( ) -> int | None:
for base_num in range(9999 , 4999 , -1 ):
lowerCAmelCase__ = 100002 * base_num
if is_9_pandigital(A ):
return candidate
for base_num in range(333 , 99 , -1 ):
lowerCAmelCase__ = 1002003 * base_num
if is_9_pandigital(A ):
return candidate
return None
if __name__ == "__main__":
print(f"""{solution() = }""") | 90 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _a ( UpperCamelCase_ : Dict , UpperCamelCase_ : Dict , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[str]=True , UpperCamelCase_ : List[str]="pt" ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ = {"add_prefix_space": True} if isinstance(UpperCamelCase_ , UpperCamelCase_ ) and not line.startswith(" " ) else {}
lowerCAmelCase__ = padding_side
return tokenizer(
[line] , max_length=UpperCamelCase_ , padding="max_length" if pad_to_max_length else None , truncation=UpperCamelCase_ , return_tensors=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
def _a ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Dict=None , ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ = input_ids.ne(UpperCamelCase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class lowercase__ ( _UpperCAmelCase ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase="train" , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="" , )-> Optional[Any]:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = Path(__UpperCAmelCase ).joinpath(type_path + ".source" )
lowerCAmelCase__ = Path(__UpperCAmelCase ).joinpath(type_path + ".target" )
lowerCAmelCase__ = self.get_char_lens(self.src_file )
lowerCAmelCase__ = max_source_length
lowerCAmelCase__ = max_target_length
assert min(self.src_lens ) > 0, F"found empty line in {self.src_file}"
lowerCAmelCase__ = tokenizer
lowerCAmelCase__ = prefix
if n_obs is not None:
lowerCAmelCase__ = self.src_lens[:n_obs]
lowerCAmelCase__ = src_lang
lowerCAmelCase__ = tgt_lang
def __len__( self )-> List[Any]:
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self , __UpperCAmelCase )-> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase__ = index + 1 # linecache starts at 1
lowerCAmelCase__ = self.prefix + linecache.getline(str(self.src_file ) , __UpperCAmelCase ).rstrip("\n" )
lowerCAmelCase__ = linecache.getline(str(self.tgt_file ) , __UpperCAmelCase ).rstrip("\n" )
assert source_line, F"empty source line for index {index}"
assert tgt_line, F"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer , __UpperCAmelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
lowerCAmelCase__ = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , __UpperCAmelCase ) else self.tokenizer
)
lowerCAmelCase__ = self.tokenizer.generator if isinstance(self.tokenizer , __UpperCAmelCase ) else self.tokenizer
lowerCAmelCase__ = encode_line(__UpperCAmelCase , __UpperCAmelCase , self.max_source_length , "right" )
lowerCAmelCase__ = encode_line(__UpperCAmelCase , __UpperCAmelCase , self.max_target_length , "right" )
lowerCAmelCase__ = source_inputs["input_ids"].squeeze()
lowerCAmelCase__ = target_inputs["input_ids"].squeeze()
lowerCAmelCase__ = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def UpperCAmelCase ( __UpperCAmelCase )-> Optional[Any]:
'''simple docstring'''
return [len(__UpperCAmelCase ) for x in Path(__UpperCAmelCase ).open().readlines()]
def UpperCAmelCase ( self , __UpperCAmelCase )-> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase__ = torch.stack([x["input_ids"] for x in batch] )
lowerCAmelCase__ = torch.stack([x["attention_mask"] for x in batch] )
lowerCAmelCase__ = torch.stack([x["decoder_input_ids"] for x in batch] )
lowerCAmelCase__ = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , __UpperCAmelCase )
else self.tokenizer.pad_token_id
)
lowerCAmelCase__ = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , __UpperCAmelCase )
else self.tokenizer.pad_token_id
)
lowerCAmelCase__ = trim_batch(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ = trim_batch(__UpperCAmelCase , __UpperCAmelCase , attention_mask=__UpperCAmelCase )
lowerCAmelCase__ = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
a_ = getLogger(__name__)
def _a ( UpperCamelCase_ : List[List] ) -> List[str]:
"""simple docstring"""
return list(itertools.chain.from_iterable(UpperCamelCase_ ) )
def _a ( UpperCamelCase_ : str ) -> None:
"""simple docstring"""
lowerCAmelCase__ = get_git_info()
save_json(UpperCamelCase_ , os.path.join(UpperCamelCase_ , "git_log.json" ) )
def _a ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Dict=4 , **UpperCamelCase_ : Tuple ) -> int:
"""simple docstring"""
with open(UpperCamelCase_ , "w" ) as f:
json.dump(UpperCamelCase_ , UpperCamelCase_ , indent=UpperCamelCase_ , **UpperCamelCase_ )
def _a ( UpperCamelCase_ : Optional[Any] ) -> Tuple:
"""simple docstring"""
with open(UpperCamelCase_ ) as f:
return json.load(UpperCamelCase_ )
def _a ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = git.Repo(search_parent_directories=UpperCamelCase_ )
lowerCAmelCase__ = {
"repo_id": str(UpperCamelCase_ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def _a ( UpperCamelCase_ : Callable , UpperCamelCase_ : Iterable ) -> List:
"""simple docstring"""
return list(map(UpperCamelCase_ , UpperCamelCase_ ) )
def _a ( UpperCamelCase_ : List[str] , UpperCamelCase_ : List[Any] ) -> Optional[int]:
"""simple docstring"""
with open(UpperCamelCase_ , "wb" ) as f:
return pickle.dump(UpperCamelCase_ , UpperCamelCase_ )
def _a ( UpperCamelCase_ : Dict ) -> Tuple:
"""simple docstring"""
def remove_articles(UpperCamelCase_ : List[str] ):
return re.sub(R"\b(a|an|the)\b" , " " , UpperCamelCase_ )
def white_space_fix(UpperCamelCase_ : Dict ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase_ : Union[str, Any] ):
lowerCAmelCase__ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase_ : Optional[int] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_ ) ) ) )
def _a ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = normalize_answer(UpperCamelCase_ ).split()
lowerCAmelCase__ = normalize_answer(UpperCamelCase_ ).split()
lowerCAmelCase__ = Counter(UpperCamelCase_ ) & Counter(UpperCamelCase_ )
lowerCAmelCase__ = sum(common.values() )
if num_same == 0:
return 0
lowerCAmelCase__ = 1.0 * num_same / len(UpperCamelCase_ )
lowerCAmelCase__ = 1.0 * num_same / len(UpperCamelCase_ )
lowerCAmelCase__ = (2 * precision * recall) / (precision + recall)
return fa
def _a ( UpperCamelCase_ : Tuple , UpperCamelCase_ : int ) -> List[str]:
"""simple docstring"""
return normalize_answer(UpperCamelCase_ ) == normalize_answer(UpperCamelCase_ )
def _a ( UpperCamelCase_ : List[str] , UpperCamelCase_ : List[str] ) -> Dict:
"""simple docstring"""
assert len(UpperCamelCase_ ) == len(UpperCamelCase_ )
lowerCAmelCase__ = 0
for hypo, pred in zip(UpperCamelCase_ , UpperCamelCase_ ):
em += exact_match_score(UpperCamelCase_ , UpperCamelCase_ )
if len(UpperCamelCase_ ) > 0:
em /= len(UpperCamelCase_ )
return {"em": em}
def _a ( UpperCamelCase_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return model_prefix.startswith("rag" )
def _a ( UpperCamelCase_ : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
lowerCAmelCase__ = "dropout_rate"
for p in extra_params:
if getattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
if not hasattr(UpperCamelCase_ , UpperCamelCase_ ) and not hasattr(UpperCamelCase_ , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(UpperCamelCase_ ) )
delattr(UpperCamelCase_ , UpperCamelCase_ )
continue
lowerCAmelCase__ = p if hasattr(UpperCamelCase_ , UpperCamelCase_ ) else equivalent_param[p]
setattr(UpperCamelCase_ , UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
delattr(UpperCamelCase_ , UpperCamelCase_ )
return hparams, config
| 115 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
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 transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class lowercase__ ( _UpperCAmelCase ):
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "num_attention_heads" ) )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "num_encoder_blocks" ) )
class lowercase__ :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=64 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=[2, 2, 2, 2] , __UpperCAmelCase=[8, 4, 2, 1] , __UpperCAmelCase=[16, 32, 64, 128] , __UpperCAmelCase=[1, 4, 8, 16] , __UpperCAmelCase=[1, 2, 4, 8] , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=None , )-> str:
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = num_encoder_blocks
lowerCAmelCase__ = sr_ratios
lowerCAmelCase__ = depths
lowerCAmelCase__ = hidden_sizes
lowerCAmelCase__ = downsampling_rates
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = scope
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = SegformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = lowerCAmelCase__ = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = SegformerForSemanticSegmentation(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
lowerCAmelCase__ = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = 1
lowerCAmelCase__ = SegformerForSemanticSegmentation(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(__UpperCAmelCase )
lowerCAmelCase__ = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase ):
a_ =(
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
a_ =(
{
"""feature-extraction""": SegformerModel,
"""image-classification""": SegformerForImageClassification,
"""image-segmentation""": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
a_ =True
a_ =False
a_ =False
a_ =False
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = SegformerModelTester(self )
lowerCAmelCase__ = SegformerConfigTester(self , config_class=__UpperCAmelCase )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase )
@unittest.skip("SegFormer does not use inputs_embeds" )
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
pass
@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods" )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__UpperCAmelCase )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ = outputs.attentions
lowerCAmelCase__ = sum(self.model_tester.depths )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first attentions (first block, first layer)
lowerCAmelCase__ = (self.model_tester.image_size // 4) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
lowerCAmelCase__ = (self.model_tester.image_size // 32) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
lowerCAmelCase__ = len(__UpperCAmelCase )
# Check attention is always last and order is fine
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
lowerCAmelCase__ = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first attentions (first block, first layer)
lowerCAmelCase__ = (self.model_tester.image_size // 4) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ = outputs.hidden_states
lowerCAmelCase__ = self.model_tester.num_encoder_blocks
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(__UpperCAmelCase ):
continue
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.train()
lowerCAmelCase__ = self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase , return_labels=__UpperCAmelCase )
lowerCAmelCase__ = model(**__UpperCAmelCase ).loss
loss.backward()
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self )-> Union[str, Any]:
'''simple docstring'''
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = SegformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class lowercase__ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=__UpperCAmelCase , align=__UpperCAmelCase , do_random_crop=__UpperCAmelCase )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
__UpperCAmelCase )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__UpperCAmelCase , return_tensors="pt" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(__UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
lowerCAmelCase__ = torch.tensor(
[
[[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
[[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
[[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=__UpperCAmelCase , align=__UpperCAmelCase , do_random_crop=__UpperCAmelCase )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ).to(__UpperCAmelCase )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__UpperCAmelCase , return_tensors="pt" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(__UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
lowerCAmelCase__ = torch.tensor(
[
[[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
[[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
[[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , __UpperCAmelCase , atol=1E-1 ) )
@slow
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=__UpperCAmelCase , align=__UpperCAmelCase , do_random_crop=__UpperCAmelCase )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
__UpperCAmelCase )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__UpperCAmelCase , return_tensors="pt" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(__UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = outputs.logits.detach().cpu()
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase , target_sizes=[(500, 300)] )
lowerCAmelCase__ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __UpperCAmelCase )
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase )
lowerCAmelCase__ = torch.Size((128, 128) )
self.assertEqual(segmentation[0].shape , __UpperCAmelCase )
| 115 | 1 |
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 a_ (__A ) -> Dict:
"""simple docstring"""
__a : Union[str, Any] = fname.split(os.path.sep )[-1]
return re.search(r"^(.*)_\d+\.jpg$" , __A ).groups()[0]
class snake_case_ ( __UpperCamelCase ):
"""simple docstring"""
def __init__(self: str , __UpperCAmelCase: int , __UpperCAmelCase: List[Any]=None , __UpperCAmelCase: List[str]=None ) -> Any:
'''simple docstring'''
__a : Optional[Any] = file_names
__a : Tuple = image_transform
__a : List[str] = label_to_id
def __len__(self: Optional[Any] ) -> List[str]:
'''simple docstring'''
return len(self.file_names )
def __getitem__(self: Optional[Any] , __UpperCAmelCase: List[Any] ) -> Any:
'''simple docstring'''
__a : List[Any] = self.file_names[idx]
__a : List[str] = PIL.Image.open(__UpperCAmelCase )
__a : List[Any] = raw_image.convert("RGB" )
if self.image_transform is not None:
__a : str = self.image_transform(__UpperCAmelCase )
__a : List[str] = extract_label(__UpperCAmelCase )
if self.label_to_id is not None:
__a : str = self.label_to_id[label]
return {"image": image, "label": label}
def a_ (__A , __A ) -> Optional[Any]:
"""simple docstring"""
# Initialize accelerator
if args.with_tracking:
__a : int = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
__a : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__a : Union[str, Any] = config["lr"]
__a : List[str] = int(config["num_epochs"] )
__a : Any = int(config["seed"] )
__a : Union[str, Any] = int(config["batch_size"] )
__a : Tuple = config["image_size"]
if not isinstance(__A , (list, tuple) ):
__a : Tuple = (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":
__a : List[str] = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
__a : Optional[int] = int(args.checkpointing_steps )
else:
raise ValueError(
f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
else:
__a : List[str] = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
__a : int = os.path.split(__A )[-1].split("." )[0]
accelerator.init_trackers(__A , __A )
# Grab all the image filenames
__a : Tuple = [os.path.join(args.data_dir , __A ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]
# Build the label correspondences
__a : Dict = [extract_label(__A ) for fname in file_names]
__a : Optional[int] = list(set(__A ) )
id_to_label.sort()
__a : Tuple = {lbl: i for i, lbl in enumerate(__A )}
# Set the seed before splitting the data.
np.random.seed(__A )
torch.manual_seed(__A )
torch.cuda.manual_seed_all(__A )
# Split our filenames between train and validation
__a : str = np.random.permutation(len(__A ) )
__a : Any = int(0.8 * len(__A ) )
__a : Optional[int] = random_perm[:cut]
__a : str = random_perm[cut:]
# For training we use a simple RandomResizedCrop
__a : Dict = Compose([RandomResizedCrop(__A , scale=(0.5, 1.0) ), ToTensor()] )
__a : Tuple = PetsDataset(
[file_names[i] for i in train_split] , image_transform=__A , label_to_id=__A )
# For evaluation, we use a deterministic Resize
__a : str = Compose([Resize(__A ), ToTensor()] )
__a : List[str] = PetsDataset([file_names[i] for i in eval_split] , image_transform=__A , label_to_id=__A )
# Instantiate dataloaders.
__a : Any = DataLoader(__A , shuffle=__A , batch_size=__A , num_workers=4 )
__a : Dict = DataLoader(__A , shuffle=__A , batch_size=__A , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__a : Optional[int] = create_model("resnet50d" , pretrained=__A , num_classes=len(__A ) )
# 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 : str = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
__a : Tuple = False
for param in model.get_classifier().parameters():
__a : int = True
# We normalize the batches of images to be a bit faster.
__a : Dict = torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
__a : str = torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
__a : Dict = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
__a : Optional[int] = OneCycleLR(optimizer=__A , max_lr=__A , epochs=__A , steps_per_epoch=len(__A ) )
# 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 : List[str] = accelerator.prepare(
__A , __A , __A , __A , __A )
# We need to keep track of how many total steps we have iterated over
__a : Optional[int] = 0
# We also need to keep track of the starting epoch so files are named properly
__a : int = 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 )
__a : Any = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
__a : Optional[int] = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
__a : Optional[Any] = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
__a : Optional[int] = os.path.splitext(__A )[0]
if "epoch" in training_difference:
__a : List[str] = int(training_difference.replace("epoch_" , "" ) ) + 1
__a : Dict = None
else:
__a : List[str] = int(training_difference.replace("step_" , "" ) )
__a : Any = resume_step // len(__A )
resume_step -= starting_epoch * len(__A )
# Now we train the model
for epoch in range(__A , __A ):
model.train()
if args.with_tracking:
__a : List[str] = 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
__a : Tuple = accelerator.skip_first_batches(__A , __A )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
__a : Any = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
__a : int = {k: v.to(accelerator.device ) for k, v in batch.items()}
__a : str = (batch["image"] - mean) / std
__a : int = model(__A )
__a : Optional[int] = torch.nn.functional.cross_entropy(__A , batch["label"] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(__A )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(__A , __A ):
__a : Union[str, Any] = f'step_{overall_step}'
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
__a : Dict = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
model.eval()
__a : int = 0
__a : Optional[int] = 0
for step, batch in enumerate(__A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
__a : Tuple = {k: v.to(accelerator.device ) for k, v in batch.items()}
__a : str = (batch["image"] - mean) / std
with torch.no_grad():
__a : List[Any] = model(__A )
__a : Optional[int] = outputs.argmax(dim=-1 )
__a , __a : List[Any] = accelerator.gather_for_metrics((predictions, batch["label"]) )
__a : int = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
__a : str = 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(__A ),
"epoch": epoch,
} , step=__A , )
if checkpointing_steps == "epoch":
__a : Dict = f'epoch_{epoch}'
if args.output_dir is not None:
__a : List[str] = os.path.join(args.output_dir , __A )
accelerator.save_state(__A )
if args.with_tracking:
accelerator.end_training()
def a_ () -> List[Any]:
"""simple docstring"""
__a : int = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument("--data_dir" , required=__A , 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=__A , default=__A , 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=__A , default=__A , 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=__A , 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=__A , default=__A , 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=__A , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
__a : str = parser.parse_args()
__a : int = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
training_function(__A , __A )
if __name__ == "__main__":
main()
| 351 |
UpperCAmelCase__ = '''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def a_ (__A ) -> bool:
"""simple docstring"""
if not isinstance(__A , __A ):
__a : Any = f'Expected string as input, found {type(__A ).__name__}'
raise TypeError(__A )
__a : int = spanish_id.replace("-" , "" ).upper()
if len(__A ) != 9:
raise ValueError(__A )
try:
__a : Tuple = int(spanish_id_clean[0:8] )
__a : Optional[int] = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__A ) from ex
if letter.isdigit():
raise ValueError(__A )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
__UpperCamelCase : Union[str, Any] = str(bin(snake_case__ ) )
binary_number += "0" * shift_amount
return binary_number
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
__UpperCamelCase : Any = str(bin(snake_case__ ) )[2:]
if shift_amount >= len(snake_case__ ):
return "0b0"
__UpperCamelCase : Tuple = binary_number[: len(snake_case__ ) - shift_amount]
return "0b" + shifted_binary_number
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
if number >= 0: # Get binary representation of positive number
__UpperCamelCase : int = "0" + str(bin(snake_case__ ) ).strip("-" )[2:]
else: # Get binary (2's complement) representation of negative number
__UpperCamelCase : Tuple = len(bin(snake_case__ )[3:] ) # Find 2's complement of number
__UpperCamelCase : Optional[Any] = bin(abs(snake_case__ ) - (1 << binary_number_length) )[3:]
__UpperCamelCase : List[str] = (
"1" + "0" * (binary_number_length - len(snake_case__ )) + binary_number
)
if shift_amount >= len(snake_case__ ):
return "0b" + binary_number[0] * len(snake_case__ )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(snake_case__ ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 399 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class A ( unittest.TestCase , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def a_ (self ) -> List[Any]:
__UpperCamelCase : str = load_tool("text-to-speech" )
self.tool.setup()
def a_ (self ) -> Optional[int]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__UpperCamelCase : List[str] = self.tool("hey" )
__UpperCamelCase : Tuple = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485] ) , ) )
def a_ (self ) -> str:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__UpperCamelCase : str = self.tool("hey" )
__UpperCamelCase : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485] ) , ) )
| 399 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = ['''note_seq''']
def __init__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
requires_backends(self , ["""note_seq"""] )
@classmethod
def __UpperCamelCase ( cls , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
requires_backends(cls , ["""note_seq"""] )
@classmethod
def __UpperCamelCase ( cls , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
requires_backends(cls , ["""note_seq"""] )
| 38 |
'''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
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." , _lowerCAmelCase , ) | 433 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=lowercase__ ):
a : Union[str, Any] =["""speech"""]
def __init__( self , *snake_case_ , **snake_case_ ) -> str:
requires_backends(self , ['speech'] )
class __lowerCamelCase ( metaclass=lowercase__ ):
a : Optional[Any] =["""speech"""]
def __init__( self , *snake_case_ , **snake_case_ ) -> int:
requires_backends(self , ['speech'] )
| 714 |
"""simple docstring"""
import sys
from collections import defaultdict
class __lowerCamelCase :
def __init__( self ) -> Tuple:
UpperCamelCase__ = []
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ ) -> List[str]:
return self.node_position[vertex]
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ ) -> Optional[int]:
UpperCamelCase__ = pos
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) -> List[Any]:
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
UpperCamelCase__ = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
UpperCamelCase__ = 2 * start + 1
else:
UpperCamelCase__ = 2 * start + 2
if heap[smallest_child] < heap[start]:
UpperCamelCase__ , UpperCamelCase__ = heap[smallest_child], positions[smallest_child]
UpperCamelCase__ , UpperCamelCase__ = (
heap[start],
positions[start],
)
UpperCamelCase__ , UpperCamelCase__ = temp, tempa
UpperCamelCase__ = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , snake_case_ )
self.top_to_bottom(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) -> Any:
UpperCamelCase__ = position[index]
while index != 0:
UpperCamelCase__ = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
UpperCamelCase__ = heap[parent]
UpperCamelCase__ = position[parent]
self.set_position(position[parent] , snake_case_ )
else:
UpperCamelCase__ = val
UpperCamelCase__ = temp
self.set_position(snake_case_ , snake_case_ )
break
UpperCamelCase__ = parent
else:
UpperCamelCase__ = val
UpperCamelCase__ = temp
self.set_position(snake_case_ , 0 )
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ ) -> Any:
UpperCamelCase__ = len(snake_case_ ) // 2 - 1
for i in range(snake_case_ , -1 , -1 ):
self.top_to_bottom(snake_case_ , snake_case_ , len(snake_case_ ) , snake_case_ )
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ ) -> List[Any]:
UpperCamelCase__ = positions[0]
UpperCamelCase__ = sys.maxsize
self.top_to_bottom(snake_case_ , 0 , len(snake_case_ ) , snake_case_ )
return temp
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ = Heap()
UpperCamelCase__ = [0] * len(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = [-1] * len(SCREAMING_SNAKE_CASE ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
UpperCamelCase__ = [] # Heap of Distance of vertices from their neighboring vertex
UpperCamelCase__ = []
for vertex in range(len(SCREAMING_SNAKE_CASE ) ):
distance_tv.append(sys.maxsize )
positions.append(SCREAMING_SNAKE_CASE )
heap.node_position.append(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = []
UpperCamelCase__ = 1
UpperCamelCase__ = sys.maxsize
for neighbor, distance in adjacency_list[0]:
UpperCamelCase__ = 0
UpperCamelCase__ = distance
heap.heapify(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for _ in range(1 , len(SCREAMING_SNAKE_CASE ) ):
UpperCamelCase__ = heap.delete_minimum(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
UpperCamelCase__ = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(SCREAMING_SNAKE_CASE )]
):
UpperCamelCase__ = distance
heap.bottom_to_top(
SCREAMING_SNAKE_CASE , heap.get_position(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
A__ : Dict= int(input("""Enter number of edges: """).strip())
A__ : Dict= defaultdict(list)
for _ in range(edges_number):
A__ : Dict= [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 20 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = iter(_lowerCAmelCase )
while True:
_UpperCAmelCase = tuple(itertools.islice(_lowerCAmelCase , _lowerCAmelCase ) )
if not chunk:
return
yield chunk
def __lowerCamelCase ( _lowerCAmelCase ) -> Tuple:
_UpperCAmelCase = "".join([c.upper() for c in dirty if c in string.ascii_letters] )
_UpperCAmelCase = ""
if len(_lowerCAmelCase ) < 2:
return dirty
for i in range(len(_lowerCAmelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowerCAmelCase ) & 1:
clean += "X"
return clean
def __lowerCamelCase ( _lowerCAmelCase ) -> List[Any]:
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
_UpperCAmelCase = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_UpperCAmelCase = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowerCAmelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowerCAmelCase )
return table
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_UpperCAmelCase = generate_table(_lowerCAmelCase )
_UpperCAmelCase = prepare_input(_lowerCAmelCase )
_UpperCAmelCase = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowerCAmelCase , 2 ):
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_lowerCAmelCase ) , 5 )
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_lowerCAmelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
_UpperCAmelCase = generate_table(_lowerCAmelCase )
_UpperCAmelCase = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowerCAmelCase , 2 ):
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_lowerCAmelCase ) , 5 )
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_lowerCAmelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 684 | """simple docstring"""
import argparse
from collections import defaultdict
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case, snake_case, snake_case):
__snake_case = f"{file}_{class_name}_{test_name}"
done_test[_id] += 1
with open(snake_case, '''r''') as f:
__snake_case = f.readlines()
__snake_case = f"class {class_name}("
__snake_case = f"{4 * ' '}def {test_name}("
__snake_case = f"{8 * ' '}{correct_line.split()[0]}"
__snake_case = f"{16 * ' '}{correct_line.split()[0]}"
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = 0
__snake_case = 0
__snake_case = []
for line in lines:
if line.startswith(snake_case):
__snake_case = True
elif in_class and line.startswith(snake_case):
__snake_case = True
elif in_class and in_func and (line.startswith(snake_case) or line.startswith(snake_case)):
__snake_case = len(line.split(correct_line.split()[0])[0])
count += 1
if count == done_test[_id]:
__snake_case = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
__snake_case = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f"{spaces * ' '}{correct_line}")
__snake_case = __snake_case = __snake_case = __snake_case = False
else:
new_lines.append(snake_case)
with open(snake_case, '''w''') as f:
for line in new_lines:
f.write(snake_case)
def SCREAMING_SNAKE_CASE ( snake_case, snake_case=None):
if fail is not None:
with open(snake_case, '''r''') as f:
__snake_case = {l.strip() for l in f.readlines()}
else:
__snake_case = None
with open(snake_case, '''r''') as f:
__snake_case = f.readlines()
__snake_case = defaultdict(snake_case)
for line in correct_lines:
__snake_case , __snake_case , __snake_case , __snake_case = line.split(''';''')
if test_failures is None or "::".join([file, class_name, test_name]) in test_failures:
overwrite_file(snake_case, snake_case, snake_case, snake_case, snake_case)
if __name__ == "__main__":
__lowercase : Tuple = argparse.ArgumentParser()
parser.add_argument("--correct_filename", help="filename of tests with expected result")
parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
__lowercase : Union[str, Any] = parser.parse_args()
main(args.correct_filename, args.fail_filename) | 564 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
'tokenization_electra': ['ElectraTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ['ElectraTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'ElectraForCausalLM',
'ElectraForMaskedLM',
'ElectraForMultipleChoice',
'ElectraForPreTraining',
'ElectraForQuestionAnswering',
'ElectraForSequenceClassification',
'ElectraForTokenClassification',
'ElectraModel',
'ElectraPreTrainedModel',
'load_tf_weights_in_electra',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFElectraForMaskedLM',
'TFElectraForMultipleChoice',
'TFElectraForPreTraining',
'TFElectraForQuestionAnswering',
'TFElectraForSequenceClassification',
'TFElectraForTokenClassification',
'TFElectraModel',
'TFElectraPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'FlaxElectraForCausalLM',
'FlaxElectraForMaskedLM',
'FlaxElectraForMultipleChoice',
'FlaxElectraForPreTraining',
'FlaxElectraForQuestionAnswering',
'FlaxElectraForSequenceClassification',
'FlaxElectraForTokenClassification',
'FlaxElectraModel',
'FlaxElectraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 709 |
from __future__ import annotations
from math import gcd
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int = 2 , __UpperCAmelCase: int = 1 , __UpperCAmelCase: int = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('''The input value cannot be less than 2''' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(__UpperCAmelCase: int , __UpperCAmelCase: int , __UpperCAmelCase: int ) -> int:
return (pow(__UpperCAmelCase , 2 ) + step) % modulus
for _ in range(__UpperCAmelCase ):
# These track the position within the cycle detection logic.
UpperCamelCase__ : List[Any] = seed
UpperCamelCase__ : List[str] = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
UpperCamelCase__ : Optional[int] = rand_fn(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCamelCase__ : List[str] = rand_fn(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCamelCase__ : Optional[int] = rand_fn(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
UpperCamelCase__ : Optional[int] = gcd(hare - tortoise , __UpperCAmelCase )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
UpperCamelCase__ : List[str] = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'num',
type=int,
help='The value to find a divisor of',
)
parser.add_argument(
'--attempts',
type=int,
default=3,
help='The number of attempts before giving up',
)
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F'''{args.num} is probably prime''')
else:
UpperCAmelCase_ = args.num // divisor
print(F'''{args.num} = {divisor} * {quotient}''')
| 369 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__lowerCamelCase = 25_60_47
__lowerCamelCase = 25_61_45
@require_sentencepiece
@require_tokenizers
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
_lowerCamelCase = NllbTokenizer
_lowerCamelCase = NllbTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
_lowerCamelCase = {}
def lowerCAmelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
__magic_name__ = NllbTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase__ ( self ):
__magic_name__ = NllbTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_ )
__magic_name__ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCamelCase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__magic_name__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
UpperCamelCase_ , [
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''',
'''.''',
] , )
__magic_name__ = tokenizer.convert_tokens_to_ids(UpperCamelCase_ )
self.assertListEqual(
UpperCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__magic_name__ = tokenizer.convert_ids_to_tokens(UpperCamelCase_ )
self.assertListEqual(
UpperCamelCase_ , [
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 lowerCAmelCase__ ( self ):
__magic_name__ = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__magic_name__ = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
__magic_name__ = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
__magic_name__ = tempfile.mkdtemp()
__magic_name__ = tokenizer_r.save_pretrained(UpperCamelCase_ )
__magic_name__ = tokenizer_p.save_pretrained(UpperCamelCase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__magic_name__ = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_ )
# Checks everything loads correctly in the same way
__magic_name__ = tokenizer_r.from_pretrained(UpperCamelCase_ )
__magic_name__ = tokenizer_p.from_pretrained(UpperCamelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_ ) )
shutil.rmtree(UpperCamelCase_ )
# Save tokenizer rust, legacy_format=True
__magic_name__ = tempfile.mkdtemp()
__magic_name__ = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_ )
__magic_name__ = tokenizer_p.save_pretrained(UpperCamelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_ )
# Checks everything loads correctly in the same way
__magic_name__ = tokenizer_r.from_pretrained(UpperCamelCase_ )
__magic_name__ = tokenizer_p.from_pretrained(UpperCamelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_ ) )
shutil.rmtree(UpperCamelCase_ )
# Save tokenizer rust, legacy_format=False
__magic_name__ = tempfile.mkdtemp()
__magic_name__ = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_ )
__magic_name__ = tokenizer_p.save_pretrained(UpperCamelCase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__magic_name__ = tokenizer_r.from_pretrained(UpperCamelCase_ )
__magic_name__ = tokenizer_p.from_pretrained(UpperCamelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_ ) )
shutil.rmtree(UpperCamelCase_ )
@require_torch
def lowerCAmelCase__ ( self ):
if not self.test_seqaseq:
return
__magic_name__ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Longer text that will definitely require truncation.
__magic_name__ = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
''' will only worsen the violence and misery for millions of people.''',
]
__magic_name__ = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
try:
__magic_name__ = tokenizer.prepare_seqaseq_batch(
src_texts=UpperCamelCase_ , tgt_texts=UpperCamelCase_ , max_length=3 , max_target_length=10 , return_tensors='''pt''' , src_lang='''eng_Latn''' , tgt_lang='''ron_Latn''' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__magic_name__ = tokenizer.prepare_seqaseq_batch(
UpperCamelCase_ , tgt_texts=UpperCamelCase_ , max_length=3 , return_tensors='''pt''' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__magic_name__ = tokenizer.prepare_seqaseq_batch(
src_texts=UpperCamelCase_ , max_length=3 , max_target_length=10 , return_tensors='''pt''' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('''decoder_input_ids''' , UpperCamelCase_ )
@unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.''' )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__magic_name__ = [AddedToken('''<special>''' , lstrip=UpperCamelCase_ )]
__magic_name__ = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ )
__magic_name__ = tokenizer_r.encode('''Hey this is a <special> token''' )
__magic_name__ = tokenizer_r.encode('''<special>''' , add_special_tokens=UpperCamelCase_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__magic_name__ = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__magic_name__ = self.tokenizer_class.from_pretrained(
UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ )
__magic_name__ = tokenizer_p.encode('''Hey this is a <special> token''' )
__magic_name__ = tokenizer_cr.encode('''Hey this is a <special> token''' )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
_lowerCamelCase = '''facebook/nllb-200-distilled-600M'''
_lowerCamelCase = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
_lowerCamelCase = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
_lowerCamelCase = [
256047,
16297,
134408,
8165,
248066,
14734,
950,
1135,
105721,
3573,
83,
27352,
108,
49486,
2,
]
@classmethod
def lowerCAmelCase__ ( cls ):
__magic_name__ = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''eng_Latn''' , tgt_lang='''ron_Latn''' )
__magic_name__ = 1
return cls
def lowerCAmelCase__ ( self ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab'''] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn'''] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn'''] , 25_6057 )
def lowerCAmelCase__ ( self ):
__magic_name__ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
self.assertIn(UpperCamelCase_ , self.tokenizer.all_special_ids )
# fmt: off
__magic_name__ = [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__magic_name__ = self.tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_ )
__magic_name__ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
__magic_name__ = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , UpperCamelCase_ )
__magic_name__ = 10
__magic_name__ = self.tokenizer(UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , UpperCamelCase_ )
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [25_6203, 3] )
def lowerCAmelCase__ ( self ):
__magic_name__ = tempfile.mkdtemp()
__magic_name__ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase_ )
__magic_name__ = NllbTokenizer.from_pretrained(UpperCamelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase_ )
@require_torch
def lowerCAmelCase__ ( self ):
__magic_name__ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
__magic_name__ = shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['''ron_Latn'''] )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__magic_name__ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def lowerCAmelCase__ ( self ):
__magic_name__ = self.tokenizer(self.src_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=3 , return_tensors='''pt''' )
__magic_name__ = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=10 , return_tensors='''pt''' )
__magic_name__ = targets['''input_ids''']
__magic_name__ = shift_tokens_right(
UpperCamelCase_ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowerCAmelCase__ ( self ):
__magic_name__ = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
nested_simplify(UpperCamelCase_ ) , {
# A, test, EOS, en_XX
'''input_ids''': [[25_6047, 70, 7356, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 25_6057,
} , )
@require_torch
def lowerCAmelCase__ ( self ):
__magic_name__ = True
__magic_name__ = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__magic_name__ = False
__magic_name__ = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 490 |
"""simple docstring"""
import math
def lowercase ( __UpperCamelCase = 100 ) -> int:
__magic_name__ = sum(i * i for i in range(1 , n + 1 ) )
__magic_name__ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 490 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : str =logging.get_logger(__name__)
lowerCAmelCase : Any ={
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
),
}
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = 'xlm-roberta'
def __init__( self : List[str] , _UpperCamelCase : Any=3_0522 , _UpperCamelCase : Optional[Any]=768 , _UpperCamelCase : Any=12 , _UpperCamelCase : Optional[int]=12 , _UpperCamelCase : Optional[Any]=3072 , _UpperCamelCase : Any="gelu" , _UpperCamelCase : Tuple=0.1 , _UpperCamelCase : Tuple=0.1 , _UpperCamelCase : Tuple=512 , _UpperCamelCase : str=2 , _UpperCamelCase : Dict=0.0_2 , _UpperCamelCase : List[str]=1E-1_2 , _UpperCamelCase : Optional[int]=1 , _UpperCamelCase : Union[str, Any]=0 , _UpperCamelCase : str=2 , _UpperCamelCase : Tuple="absolute" , _UpperCamelCase : Union[str, Any]=True , _UpperCamelCase : Tuple=None , **_UpperCamelCase : Dict , ) ->List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase)
_lowerCamelCase : Union[str, Any] = vocab_size
_lowerCamelCase : Tuple = hidden_size
_lowerCamelCase : Union[str, Any] = num_hidden_layers
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : List[Any] = intermediate_size
_lowerCamelCase : Optional[Any] = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : Tuple = max_position_embeddings
_lowerCamelCase : Union[str, Any] = type_vocab_size
_lowerCamelCase : Dict = initializer_range
_lowerCamelCase : Any = layer_norm_eps
_lowerCamelCase : Union[str, Any] = position_embedding_type
_lowerCamelCase : Tuple = use_cache
_lowerCamelCase : int = classifier_dropout
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
@property
def _SCREAMING_SNAKE_CASE ( self : List[str]) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_lowerCamelCase : Union[str, Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_lowerCamelCase : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
])
| 15 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple) ->int:
"""simple docstring"""
_lowerCamelCase : Tuple = XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
_lowerCamelCase : Optional[int] = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
# The dog is cute and lives in the garden house
_lowerCamelCase : Optional[Any] = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
_lowerCamelCase : str = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
_lowerCamelCase : List[str] = model(_UpperCamelCase)["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _UpperCamelCase)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _UpperCamelCase , atol=1E-3))
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any]) ->Optional[Any]:
"""simple docstring"""
_lowerCamelCase : List[Any] = XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
_lowerCamelCase : Optional[Any] = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
# The dog is cute and lives in the garden house
_lowerCamelCase : str = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
_lowerCamelCase : Union[str, Any] = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
_lowerCamelCase : int = model(_UpperCamelCase)["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _UpperCamelCase)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _UpperCamelCase , atol=1E-3))
| 15 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'vocab_file': 'spiece.model'}
UpperCamelCase__ = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
UpperCamelCase__ = {
'google/bigbird-roberta-base': 40_96,
'google/bigbird-roberta-large': 40_96,
'google/bigbird-base-trivia-itc': 40_96,
}
class a ( lowercase ):
UpperCamelCase : Dict = VOCAB_FILES_NAMES
UpperCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : List[str] = ["""input_ids""", """attention_mask"""]
UpperCamelCase : List[int] = []
def __init__( self , UpperCamelCase_ , UpperCamelCase_="<unk>" , UpperCamelCase_="<s>" , UpperCamelCase_="</s>" , UpperCamelCase_="<pad>" , UpperCamelCase_="[SEP]" , UpperCamelCase_="[MASK]" , UpperCamelCase_="[CLS]" , UpperCamelCase_ = None , **UpperCamelCase_ , ):
UpperCAmelCase__ : int = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else bos_token
UpperCAmelCase__ : int = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else eos_token
UpperCAmelCase__ : List[Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else unk_token
UpperCAmelCase__ : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else pad_token
UpperCAmelCase__ : Optional[Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else cls_token
UpperCAmelCase__ : str = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase__ : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else mask_token
UpperCAmelCase__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
UpperCAmelCase__ : Any = vocab_file
UpperCAmelCase__ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase_ )
@property
def __snake_case ( self ):
return self.sp_model.get_piece_size()
def __snake_case ( self ):
UpperCAmelCase__ : Optional[Any] = {self.convert_ids_to_tokens(UpperCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
UpperCAmelCase__ : str = self.__dict__.copy()
UpperCAmelCase__ : Dict = None
return state
def __setstate__( self , UpperCamelCase_ ):
UpperCAmelCase__ : List[Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCAmelCase__ : Any = {}
UpperCAmelCase__ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __snake_case ( self , UpperCamelCase_ ):
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_ )
def __snake_case ( self , UpperCamelCase_ ):
return self.sp_model.piece_to_id(UpperCamelCase_ )
def __snake_case ( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = self.sp_model.IdToPiece(UpperCamelCase_ )
return token
def __snake_case ( self , UpperCamelCase_ ):
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : Optional[Any] = ''
UpperCAmelCase__ : str = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase_ ) + token
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : str = []
else:
current_sub_tokens.append(UpperCamelCase_ )
UpperCAmelCase__ : Tuple = False
out_string += self.sp_model.decode(UpperCamelCase_ )
return out_string.strip()
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = True , **UpperCamelCase_ , ):
UpperCAmelCase__ : Any = kwargs.pop('use_source_tokenizer' , UpperCamelCase_ )
UpperCAmelCase__ : List[Any] = self.convert_ids_to_tokens(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : Any = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_ ) )
UpperCAmelCase__ : Dict = []
sub_texts.append(UpperCamelCase_ )
else:
current_sub_text.append(UpperCamelCase_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
UpperCAmelCase__ : List[str] = re.sub(R' (\[(MASK|SEP)\])' , R'\1' , ' '.join(UpperCamelCase_ ) )
else:
UpperCAmelCase__ : str = ''.join(UpperCamelCase_ )
UpperCAmelCase__ : Union[str, Any] = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
UpperCAmelCase__ : str = self.clean_up_tokenization(UpperCamelCase_ )
return clean_text
else:
return text
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if not os.path.isdir(UpperCamelCase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase__ : Tuple = os.path.join(
UpperCamelCase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase_ , 'wb' ) as fi:
UpperCAmelCase__ : str = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_ )
return (out_vocab_file,)
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : int = [self.cls_token_id]
UpperCAmelCase__ : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = False ):
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] + ([0] * len(UpperCamelCase_ )) + [1]
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
UpperCAmelCase__ : str = [self.sep_token_id]
UpperCAmelCase__ : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
| 110 |
"""simple docstring"""
__lowerCAmelCase : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowerCAmelCase : Tuple = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowerCAmelCase : Any = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 58 | 0 |
"""simple docstring"""
import torch
from torch import nn
class a ( nn.Module ):
def __init__( self : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Tuple=False ):
super().__init__()
_UpperCAmelCase = n_token
_UpperCAmelCase = d_embed
_UpperCAmelCase = d_proj
_UpperCAmelCase = cutoffs + [n_token]
_UpperCAmelCase = [0] + self.cutoffs
_UpperCAmelCase = div_val
_UpperCAmelCase = self.cutoffs[0]
_UpperCAmelCase = len(self.cutoffs ) - 1
_UpperCAmelCase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
_UpperCAmelCase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
_UpperCAmelCase = nn.Parameter(torch.zeros(self.n_clusters ) )
_UpperCAmelCase = nn.ModuleList()
_UpperCAmelCase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(__lowerCAmelCase , __lowerCAmelCase ) ) )
else:
self.out_projs.append(__lowerCAmelCase )
self.out_layers.append(nn.Linear(__lowerCAmelCase , __lowerCAmelCase ) )
else:
for i in range(len(self.cutoffs ) ):
_UpperCAmelCase , _UpperCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(__lowerCAmelCase , __lowerCAmelCase ) ) )
self.out_layers.append(nn.Linear(__lowerCAmelCase , r_idx - l_idx ) )
_UpperCAmelCase = keep_order
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Tuple ):
if proj is None:
_UpperCAmelCase = nn.functional.linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
_UpperCAmelCase = nn.functional.linear(__lowerCAmelCase , proj.t().contiguous() )
_UpperCAmelCase = nn.functional.linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : Union[str, Any]=False ):
if labels is not None:
# Shift so that tokens < n predict n
_UpperCAmelCase = hidden[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = hidden.view(-1 , hidden.size(-1 ) )
_UpperCAmelCase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("""Input and labels should have the same size in the batch dimension.""" )
else:
_UpperCAmelCase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
_UpperCAmelCase = self._compute_logit(__lowerCAmelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
_UpperCAmelCase = labels != -100
_UpperCAmelCase = torch.zeros_like(__lowerCAmelCase , dtype=hidden.dtype , device=hidden.device )
_UpperCAmelCase = (
-nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
_UpperCAmelCase = nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )
else:
# construct weights and biases
_UpperCAmelCase , _UpperCAmelCase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_UpperCAmelCase , _UpperCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase = self.out_layers[0].weight[l_idx:r_idx]
_UpperCAmelCase = self.out_layers[0].bias[l_idx:r_idx]
else:
_UpperCAmelCase = self.out_layers[i].weight
_UpperCAmelCase = self.out_layers[i].bias
if i == 0:
_UpperCAmelCase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_UpperCAmelCase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(__lowerCAmelCase )
biases.append(__lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = weights[0], biases[0], self.out_projs[0]
_UpperCAmelCase = self._compute_logit(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = nn.functional.log_softmax(__lowerCAmelCase , dim=1 )
if labels is None:
_UpperCAmelCase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
_UpperCAmelCase = torch.zeros_like(__lowerCAmelCase , dtype=hidden.dtype , device=hidden.device )
_UpperCAmelCase = 0
_UpperCAmelCase = [0] + self.cutoffs
for i in range(len(__lowerCAmelCase ) - 1 ):
_UpperCAmelCase , _UpperCAmelCase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
_UpperCAmelCase = (labels >= l_idx) & (labels < r_idx)
_UpperCAmelCase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
_UpperCAmelCase = labels.index_select(0 , __lowerCAmelCase ) - l_idx
_UpperCAmelCase = head_logprob.index_select(0 , __lowerCAmelCase )
_UpperCAmelCase = hidden.index_select(0 , __lowerCAmelCase )
else:
_UpperCAmelCase = hidden
if i == 0:
if labels is not None:
_UpperCAmelCase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
_UpperCAmelCase = head_logprob[:, : self.cutoffs[0]]
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = weights[i], biases[i], self.out_projs[i]
_UpperCAmelCase = self._compute_logit(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = nn.functional.log_softmax(__lowerCAmelCase , dim=1 )
_UpperCAmelCase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
_UpperCAmelCase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
_UpperCAmelCase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
_UpperCAmelCase = logprob_i
if labels is not None:
if (hasattr(self , """keep_order""" ) and self.keep_order) or keep_order:
out.index_copy_(0 , __lowerCAmelCase , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : List[Any] ):
if self.n_clusters == 0:
_UpperCAmelCase = self._compute_logit(__lowerCAmelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )
else:
# construct weights and biases
_UpperCAmelCase , _UpperCAmelCase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_UpperCAmelCase , _UpperCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase = self.out_layers[0].weight[l_idx:r_idx]
_UpperCAmelCase = self.out_layers[0].bias[l_idx:r_idx]
else:
_UpperCAmelCase = self.out_layers[i].weight
_UpperCAmelCase = self.out_layers[i].bias
if i == 0:
_UpperCAmelCase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_UpperCAmelCase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(__lowerCAmelCase )
biases.append(__lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = weights[0], biases[0], self.out_projs[0]
_UpperCAmelCase = self._compute_logit(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
_UpperCAmelCase = nn.functional.log_softmax(__lowerCAmelCase , dim=1 )
_UpperCAmelCase = [0] + self.cutoffs
for i in range(len(__lowerCAmelCase ) - 1 ):
_UpperCAmelCase , _UpperCAmelCase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
_UpperCAmelCase = head_logprob[:, : self.cutoffs[0]]
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = weights[i], biases[i], self.out_projs[i]
_UpperCAmelCase = self._compute_logit(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = nn.functional.log_softmax(__lowerCAmelCase , dim=1 )
_UpperCAmelCase = head_logprob[:, -i] + tail_logprob_i
_UpperCAmelCase = logprob_i
return out
| 275 | """simple docstring"""
import unittest
from knapsack import knapsack as k
class a ( unittest.TestCase ):
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
_UpperCAmelCase = [0]
_UpperCAmelCase = len(__lowerCAmelCase )
self.assertEqual(k.knapsack(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) , 0 )
_UpperCAmelCase = [60]
_UpperCAmelCase = [10]
_UpperCAmelCase = len(__lowerCAmelCase )
self.assertEqual(k.knapsack(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) , 0 )
def lowerCAmelCase_ ( self : Optional[int] ):
_UpperCAmelCase = 3
_UpperCAmelCase = [1, 2, 3]
_UpperCAmelCase = [3, 2, 1]
_UpperCAmelCase = len(__lowerCAmelCase )
self.assertEqual(k.knapsack(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) , 5 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = 50
_UpperCAmelCase = [60, 100, 120]
_UpperCAmelCase = [10, 20, 30]
_UpperCAmelCase = len(__lowerCAmelCase )
self.assertEqual(k.knapsack(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) , 220 )
if __name__ == "__main__":
unittest.main()
| 275 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __a ( __UpperCAmelCase : Tuple=None , __UpperCAmelCase : List[Any]=None ) -> Dict:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__UpperCAmelCase )
@dataclass
class snake_case_ :
'''simple docstring'''
lowerCamelCase = field(
metadata={"help": "The csv file to plot."} , )
lowerCamelCase = field(
default=__A , metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."} , )
lowerCamelCase = field(
default=__A , metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."} , )
lowerCamelCase = field(
default=__A , metadata={"help": "Disable logarithmic scale when plotting"} , )
lowerCamelCase = field(
default=__A , metadata={
"help": "Whether the csv file has training results or inference results. Defaults to inference results."
} , )
lowerCamelCase = field(
default=__A , metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."} , )
lowerCamelCase = list_field(
default=__A , metadata={"help": "List of model names that are used instead of the ones in the csv file."} )
def __a ( __UpperCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
try:
int(__UpperCAmelCase )
return True
except ValueError:
return False
def __a ( __UpperCAmelCase : Any ) -> List[str]:
"""simple docstring"""
try:
float(__UpperCAmelCase )
return True
except ValueError:
return False
class snake_case_ :
'''simple docstring'''
def __init__( self : int , __magic_name__ : List[str] ) -> List[Any]:
lowerCamelCase_ : int = args
lowerCamelCase_ : Tuple = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline="" ) as csv_file:
lowerCamelCase_ : Optional[Any] = csv.DictReader(__magic_name__ )
for row in reader:
lowerCamelCase_ : Union[str, Any] = row["model"]
self.result_dict[model_name]["bsz"].append(int(row["batch_size"] ) )
self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"] ) )
if can_convert_to_int(row["result"] ):
# value is not None
lowerCamelCase_ : Tuple = int(row["result"] )
elif can_convert_to_float(row["result"] ):
# value is not None
lowerCamelCase_ : Tuple = float(row["result"] )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
lowerCamelCase_ , lowerCamelCase_ : int = plt.subplots()
lowerCamelCase_ : Any = "Time usage" if self.args.is_time else "Memory usage"
lowerCamelCase_ : int = title_str + " for training" if self.args.is_train else title_str + " for inference"
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale("log" )
ax.set_yscale("log" )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
lowerCamelCase_ : Any = sorted(set(self.result_dict[model_name]["bsz"] ) )
lowerCamelCase_ : List[str] = sorted(set(self.result_dict[model_name]["seq_len"] ) )
lowerCamelCase_ : Any = self.result_dict[model_name]["result"]
((lowerCamelCase_) , (lowerCamelCase_)) : List[Any] = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
lowerCamelCase_ : Dict = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
lowerCamelCase_ : Any = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=__magic_name__ , )
else:
lowerCamelCase_ : int = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((lowerCamelCase_) , (lowerCamelCase_)) : Optional[Any] = (
("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
)
lowerCamelCase_ : Optional[int] = np.asarray(__magic_name__ , __magic_name__ )[: len(__magic_name__ )]
plt.scatter(
__magic_name__ , __magic_name__ , label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}" )
plt.plot(__magic_name__ , __magic_name__ , "--" )
title_str += F" {label_model_name} vs."
lowerCamelCase_ : Optional[int] = title_str[:-4]
lowerCamelCase_ : List[str] = "Time in s" if self.args.is_time else "Memory in MB"
# plot
plt.title(__magic_name__ )
plt.xlabel(__magic_name__ )
plt.ylabel(__magic_name__ )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def __a ( ) -> Any:
"""simple docstring"""
lowerCamelCase_ : Any = HfArgumentParser(__UpperCAmelCase )
lowerCamelCase_ : Any = parser.parse_args_into_dataclasses()[0]
lowerCamelCase_ : Optional[int] = Plot(args=__UpperCAmelCase )
plot.plot()
if __name__ == "__main__":
main()
| 488 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case_ : List[Any] = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Tuple = [
"VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMAEForPreTraining",
"ViTMAELayer",
"ViTMAEModel",
"ViTMAEPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = [
"TFViTMAEForPreTraining",
"TFViTMAEModel",
"TFViTMAEPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
snake_case_ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 488 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= 'Wav2Vec2FeatureExtractor'
A__= 'AutoTokenizer'
def __init__( self : Any , _lowercase : Any , _lowercase : Dict ):
"""simple docstring"""
super().__init__(_lowercase , _lowercase )
UpperCAmelCase__ = self.feature_extractor
UpperCAmelCase__ = False
@classmethod
def _UpperCAmelCase ( cls : List[str] , _lowercase : Tuple , **_lowercase : List[Any] ):
"""simple docstring"""
try:
return super().from_pretrained(_lowercase , **_lowercase )
except OSError:
warnings.warn(
F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
" include a `tokenizer_class` attribute is deprecated and will be "
"removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
" attribute to either your `config.json` or `tokenizer_config.json` "
"file to suppress this warning: " , _lowercase , )
UpperCAmelCase__ = WavaVecaFeatureExtractor.from_pretrained(_lowercase , **_lowercase )
UpperCAmelCase__ = WavaVecaCTCTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(feature_extractor=_lowercase , tokenizer=_lowercase )
def __call__( self : int , *_lowercase : Union[str, Any] , **_lowercase : Dict ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*_lowercase , **_lowercase )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
UpperCAmelCase__ = kwargs.pop("raw_speech" )
else:
UpperCAmelCase__ = kwargs.pop("audio" , _lowercase )
UpperCAmelCase__ = kwargs.pop("sampling_rate" , _lowercase )
UpperCAmelCase__ = kwargs.pop("text" , _lowercase )
if len(_lowercase ) > 0:
UpperCAmelCase__ = args[0]
UpperCAmelCase__ = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
UpperCAmelCase__ = self.feature_extractor(_lowercase , *_lowercase , sampling_rate=_lowercase , **_lowercase )
if text is not None:
UpperCAmelCase__ = self.tokenizer(_lowercase , **_lowercase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
UpperCAmelCase__ = encodings["input_ids"]
return inputs
def _UpperCAmelCase ( self : str , *_lowercase : str , **_lowercase : List[Any] ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*_lowercase , **_lowercase )
UpperCAmelCase__ = kwargs.pop("input_features" , _lowercase )
UpperCAmelCase__ = kwargs.pop("labels" , _lowercase )
if len(_lowercase ) > 0:
UpperCAmelCase__ = args[0]
UpperCAmelCase__ = args[1:]
if input_features is not None:
UpperCAmelCase__ = self.feature_extractor.pad(_lowercase , *_lowercase , **_lowercase )
if labels is not None:
UpperCAmelCase__ = self.tokenizer.pad(_lowercase , **_lowercase )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
UpperCAmelCase__ = labels["input_ids"]
return input_features
def _UpperCAmelCase ( self : Any , *_lowercase : str , **_lowercase : str ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_lowercase , **_lowercase )
def _UpperCAmelCase ( self : Optional[int] , *_lowercase : Optional[int] , **_lowercase : Optional[int] ):
"""simple docstring"""
return self.tokenizer.decode(*_lowercase , **_lowercase )
@contextmanager
def _UpperCAmelCase ( self : int ):
"""simple docstring"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call." )
UpperCAmelCase__ = True
UpperCAmelCase__ = self.tokenizer
yield
UpperCAmelCase__ = self.feature_extractor
UpperCAmelCase__ = False
| 277 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A = logging.getLogger(__name__)
@dataclass
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= field(
default=0.0 , metadata={'help': 'The label smoothing epsilon to apply (if not zero).'} )
A__= field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to SortishSamler or not.'} )
A__= field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
A__= field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'whether to use adafactor'} )
A__= field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'} )
A__= field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'} )
A__= field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Dropout probability. Goes into model.config.'} )
A__= field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Attention dropout probability. Goes into model.config.'} )
A__= field(
default='linear' , metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'} , )
| 277 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase ( _snake_case , unittest.TestCase ):
UpperCAmelCase = DebertaTokenizer
UpperCAmelCase = True
UpperCAmelCase = DebertaTokenizerFast
def __SCREAMING_SNAKE_CASE ( self : Dict ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ :List[Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
UpperCAmelCase__ :int = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
UpperCAmelCase__ :str = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
UpperCAmelCase__ :Optional[int] = {'''unk_token''': '''[UNK]'''}
UpperCAmelCase__ :str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase__ :Optional[Any] = 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(__lowerCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowerCamelCase ) )
def __SCREAMING_SNAKE_CASE ( self : Any , **__lowerCamelCase : List[Any] ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def __SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCamelCase : Tuple ):
UpperCAmelCase__ :List[str] = '''lower newer'''
UpperCAmelCase__ :str = '''lower newer'''
return input_text, output_text
def __SCREAMING_SNAKE_CASE ( self : List[str] ):
UpperCAmelCase__ :Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ :List[str] = '''lower newer'''
UpperCAmelCase__ :str = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
UpperCAmelCase__ :int = tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
UpperCAmelCase__ :List[Any] = tokens + [tokenizer.unk_token]
UpperCAmelCase__ :Optional[int] = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ):
UpperCAmelCase__ :Tuple = self.get_tokenizer()
UpperCAmelCase__ :Dict = tokenizer('''Hello''' , '''World''' )
UpperCAmelCase__ :Any = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['''token_type_ids'''] , __lowerCamelCase )
@slow
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
UpperCAmelCase__ :Any = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
UpperCAmelCase__ :Any = tokenizer.encode('''sequence builders''' , add_special_tokens=__lowerCamelCase )
UpperCAmelCase__ :Union[str, Any] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__lowerCamelCase )
UpperCAmelCase__ :Optional[Any] = tokenizer.encode(
'''sequence builders''' , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
UpperCAmelCase__ :Optional[int] = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
UpperCAmelCase__ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
UpperCAmelCase__ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def __SCREAMING_SNAKE_CASE ( self : str ):
UpperCAmelCase__ :str = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
UpperCAmelCase__ :List[Any] = tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
UpperCAmelCase__ :Tuple = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
UpperCAmelCase__ :Optional[int] = tokenizer(__lowerCamelCase , padding=__lowerCamelCase )
UpperCAmelCase__ :List[str] = [tokenizer.decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase ) for seq in encoding['''input_ids''']]
# fmt: off
UpperCAmelCase__ :List[str] = {
'''input_ids''': [
[1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
],
'''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
'''attention_mask''': [
[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],
[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],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
UpperCAmelCase__ :Any = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
self.assertDictEqual(encoding.data , __lowerCamelCase )
for expected, decoded in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
| 467 |
'''simple docstring'''
from functools import reduce
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def a__ ( UpperCamelCase_ : str = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda UpperCamelCase_, UpperCamelCase_ : str(int(UpperCamelCase_ ) * int(UpperCamelCase_ ) ), n[i : i + 13] ) )
for i in range(len(UpperCamelCase_ ) - 12 ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 467 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _a ( self) -> None:
__snake_case = Vector([1, 2, 3])
self.assertEqual(x.component(0) , 1)
self.assertEqual(x.component(2) , 3)
__snake_case = Vector()
def _a ( self) -> None:
__snake_case = Vector([0, 0, 0, 0, 0, 1])
self.assertEqual(str(lowercase_) , '(0,0,0,0,0,1)')
def _a ( self) -> None:
__snake_case = Vector([1, 2, 3, 4])
self.assertEqual(len(lowercase_) , 4)
def _a ( self) -> None:
__snake_case = Vector([1, 2])
__snake_case = Vector([1, 2, 3, 4, 5])
__snake_case = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
__snake_case = Vector([1, -1, 1, -1, 2, -3, 4, -5])
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3)
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3)
self.assertEqual(z.euclidean_length() , 0)
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3)
def _a ( self) -> None:
__snake_case = Vector([1, 2, 3])
__snake_case = Vector([1, 1, 1])
self.assertEqual((x + y).component(0) , 2)
self.assertEqual((x + y).component(1) , 3)
self.assertEqual((x + y).component(2) , 4)
def _a ( self) -> None:
__snake_case = Vector([1, 2, 3])
__snake_case = Vector([1, 1, 1])
self.assertEqual((x - y).component(0) , 0)
self.assertEqual((x - y).component(1) , 1)
self.assertEqual((x - y).component(2) , 2)
def _a ( self) -> None:
__snake_case = Vector([1, 2, 3])
__snake_case = Vector([2, -1, 4]) # for test of dot product
__snake_case = Vector([1, -2, -1])
self.assertEqual(str(x * 3.0) , '(3.0,6.0,9.0)')
self.assertEqual((a * b) , 0)
def _a ( self) -> None:
self.assertEqual(str(zero_vector(1_0)).count('0') , 1_0)
def _a ( self) -> None:
self.assertEqual(str(unit_basis_vector(3 , 1)) , '(0,1,0)')
def _a ( self) -> None:
__snake_case = Vector([1, 2, 3])
__snake_case = Vector([1, 0, 1])
self.assertEqual(str(axpy(2 , lowercase_ , lowercase_)) , '(3,4,7)')
def _a ( self) -> None:
__snake_case = Vector([1, 0, 0, 0, 0, 0])
__snake_case = x.copy()
self.assertEqual(str(lowercase_) , str(lowercase_))
def _a ( self) -> None:
__snake_case = Vector([1, 0, 0])
x.change_component(0 , 0)
x.change_component(1 , 1)
self.assertEqual(str(lowercase_) , '(0,1,0)')
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(lowercase_))
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
__snake_case = [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(minors[x][y] , a.minor(lowercase_ , lowercase_))
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
__snake_case = [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
for x in range(a.height()):
for y in range(a.width()):
self.assertEqual(cofactors[x][y] , a.cofactor(lowercase_ , lowercase_))
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(-5 , a.determinant())
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3)
__snake_case = Vector([1, 2, 3])
self.assertEqual('(14,32,50)' , str(a * x))
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2))
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
a.change_component(0 , 2 , 5)
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(lowercase_))
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
self.assertEqual(7 , a.component(2 , 1) , 0.01)
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
__snake_case = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b))
def _a ( self) -> None:
__snake_case = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3)
__snake_case = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3)
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b))
def _a ( self) -> None:
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5)) , )
if __name__ == "__main__":
unittest.main()
| 719 |
from maths.prime_check import is_prime
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 676 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : List[str] = (DPMSolverSinglestepScheduler,)
__lowerCamelCase : int = (("num_inference_steps", 25),)
def _snake_case ( self , **_lowerCAmelCase ) -> Any:
_lowerCAmelCase = {
"num_train_timesteps": 1000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"prediction_type": "epsilon",
"thresholding": False,
"sample_max_value": 1.0,
"algorithm_type": "dpmsolver++",
"solver_type": "midpoint",
"lambda_min_clipped": -float("inf" ),
"variance_type": None,
}
config.update(**_lowerCAmelCase )
return config
def _snake_case ( self , _lowerCAmelCase=0 , **_lowerCAmelCase ) -> List[Any]:
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop("num_inference_steps" , _lowerCAmelCase )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
_lowerCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config(**_lowerCAmelCase )
_lowerCAmelCase = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residuals
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCAmelCase )
_lowerCAmelCase = scheduler_class.from_pretrained(_lowerCAmelCase )
new_scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residuals
_lowerCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase , _lowerCAmelCase = sample, sample
for t in range(_lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
_lowerCAmelCase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase ).prev_sample
_lowerCAmelCase = new_scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _snake_case ( self ) -> int:
pass
def _snake_case ( self , _lowerCAmelCase=0 , **_lowerCAmelCase ) -> Optional[int]:
_lowerCAmelCase = dict(self.forward_default_kwargs )
_lowerCAmelCase = kwargs.pop("num_inference_steps" , _lowerCAmelCase )
_lowerCAmelCase = self.dummy_sample
_lowerCAmelCase = 0.1 * sample
_lowerCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
_lowerCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCAmelCase )
_lowerCAmelCase = 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)
_lowerCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase ).prev_sample
_lowerCAmelCase = new_scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _snake_case ( self , _lowerCAmelCase=None , **_lowerCAmelCase ) -> Tuple:
if scheduler is None:
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(**_lowerCAmelCase )
_lowerCAmelCase = scheduler_class(**_lowerCAmelCase )
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(**_lowerCAmelCase )
_lowerCAmelCase = scheduler_class(**_lowerCAmelCase )
_lowerCAmelCase = 10
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = model(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
return sample
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_lowerCAmelCase = 50
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_lowerCAmelCase = model(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def _snake_case ( self ) -> Optional[Any]:
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def _snake_case ( self ) -> List[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_lowerCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_lowerCAmelCase = self.full_loop(scheduler=_lowerCAmelCase )
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
_lowerCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_lowerCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_lowerCAmelCase = self.full_loop(scheduler=_lowerCAmelCase )
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def _snake_case ( self ) -> str:
self.check_over_configs(thresholding=_lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_lowerCAmelCase , prediction_type=_lowerCAmelCase , sample_max_value=_lowerCAmelCase , algorithm_type="dpmsolver++" , solver_order=_lowerCAmelCase , solver_type=_lowerCAmelCase , )
def _snake_case ( self ) -> Dict:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def _snake_case ( self ) -> Union[str, Any]:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_lowerCAmelCase , solver_type=_lowerCAmelCase , prediction_type=_lowerCAmelCase , algorithm_type=_lowerCAmelCase , )
_lowerCAmelCase = self.full_loop(
solver_order=_lowerCAmelCase , solver_type=_lowerCAmelCase , prediction_type=_lowerCAmelCase , algorithm_type=_lowerCAmelCase , )
assert not torch.isnan(_lowerCAmelCase ).any(), "Samples have nan numbers"
def _snake_case ( self ) -> Optional[Any]:
self.check_over_configs(lower_order_final=_lowerCAmelCase )
self.check_over_configs(lower_order_final=_lowerCAmelCase )
def _snake_case ( self ) -> Optional[Any]:
self.check_over_configs(lambda_min_clipped=-float("inf" ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def _snake_case ( self ) -> str:
self.check_over_configs(variance_type=_lowerCAmelCase )
self.check_over_configs(variance_type="learned_range" )
def _snake_case ( self ) -> int:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=_lowerCAmelCase , time_step=0 )
def _snake_case ( self ) -> Any:
_lowerCAmelCase = self.full_loop()
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def _snake_case ( self ) -> List[str]:
_lowerCAmelCase = self.full_loop(use_karras_sigmas=_lowerCAmelCase )
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase = self.full_loop(prediction_type="v_prediction" )
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def _snake_case ( self ) -> Any:
_lowerCAmelCase = self.full_loop(prediction_type="v_prediction" , use_karras_sigmas=_lowerCAmelCase )
_lowerCAmelCase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def _snake_case ( self ) -> List[Any]:
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(thresholding=_lowerCAmelCase , dynamic_thresholding_ratio=0 )
_lowerCAmelCase = scheduler_class(**_lowerCAmelCase )
_lowerCAmelCase = 10
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(_lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = model(_lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 18 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
def __init__( self , A_ , A_=7 , A_=3 , A_=18 , A_=30 , A_=400 , A_=True , A_=None , A_=True , A_=False , A_=True , A_=True , A_=[0.5, 0.5, 0.5] , A_=[0.5, 0.5, 0.5] , )-> Dict:
'''simple docstring'''
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size if size is not None else {'height': 18, 'width': 20}
UpperCamelCase = do_thumbnail
UpperCamelCase = do_align_axis
UpperCamelCase = do_pad
UpperCamelCase = do_normalize
UpperCamelCase = image_mean
UpperCamelCase = image_std
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( snake_case_ , unittest.TestCase):
lowerCAmelCase_ = DonutImageProcessor if is_vision_available() else None
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
UpperCamelCase = DonutImageProcessingTester(self )
@property
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A_ , 'do_resize' ) )
self.assertTrue(hasattr(A_ , 'size' ) )
self.assertTrue(hasattr(A_ , 'do_thumbnail' ) )
self.assertTrue(hasattr(A_ , 'do_align_long_axis' ) )
self.assertTrue(hasattr(A_ , 'do_pad' ) )
self.assertTrue(hasattr(A_ , 'do_normalize' ) )
self.assertTrue(hasattr(A_ , 'image_mean' ) )
self.assertTrue(hasattr(A_ , 'image_std' ) )
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def UpperCAmelCase_ ( self )-> Tuple:
'''simple docstring'''
pass
@is_flaky()
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , Image.Image )
# Test not batched input
UpperCamelCase = 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
UpperCamelCase = image_processing(A_ , 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'],
) , )
@is_flaky()
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , numpify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , np.ndarray )
# Test not batched input
UpperCamelCase = 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
UpperCamelCase = image_processing(A_ , 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'],
) , )
@is_flaky()
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , torchify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , torch.Tensor )
# Test not batched input
UpperCamelCase = 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
UpperCamelCase = image_processing(A_ , 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'],
) , )
| 3 | 0 |
"""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
lowerCAmelCase__ = [
'''openmmlab/upernet-convnext-tiny''',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCAmelCase__ = '''UperNetConfig'''
class __snake_case ( nn.Module):
def __init__( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : Union[int, Tuple[int, int]] , __lowerCAmelCase : Union[int, Tuple[int, int], str] = 0 , __lowerCAmelCase : bool = False , __lowerCAmelCase : Union[int, Tuple[int, int]] = 1 , ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : Optional[Any] = nn.Convad(
in_channels=__lowerCAmelCase , out_channels=__lowerCAmelCase , kernel_size=__lowerCAmelCase , padding=__lowerCAmelCase , bias=__lowerCAmelCase , dilation=__lowerCAmelCase , )
_lowerCamelCase : Tuple = nn.BatchNormad(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = nn.ReLU()
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : torch.Tensor ):
"""simple docstring"""
_lowerCamelCase : List[Any] = self.conv(__lowerCAmelCase )
_lowerCamelCase : int = self.batch_norm(__lowerCAmelCase )
_lowerCamelCase : Tuple = self.activation(__lowerCAmelCase )
return output
class __snake_case ( nn.Module):
def __init__( self : str , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : Optional[Any] = [
nn.AdaptiveAvgPoolad(__lowerCAmelCase ),
UperNetConvModule(__lowerCAmelCase , __lowerCAmelCase , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(__lowerCAmelCase ) , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : torch.Tensor ):
"""simple docstring"""
_lowerCamelCase : List[Any] = input
for layer in self.layers:
_lowerCamelCase : List[str] = layer(__lowerCAmelCase )
return hidden_state
class __snake_case ( nn.Module):
def __init__( self : Union[str, Any] , __lowerCAmelCase : Tuple[int, ...] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : bool ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : int = pool_scales
_lowerCamelCase : Any = align_corners
_lowerCamelCase : Optional[Any] = in_channels
_lowerCamelCase : Any = channels
_lowerCamelCase : Dict = []
for i, pool_scale in enumerate(__lowerCAmelCase ):
_lowerCamelCase : str = UperNetPyramidPoolingBlock(pool_scale=__lowerCAmelCase , in_channels=__lowerCAmelCase , channels=__lowerCAmelCase )
self.blocks.append(__lowerCAmelCase )
self.add_module(str(__lowerCAmelCase ) , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : torch.Tensor ):
"""simple docstring"""
_lowerCamelCase : Tuple = []
for ppm in self.blocks:
_lowerCamelCase : List[Any] = ppm(__lowerCAmelCase )
_lowerCamelCase : List[Any] = nn.functional.interpolate(
__lowerCAmelCase , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(__lowerCAmelCase )
return ppm_outs
class __snake_case ( nn.Module):
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : int ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : Union[str, Any] = config
_lowerCamelCase : int = config.pool_scales # e.g. (1, 2, 3, 6)
_lowerCamelCase : Optional[Any] = in_channels
_lowerCamelCase : List[str] = config.hidden_size
_lowerCamelCase : List[str] = False
_lowerCamelCase : Union[str, Any] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
_lowerCamelCase : Any = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
_lowerCamelCase : Optional[Any] = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
_lowerCamelCase : Optional[Any] = nn.ModuleList()
_lowerCamelCase : Optional[int] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
_lowerCamelCase : Any = UperNetConvModule(__lowerCAmelCase , self.channels , kernel_size=1 )
_lowerCamelCase : Union[str, Any] = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(__lowerCAmelCase )
self.fpn_convs.append(__lowerCAmelCase )
_lowerCamelCase : List[Any] = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
self.apply(self._init_weights )
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
if isinstance(__lowerCAmelCase , 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 SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : List[str] = inputs[-1]
_lowerCamelCase : List[str] = [x]
psp_outs.extend(self.psp_modules(__lowerCAmelCase ) )
_lowerCamelCase : Optional[Any] = torch.cat(__lowerCAmelCase , dim=1 )
_lowerCamelCase : Any = self.bottleneck(__lowerCAmelCase )
return output
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : torch.Tensor ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(__lowerCAmelCase ) )
# build top-down path
_lowerCamelCase : List[str] = len(__lowerCAmelCase )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_lowerCamelCase : List[str] = laterals[i - 1].shape[2:]
_lowerCamelCase : List[str] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=__lowerCAmelCase , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
_lowerCamelCase : Optional[Any] = [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 ):
_lowerCamelCase : List[Any] = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
_lowerCamelCase : Optional[Any] = torch.cat(__lowerCAmelCase , dim=1 )
_lowerCamelCase : Union[str, Any] = self.fpn_bottleneck(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self.classifier(__lowerCAmelCase )
return output
class __snake_case ( nn.Module):
def __init__( self : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : int = 2 , __lowerCAmelCase : int = 3 , __lowerCAmelCase : Union[int, Tuple[int, int]] = 1 ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : str = config
_lowerCamelCase : List[Any] = config.auxiliary_in_channels
_lowerCamelCase : int = config.auxiliary_channels
_lowerCamelCase : List[Any] = config.auxiliary_num_convs
_lowerCamelCase : List[str] = config.auxiliary_concat_input
_lowerCamelCase : Tuple = in_index
_lowerCamelCase : str = (kernel_size // 2) * dilation
_lowerCamelCase : List[Any] = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=__lowerCAmelCase , padding=__lowerCAmelCase , dilation=__lowerCAmelCase ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=__lowerCAmelCase , padding=__lowerCAmelCase , dilation=__lowerCAmelCase ) )
if self.num_convs == 0:
_lowerCamelCase : str = nn.Identity()
else:
_lowerCamelCase : Tuple = nn.Sequential(*__lowerCAmelCase )
if self.concat_input:
_lowerCamelCase : Tuple = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=__lowerCAmelCase , padding=kernel_size // 2 )
_lowerCamelCase : Any = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
self.apply(self._init_weights )
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(__lowerCAmelCase , 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 SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : torch.Tensor ):
"""simple docstring"""
_lowerCamelCase : int = encoder_hidden_states[self.in_index]
_lowerCamelCase : Any = self.convs(__lowerCAmelCase )
if self.concat_input:
_lowerCamelCase : List[str] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
_lowerCamelCase : Optional[Any] = self.classifier(__lowerCAmelCase )
return output
class __snake_case ( _lowercase):
snake_case__ : List[Any] = UperNetConfig
snake_case__ : Union[str, Any] = "pixel_values"
snake_case__ : Optional[int] = True
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : Tuple ):
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any=False ):
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCamelCase : str = value
lowerCAmelCase__ = R'''
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowerCAmelCase__ = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." , _lowercase , )
class __snake_case ( _lowercase):
def __init__( self : str , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
super().__init__(__lowerCAmelCase )
_lowerCamelCase : str = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
_lowerCamelCase : Optional[Any] = UperNetHead(__lowerCAmelCase , in_channels=self.backbone.channels )
_lowerCamelCase : Optional[Any] = UperNetFCNHead(__lowerCAmelCase ) 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=__lowerCAmelCase , config_class=_CONFIG_FOR_DOC )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : Optional[torch.Tensor] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[torch.Tensor] = None , __lowerCAmelCase : Optional[bool] = None , ):
"""simple docstring"""
_lowerCamelCase : Tuple = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCamelCase : List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_lowerCamelCase : Union[str, Any] = output_attentions if output_attentions is not None else self.config.output_attentions
_lowerCamelCase : List[Any] = self.backbone.forward_with_filtered_kwargs(
__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , output_attentions=__lowerCAmelCase )
_lowerCamelCase : Any = outputs.feature_maps
_lowerCamelCase : Tuple = self.decode_head(__lowerCAmelCase )
_lowerCamelCase : int = nn.functional.interpolate(__lowerCAmelCase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=__lowerCAmelCase )
_lowerCamelCase : int = None
if self.auxiliary_head is not None:
_lowerCamelCase : Optional[Any] = self.auxiliary_head(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = nn.functional.interpolate(
__lowerCAmelCase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=__lowerCAmelCase )
_lowerCamelCase : Union[str, 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
_lowerCamelCase : List[str] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
_lowerCamelCase : Any = loss_fct(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Optional[int] = loss_fct(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : str = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
_lowerCamelCase : int = (logits,) + outputs[1:]
else:
_lowerCamelCase : Any = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=__lowerCAmelCase , logits=__lowerCAmelCase , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 712 |
"""simple docstring"""
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 __snake_case ( _lowercase , unittest.TestCase):
snake_case__ : Tuple = GPTaTokenizer
snake_case__ : str = GPTaTokenizerFast
snake_case__ : Union[str, Any] = True
snake_case__ : Dict = {"add_prefix_space": True}
snake_case__ : Any = False
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCamelCase : List[Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
_lowerCamelCase : Union[str, Any] = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_lowerCamelCase : List[str] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_lowerCamelCase : List[Any] = {'''unk_token''': '''<unk>'''}
_lowerCamelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase : int = 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(__lowerCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowerCAmelCase ) )
def SCREAMING_SNAKE_CASE ( self : Any , **__lowerCAmelCase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] , **__lowerCAmelCase : Tuple ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = '''lower newer'''
_lowerCamelCase : Any = '''lower newer'''
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : Any = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_lowerCamelCase : Any = '''lower newer'''
_lowerCamelCase : Dict = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
_lowerCamelCase : Optional[int] = tokenizer.tokenize(__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Dict = tokens + [tokenizer.unk_token]
_lowerCamelCase : List[str] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_lowerCamelCase : Any = self.get_tokenizer()
_lowerCamelCase : Tuple = self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase )
_lowerCamelCase : int = '''lower newer'''
# Testing tokenization
_lowerCamelCase : Optional[int] = tokenizer.tokenize(__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = rust_tokenizer.tokenize(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
# Testing conversion to ids without special tokens
_lowerCamelCase : List[Any] = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = rust_tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
# Testing conversion to ids with special tokens
_lowerCamelCase : str = self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = tokenizer.encode(__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
_lowerCamelCase : Dict = rust_tokenizer.encode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
# Testing the unknown token
_lowerCamelCase : int = tokens + [rust_tokenizer.unk_token]
_lowerCamelCase : List[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple , *__lowerCAmelCase : Optional[int] , **__lowerCAmelCase : str ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : List[Any]=1_5 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_lowerCamelCase : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
# Simple input
_lowerCamelCase : Tuple = '''This is a simple input'''
_lowerCamelCase : List[Any] = ['''This is a simple input 1''', '''This is a simple input 2''']
_lowerCamelCase : Union[str, Any] = ('''This is a simple input''', '''This is a pair''')
_lowerCamelCase : Tuple = [
('''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(__lowerCAmelCase , tokenizer_r.encode , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Simple input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Simple input
self.assertRaises(
__lowerCAmelCase , tokenizer_r.batch_encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' , )
# Pair input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Pair input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Pair input
self.assertRaises(
__lowerCAmelCase , tokenizer_r.batch_encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' , )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
_lowerCamelCase : Any = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
_lowerCamelCase : List[str] = '''This is a simple input'''
_lowerCamelCase : int = ['''This is a simple input looooooooong''', '''This is a simple input''']
_lowerCamelCase : int = ('''This is a simple input''', '''This is a pair''')
_lowerCamelCase : int = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
_lowerCamelCase : Tuple = tokenizer.pad_token_id
_lowerCamelCase : Dict = tokenizer(__lowerCAmelCase , padding='''max_length''' , max_length=3_0 , return_tensors='''np''' )
_lowerCamelCase : Any = tokenizer(__lowerCAmelCase , padding=__lowerCAmelCase , truncate=__lowerCAmelCase , return_tensors='''np''' )
_lowerCamelCase : List[Any] = tokenizer(*__lowerCAmelCase , padding='''max_length''' , max_length=6_0 , return_tensors='''np''' )
_lowerCamelCase : Optional[Any] = tokenizer(__lowerCAmelCase , padding=__lowerCAmelCase , truncate=__lowerCAmelCase , 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 SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = '''$$$'''
_lowerCamelCase : Dict = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowerCAmelCase , add_bos_token=__lowerCAmelCase )
_lowerCamelCase : Any = '''This is a simple input'''
_lowerCamelCase : Tuple = ['''This is a simple input 1''', '''This is a simple input 2''']
_lowerCamelCase : Dict = tokenizer.bos_token_id
_lowerCamelCase : Tuple = tokenizer(__lowerCAmelCase )
_lowerCamelCase : Dict = tokenizer(__lowerCAmelCase )
self.assertEqual(out_s.input_ids[0] , __lowerCAmelCase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_lowerCamelCase : Any = tokenizer.decode(out_s.input_ids )
_lowerCamelCase : Any = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowerCAmelCase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[str] = [self.get_tokenizer(do_lower_case=__lowerCAmelCase , add_bos_token=__lowerCAmelCase )]
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
_lowerCamelCase : str = '''Encode this.'''
_lowerCamelCase : Optional[Any] = '''This one too please.'''
_lowerCamelCase : List[str] = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
encoded_sequence += tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
_lowerCamelCase : List[Any] = tokenizer.encode_plus(
__lowerCAmelCase , __lowerCAmelCase , add_special_tokens=__lowerCAmelCase , return_special_tokens_mask=__lowerCAmelCase , )
_lowerCamelCase : str = encoded_sequence_dict['''input_ids''']
_lowerCamelCase : List[Any] = encoded_sequence_dict['''special_tokens_mask''']
self.assertEqual(len(__lowerCAmelCase ) , len(__lowerCAmelCase ) )
_lowerCamelCase : Any = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowerCAmelCase )
]
_lowerCamelCase : List[Any] = [x for x in filtered_sequence if x is not None]
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
@require_tokenizers
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : Dict = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=__lowerCAmelCase )
_lowerCamelCase : Tuple = '''A photo of a cat'''
_lowerCamelCase : Tuple = tokenizer.encode(
__lowerCAmelCase , )
self.assertEqual(__lowerCAmelCase , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
tokenizer.save_pretrained('''test_opt''' )
_lowerCamelCase : Tuple = AutoTokenizer.from_pretrained('''./test_opt''' )
_lowerCamelCase : Optional[int] = tokenizer.encode(
__lowerCAmelCase , )
self.assertEqual(__lowerCAmelCase , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , use_slow=__lowerCAmelCase )
_lowerCamelCase : Tuple = '''A photo of a cat'''
_lowerCamelCase : List[str] = tokenizer.encode(
__lowerCAmelCase , )
# Same as above
self.assertEqual(__lowerCAmelCase , [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 SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = '''bos'''
_lowerCamelCase : Optional[Any] = tokenizer.get_vocab()['''bos''']
_lowerCamelCase : Any = '''A photo of a cat'''
_lowerCamelCase : int = tokenizer.encode(
__lowerCAmelCase , )
# We changed the bos token
self.assertEqual(__lowerCAmelCase , [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
tokenizer.save_pretrained('''./tok''' )
_lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained('''./tok''' )
self.assertTrue(tokenizer.is_fast )
_lowerCamelCase : Tuple = tokenizer.encode(
__lowerCAmelCase , )
self.assertEqual(__lowerCAmelCase , [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
| 598 | 0 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__snake_case = {
"""cola""": 2,
"""mnli""": 3,
"""mrpc""": 2,
"""sst-2""": 2,
"""sts-b""": 1,
"""qqp""": 2,
"""qnli""": 2,
"""rte""": 2,
"""wnli""": 2,
}
logging.set_verbosity_info()
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = XLNetConfig.from_json_file(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = finetuning_task.lower() if finetuning_task is not None else ''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'Building PyTorch XLNetForSequenceClassification model from configuration: {config}' )
SCREAMING_SNAKE_CASE__ = finetuning_task
SCREAMING_SNAKE_CASE__ = GLUE_TASKS_NUM_LABELS[finetuning_task]
SCREAMING_SNAKE_CASE__ = XLNetForSequenceClassification(UpperCamelCase_ )
elif "squad" in finetuning_task:
SCREAMING_SNAKE_CASE__ = finetuning_task
SCREAMING_SNAKE_CASE__ = XLNetForQuestionAnswering(UpperCamelCase_ )
else:
SCREAMING_SNAKE_CASE__ = XLNetLMHeadModel(UpperCamelCase_ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save pytorch-model
SCREAMING_SNAKE_CASE__ = os.path.join(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = 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__":
__snake_case = 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(
"""--xlnet_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained XLNet model. \n"""
"""This specifies the model architecture."""
),
)
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(
"""--finetuning_task""",
default=None,
type=str,
help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
)
__snake_case = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 472 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""openai/imagegpt-small""": """""",
"""openai/imagegpt-medium""": """""",
"""openai/imagegpt-large""": """""",
}
class lowercase__ ( _UpperCAmelCase ):
A__ : Optional[int] ="""imagegpt"""
A__ : Union[str, Any] =["""past_key_values"""]
A__ : Union[str, Any] ={
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : int , UpperCAmelCase_ : Dict=512 + 1 , UpperCAmelCase_ : Union[str, Any]=32 * 32 , UpperCAmelCase_ : List[str]=512 , UpperCAmelCase_ : Union[str, Any]=24 , UpperCAmelCase_ : List[str]=8 , UpperCAmelCase_ : int=None , UpperCAmelCase_ : Tuple="quick_gelu" , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : List[Any]=0.1 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : Dict=1e-5 , UpperCAmelCase_ : int=0.02 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Any=True , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : int=False , UpperCAmelCase_ : Optional[Any]=False , **UpperCAmelCase_ : List[str] , ):
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = n_positions
SCREAMING_SNAKE_CASE__ = n_embd
SCREAMING_SNAKE_CASE__ = n_layer
SCREAMING_SNAKE_CASE__ = n_head
SCREAMING_SNAKE_CASE__ = n_inner
SCREAMING_SNAKE_CASE__ = activation_function
SCREAMING_SNAKE_CASE__ = resid_pdrop
SCREAMING_SNAKE_CASE__ = embd_pdrop
SCREAMING_SNAKE_CASE__ = attn_pdrop
SCREAMING_SNAKE_CASE__ = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = scale_attn_weights
SCREAMING_SNAKE_CASE__ = use_cache
SCREAMING_SNAKE_CASE__ = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE__ = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE__ = tie_word_embeddings
super().__init__(tie_word_embeddings=UpperCAmelCase_ , **UpperCAmelCase_ )
class lowercase__ ( _UpperCAmelCase ):
@property
def A_ ( self : List[str] ):
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
] )
def A_ ( self : Optional[int] , UpperCAmelCase_ : "FeatureExtractionMixin" , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional["TensorType"] = None , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : int = 32 , UpperCAmelCase_ : int = 32 , ):
SCREAMING_SNAKE_CASE__ = self._generate_dummy_images(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = dict(preprocessor(images=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ ) )
return inputs
| 472 | 1 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
snake_case__ : Optional[int] = """hp"""
snake_case__ : int = {}
snake_case__ : str = None
@classmethod
def _A ( cls : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Any ):
UpperCamelCase :Optional[int] = prefix
UpperCamelCase :Union[str, Any] = defaults
cls.build_naming_info()
@staticmethod
def _A ( __lowerCamelCase : Any , __lowerCamelCase : List[Any] ):
if len(_UpperCamelCase ) == 0:
return ""
UpperCamelCase :Optional[Any] = None
if any(char.isdigit() for char in word ):
raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number""" )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(_UpperCamelCase ) + 1 ):
UpperCamelCase :List[str] = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
UpperCamelCase :int = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(__lowerCamelCase : Dict ):
UpperCamelCase :Union[str, Any] = """"""
while integer != 0:
UpperCamelCase :Any = chr(ord("""A""" ) + integer % 10 ) + s
integer //= 10
return s
UpperCamelCase :int = 0
while True:
UpperCamelCase :int = word + """#""" + int_to_alphabetic(_UpperCamelCase )
if sword in info["reverse_short_word"]:
continue
else:
UpperCamelCase :Optional[Any] = sword
break
UpperCamelCase :Tuple = short_word
UpperCamelCase :Union[str, Any] = word
return short_word
@staticmethod
def _A ( __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any] ):
UpperCamelCase :Optional[Any] = param_name.split("""_""" )
UpperCamelCase :int = [TrialShortNamer.shortname_for_word(_UpperCamelCase , _UpperCamelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
UpperCamelCase :str = ["""""", """_"""]
for separator in separators:
UpperCamelCase :Optional[Any] = separator.join(_UpperCamelCase )
if shortname not in info["reverse_short_param"]:
UpperCamelCase :Any = shortname
UpperCamelCase :Optional[int] = param_name
return shortname
return param_name
@staticmethod
def _A ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any] ):
UpperCamelCase :str = TrialShortNamer.shortname_for_key(_UpperCamelCase , _UpperCamelCase )
UpperCamelCase :int = short_name
UpperCamelCase :Optional[int] = param_name
@classmethod
def _A ( cls : Tuple ):
if cls.NAMING_INFO is not None:
return
UpperCamelCase :str = {
"""short_word""": {},
"""reverse_short_word""": {},
"""short_param""": {},
"""reverse_short_param""": {},
}
UpperCamelCase :int = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(_UpperCamelCase , _UpperCamelCase )
UpperCamelCase :List[Any] = info
@classmethod
def _A ( cls : Dict , __lowerCamelCase : List[str] ):
cls.build_naming_info()
assert cls.PREFIX is not None
UpperCamelCase :List[str] = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F"""You should provide a default value for the param name {k} with value {v}""" )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
UpperCamelCase :Any = cls.NAMING_INFO["""short_param"""][k]
if isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCamelCase :int = 1 if v else 0
UpperCamelCase :List[str] = """""" if isinstance(_UpperCamelCase , (int, float) ) else """-"""
UpperCamelCase :Dict = F"""{key}{sep}{v}"""
name.append(_UpperCamelCase )
return "_".join(_UpperCamelCase )
@classmethod
def _A ( cls : int , __lowerCamelCase : Tuple ):
UpperCamelCase :Tuple = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
UpperCamelCase :List[Any] = []
else:
UpperCamelCase :Dict = repr.split("""_""" )
UpperCamelCase :Tuple = {}
for value in values:
if "-" in value:
UpperCamelCase :Tuple = value.split("""-""" )
else:
UpperCamelCase :int = re.sub("""[0-9.]""" , """""" , _UpperCamelCase )
UpperCamelCase :Tuple = float(re.sub("""[^0-9.]""" , """""" , _UpperCamelCase ) )
UpperCamelCase :Dict = cls.NAMING_INFO["""reverse_short_param"""][p_k]
UpperCamelCase :int = p_v
for k in cls.DEFAULTS:
if k not in parameters:
UpperCamelCase :List[Any] = cls.DEFAULTS[k]
return parameters
| 715 |
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
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : str = '''▁'''
UpperCAmelCase_ : str = {
'''vocab_file''': '''vocab.json''',
'''spm_file''': '''sentencepiece.bpe.model''',
}
UpperCAmelCase_ : List[str] = {
'''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'''
)
},
}
UpperCAmelCase_ : Any = {
'''facebook/s2t-small-librispeech-asr''': 10_24,
}
UpperCAmelCase_ : Tuple = ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']
UpperCAmelCase_ : int = {'''mustc''': MUSTC_LANGS}
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Optional[int] = VOCAB_FILES_NAMES
snake_case__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : Union[str, Any] = MAX_MODEL_INPUT_SIZES
snake_case__ : Optional[int] = ["""input_ids""", """attention_mask"""]
snake_case__ : List[int] = []
def __init__( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : Any , __lowerCamelCase : Optional[Any]="<s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : str="<pad>" , __lowerCamelCase : Optional[Any]="<unk>" , __lowerCamelCase : Union[str, Any]=False , __lowerCamelCase : Dict=False , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : Optional[Dict[str, Any]] = None , **__lowerCamelCase : int , ):
UpperCamelCase :List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , pad_token=__lowerCamelCase , do_upper_case=__lowerCamelCase , do_lower_case=__lowerCamelCase , tgt_lang=__lowerCamelCase , lang_codes=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
UpperCamelCase :List[str] = do_upper_case
UpperCamelCase :int = do_lower_case
UpperCamelCase :Dict = load_json(__lowerCamelCase )
UpperCamelCase :Optional[int] = {v: k for k, v in self.encoder.items()}
UpperCamelCase :Optional[Any] = spm_file
UpperCamelCase :str = load_spm(__lowerCamelCase , self.sp_model_kwargs )
if lang_codes is not None:
UpperCamelCase :Dict = lang_codes
UpperCamelCase :List[str] = LANGUAGES[lang_codes]
UpperCamelCase :List[Any] = [F"""<lang:{lang}>""" for lang in self.langs]
UpperCamelCase :int = {lang: self.sp_model.PieceToId(F"""<lang:{lang}>""" ) for lang in self.langs}
UpperCamelCase :Union[str, Any] = self.lang_tokens
UpperCamelCase :Tuple = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
UpperCamelCase :Optional[Any] = {}
@property
def _A ( self : Any ):
return len(self.encoder )
@property
def _A ( self : int ):
return self._tgt_lang
@tgt_lang.setter
def _A ( self : Union[str, Any] , __lowerCamelCase : int ):
UpperCamelCase :str = new_tgt_lang
self.set_tgt_lang_special_tokens(__lowerCamelCase )
def _A ( self : Dict , __lowerCamelCase : str ):
UpperCamelCase :int = self.lang_code_to_id[tgt_lang]
UpperCamelCase :Optional[int] = [lang_code_id]
def _A ( self : Union[str, Any] , __lowerCamelCase : str ):
return self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
def _A ( self : Optional[int] , __lowerCamelCase : List[str] ):
return self.encoder.get(__lowerCamelCase , self.encoder[self.unk_token] )
def _A ( self : Optional[int] , __lowerCamelCase : int ):
return self.decoder.get(__lowerCamelCase , self.unk_token )
def _A ( self : Union[str, Any] , __lowerCamelCase : List[str] ):
UpperCamelCase :Any = []
UpperCamelCase :Dict = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
UpperCamelCase :Dict = self.sp_model.decode(__lowerCamelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
UpperCamelCase :Dict = []
else:
current_sub_tokens.append(__lowerCamelCase )
UpperCamelCase :Dict = self.sp_model.decode(__lowerCamelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def _A ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int]=None ):
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 _A ( self : Optional[Any] , __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 )
UpperCamelCase :Tuple = [1] * len(self.prefix_tokens )
UpperCamelCase :Tuple = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(__lowerCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(__lowerCamelCase )) + ([0] * len(__lowerCamelCase )) + suffix_ones
def _A ( self : Any ):
UpperCamelCase :Optional[Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[Any] ):
UpperCamelCase :Optional[int] = self.__dict__.copy()
UpperCamelCase :List[str] = None
return state
def __setstate__( self : int , __lowerCamelCase : Dict ):
UpperCamelCase :List[str] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase :int = {}
UpperCamelCase :Optional[int] = load_spm(self.spm_file , self.sp_model_kwargs )
def _A ( self : List[str] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
UpperCamelCase :Union[str, Any] = Path(__lowerCamelCase )
assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
UpperCamelCase :Any = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
UpperCamelCase :str = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , __lowerCamelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __lowerCamelCase )
elif not os.path.isfile(self.spm_file ):
with open(__lowerCamelCase , """wb""" ) as fi:
UpperCamelCase :Any = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (str(__lowerCamelCase ), str(__lowerCamelCase ))
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
"""simple docstring"""
UpperCamelCase :int = sentencepiece.SentencePieceProcessor(**__magic_name__ )
spm.Load(str(__magic_name__ ) )
return spm
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> Union[Dict, List]:
"""simple docstring"""
with open(__magic_name__ , """r""" ) as f:
return json.load(__magic_name__ )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Dict , __magic_name__ : str ) -> None:
"""simple docstring"""
with open(__magic_name__ , """w""" ) as f:
json.dump(__magic_name__ , __magic_name__ , indent=2 )
| 590 | 0 |
import re
def lowerCamelCase_ ( lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
A = re.compile(r'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(lowerCAmelCase__ , lowerCAmelCase__ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('+918827897895')) | 106 | '''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase_ ( snake_case_ : str , snake_case_ : List[str]=10_00 ) -> Dict:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__lowerCAmelCase = n - 1
__lowerCAmelCase = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
__lowerCAmelCase = 0
while count < prec:
__lowerCAmelCase = random.randint(2 , n - 1 )
__lowerCAmelCase = bin_exp_mod(snake_case_ , snake_case_ , snake_case_ )
if b != 1:
__lowerCAmelCase = True
for _ in range(snake_case_ ):
if b == n - 1:
__lowerCAmelCase = False
break
__lowerCAmelCase = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
_A : Union[str, Any] = abs(int(input('''Enter bound : ''').strip()))
print('''Here\'s the list of primes:''')
print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 427 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : torch.FloatTensor
class lowerCAmelCase_ ( __magic_name__ ,__magic_name__ ):
@register_to_config
def __init__( self , _lowerCAmelCase = 3 , _lowerCAmelCase = 3 , _lowerCAmelCase = ("DownEncoderBlock2D",) , _lowerCAmelCase = ("UpDecoderBlock2D",) , _lowerCAmelCase = (64,) , _lowerCAmelCase = 1 , _lowerCAmelCase = "silu" , _lowerCAmelCase = 3 , _lowerCAmelCase = 32 , _lowerCAmelCase = 256 , _lowerCAmelCase = 32 , _lowerCAmelCase = None , _lowerCAmelCase = 0.18215 , _lowerCAmelCase = "group" , ) -> int:
super().__init__()
# pass init params to Encoder
_lowerCAmelCase = Encoder(
in_channels=_lowerCAmelCase , out_channels=_lowerCAmelCase , down_block_types=_lowerCAmelCase , block_out_channels=_lowerCAmelCase , layers_per_block=_lowerCAmelCase , act_fn=_lowerCAmelCase , norm_num_groups=_lowerCAmelCase , double_z=_lowerCAmelCase , )
_lowerCAmelCase = vq_embed_dim if vq_embed_dim is not None else latent_channels
_lowerCAmelCase = nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
_lowerCAmelCase = VectorQuantizer(_lowerCAmelCase , _lowerCAmelCase , beta=0.25 , remap=_lowerCAmelCase , sane_index_shape=_lowerCAmelCase )
_lowerCAmelCase = nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
# pass init params to Decoder
_lowerCAmelCase = Decoder(
in_channels=_lowerCAmelCase , out_channels=_lowerCAmelCase , up_block_types=_lowerCAmelCase , block_out_channels=_lowerCAmelCase , layers_per_block=_lowerCAmelCase , act_fn=_lowerCAmelCase , norm_num_groups=_lowerCAmelCase , norm_type=_lowerCAmelCase , )
@apply_forward_hook
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = True ) -> VQEncoderOutput:
_lowerCAmelCase = self.encoder(_lowerCAmelCase )
_lowerCAmelCase = self.quant_conv(_lowerCAmelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_lowerCAmelCase )
@apply_forward_hook
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = False , _lowerCAmelCase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
# also go through quantization layer
if not force_not_quantize:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.quantize(_lowerCAmelCase )
else:
_lowerCAmelCase = h
_lowerCAmelCase = self.post_quant_conv(_lowerCAmelCase )
_lowerCAmelCase = self.decoder(_lowerCAmelCase , quant if self.config.norm_type == "spatial" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = True ) -> Union[DecoderOutput, torch.FloatTensor]:
_lowerCAmelCase = sample
_lowerCAmelCase = self.encode(_lowerCAmelCase ).latents
_lowerCAmelCase = self.decode(_lowerCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCAmelCase )
| 489 |
'''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
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = "T5Config"
def __a(SCREAMING_SNAKE_CASE_ : jnp.array , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
_lowerCAmelCase = jnp.zeros_like(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
_lowerCAmelCase = shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = jnp.where(shifted_input_ids == -100 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return shifted_input_ids
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : List[str] = "mt5"
__lowerCamelCase : Any = MTaConfig
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : List[str] = "mt5"
__lowerCamelCase : Dict = MTaConfig
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : Optional[Any] = "mt5"
__lowerCamelCase : str = MTaConfig
| 489 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : List[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPriorConfig",
"JukeboxVQVAEConfig",
],
"tokenization_jukebox": ["JukeboxTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ : List[str] = [
"JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"JukeboxModel",
"JukeboxPreTrainedModel",
"JukeboxVQVAE",
"JukeboxPrior",
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
UpperCAmelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 48 |
from manim import *
class _a ( A__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =Rectangle(height=0.5 , width=0.5 )
_UpperCAmelCase =Rectangle(height=0.25 , width=0.25 )
_UpperCAmelCase =Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_UpperCAmelCase =[mem.copy() for i in range(6 )]
_UpperCAmelCase =[mem.copy() for i in range(6 )]
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =VGroup(_snake_case , _snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =Text("CPU" , font_size=24 )
_UpperCAmelCase =Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_snake_case )
_UpperCAmelCase =[mem.copy() for i in range(4 )]
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =Text("GPU" , font_size=24 )
_UpperCAmelCase =Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
gpu.move_to([-1, -1, 0] )
self.add(_snake_case )
_UpperCAmelCase =[mem.copy() for i in range(6 )]
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =Text("Model" , font_size=24 )
_UpperCAmelCase =Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
model.move_to([3, -1.0, 0] )
self.add(_snake_case )
_UpperCAmelCase =[]
_UpperCAmelCase =[]
_UpperCAmelCase =[]
for i, rect in enumerate(_snake_case ):
rect.set_stroke(_snake_case )
_UpperCAmelCase =Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(_snake_case , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_snake_case )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=_snake_case , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=_snake_case , buff=0.0 )
self.add(_snake_case )
model_cpu_arr.append(_snake_case )
self.add(*_snake_case , *_snake_case , *_snake_case )
_UpperCAmelCase =[mem.copy() for i in range(6 )]
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =Text("Loaded Checkpoint" , font_size=24 )
_UpperCAmelCase =Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
checkpoint.move_to([3, 0.5, 0] )
self.add(_snake_case )
_UpperCAmelCase =[]
_UpperCAmelCase =[]
for i, rect in enumerate(_snake_case ):
_UpperCAmelCase =fill.copy().set_fill(_snake_case , opacity=0.7 )
target.move_to(_snake_case )
ckpt_arr.append(_snake_case )
_UpperCAmelCase =target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(_snake_case )
self.add(*_snake_case , *_snake_case )
_UpperCAmelCase =Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_UpperCAmelCase =MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_snake_case , _snake_case )
_UpperCAmelCase =MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(_snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_snake_case )
_UpperCAmelCase =MarkupText(
F"Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device." , font_size=24 , )
step_a.move_to([2, 2, 0] )
_UpperCAmelCase =[meta_mem.copy() for i in range(6 )]
_UpperCAmelCase =[meta_mem.copy() for i in range(6 )]
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =VGroup(_snake_case , _snake_case ).arrange(_snake_case , buff=0 )
_UpperCAmelCase =Text("Disk" , font_size=24 )
_UpperCAmelCase =Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(_snake_case , run_time=3 ) , Write(_snake_case , run_time=1 ) , Create(_snake_case , run_time=1 ) )
_UpperCAmelCase =[]
for i, rect in enumerate(_snake_case ):
_UpperCAmelCase =rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(_snake_case , run_time=1.5 ) )
self.play(*_snake_case )
self.play(FadeOut(_snake_case ) )
_UpperCAmelCase =MarkupText(F"Then, the checkpoint is removed from memory\nthrough garbage collection." , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_snake_case , run_time=3 ) )
self.play(
FadeOut(_snake_case , _snake_case , *_snake_case , *_snake_case ) , )
self.wait()
| 408 | 0 |
import argparse
import datetime
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
_lowerCAmelCase : Any = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
_lowerCAmelCase : int = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(lowerCAmelCase__ ) < 11:
raise ValueError("Must be 10 characters long" )
# Get month
_lowerCAmelCase : int = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("Month must be between 1 - 12" )
_lowerCAmelCase : str = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get day
_lowerCAmelCase : int = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("Date must be between 1 - 31" )
# Get second separator
_lowerCAmelCase : str = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get year
_lowerCAmelCase : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 85_00:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?" )
# Get datetime obj for validation
_lowerCAmelCase : str = datetime.date(int(lowerCAmelCase__ ) , int(lowerCAmelCase__ ) , int(lowerCAmelCase__ ) )
# Start math
if m <= 2:
_lowerCAmelCase : Any = y - 1
_lowerCAmelCase : str = m + 12
# maths var
_lowerCAmelCase : int = int(str(lowerCAmelCase__ )[:2] )
_lowerCAmelCase : int = int(str(lowerCAmelCase__ )[2:] )
_lowerCAmelCase : int = int(2.6 * m - 5.39 )
_lowerCAmelCase : int = int(c / 4 )
_lowerCAmelCase : int = int(k / 4 )
_lowerCAmelCase : int = int(d + k )
_lowerCAmelCase : int = int(t + u + v + x )
_lowerCAmelCase : int = int(z - (2 * c) )
_lowerCAmelCase : int = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer." )
# Response
_lowerCAmelCase : str = f"""Your date {date_input}, is a {days[str(lowerCAmelCase__ )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
snake_case = parser.parse_args()
zeller(args.date_input)
| 587 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = "▁"
snake_case = {"vocab_file": "sentencepiece.bpe.model"}
snake_case = {
"vocab_file": {
"facebook/mbart-large-50-one-to-many-mmt": (
"https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
),
}
}
snake_case = {
"facebook/mbart-large-50-one-to-many-mmt": 1024,
}
# fmt: off
snake_case = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]
class __A ( snake_case__ ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = ['''input_ids''', '''attention_mask''']
a_ = []
a_ = []
def __init__( self , _snake_case , _snake_case=None , _snake_case=None , _snake_case="</s>" , _snake_case="</s>" , _snake_case="<s>" , _snake_case="<unk>" , _snake_case="<pad>" , _snake_case="<mask>" , _snake_case = None , **_snake_case , ):
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase : Dict = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else mask_token
_lowerCAmelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
_lowerCAmelCase : int = kwargs.get("additional_special_tokens" , [] )
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=_snake_case , tgt_lang=_snake_case , eos_token=_snake_case , unk_token=_snake_case , sep_token=_snake_case , cls_token=_snake_case , pad_token=_snake_case , mask_token=_snake_case , sp_model_kwargs=self.sp_model_kwargs , **_snake_case , )
_lowerCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_snake_case ) )
_lowerCAmelCase : Optional[int] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_lowerCAmelCase : Tuple = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : Union[str, Any] = len(self.sp_model )
_lowerCAmelCase : int = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_snake_case )
}
_lowerCAmelCase : Union[str, Any] = {v: k for k, v in self.lang_code_to_id.items()}
_lowerCAmelCase : List[str] = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_lowerCAmelCase : Any = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_lowerCAmelCase : List[Any] = src_lang if src_lang is not None else "en_XX"
_lowerCAmelCase : Optional[Any] = self.lang_code_to_id[self._src_lang]
_lowerCAmelCase : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : Optional[Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ):
_lowerCAmelCase : List[Any] = self.__dict__.copy()
_lowerCAmelCase : Dict = None
return state
def __setstate__( self , _snake_case ):
_lowerCAmelCase : Tuple = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
return self.sp_model.encode(_snake_case , out_type=_snake_case )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_lowerCAmelCase : Optional[Any] = self.sp_model.PieceToId(_snake_case )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : str = []
_lowerCAmelCase : Optional[Any] = ""
_lowerCAmelCase : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_snake_case ) + token
_lowerCAmelCase : int = True
_lowerCAmelCase : int = []
else:
current_sub_tokens.append(_snake_case )
_lowerCAmelCase : Any = False
out_string += self.sp_model.decode(_snake_case )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None ):
if not os.path.isdir(_snake_case ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCAmelCase : Union[str, Any] = 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(_snake_case , "wb" ) as fi:
_lowerCAmelCase : int = self.sp_model.serialized_model_proto()
fi.write(_snake_case )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None , _snake_case = False ):
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 : Any = [1] * len(self.prefix_tokens )
_lowerCAmelCase : Union[str, Any] = [1] * len(self.suffix_tokens )
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 SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# 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.suffix_tokens
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , **_snake_case ):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
_lowerCAmelCase : Dict = src_lang
_lowerCAmelCase : Optional[Any] = self(_snake_case , add_special_tokens=_snake_case , return_tensors=_snake_case , **_snake_case )
_lowerCAmelCase : Optional[int] = self.convert_tokens_to_ids(_snake_case )
_lowerCAmelCase : Optional[Any] = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = "en_XX" , _snake_case = None , _snake_case = "ro_RO" , **_snake_case , ):
_lowerCAmelCase : Union[str, Any] = src_lang
_lowerCAmelCase : List[str] = tgt_lang
return super().prepare_seqaseq_batch(_snake_case , _snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : List[str] = self.lang_code_to_id[src_lang]
_lowerCAmelCase : List[str] = [self.cur_lang_code_id]
_lowerCAmelCase : Optional[int] = [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : int = self.lang_code_to_id[tgt_lang]
_lowerCAmelCase : List[str] = [self.cur_lang_code_id]
_lowerCAmelCase : int = [self.eos_token_id]
| 587 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Dict = '''nllb-moe'''
UpperCamelCase : int = ['''past_key_values''']
UpperCamelCase : Optional[int] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Dict , UpperCAmelCase__ : List[Any]=128112 , UpperCAmelCase__ : Optional[Any]=1024 , UpperCAmelCase__ : Any=12 , UpperCAmelCase__ : int=4096 , UpperCAmelCase__ : Any=16 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : int=4096 , UpperCAmelCase__ : Dict=16 , UpperCAmelCase__ : Any=0.0_5 , UpperCAmelCase__ : Optional[Any]=0.0_5 , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Any="relu" , UpperCAmelCase__ : Optional[int]=1024 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Dict=0.0_2 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : str=False , UpperCAmelCase__ : Any="float32" , UpperCAmelCase__ : str=False , UpperCAmelCase__ : str=128 , UpperCAmelCase__ : Union[str, Any]=64 , UpperCAmelCase__ : int=4 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Optional[Any]=0.0_0_1 , UpperCAmelCase__ : Union[str, Any]=0.0_0_1 , UpperCAmelCase__ : Tuple="all" , UpperCAmelCase__ : Tuple=False , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Any=1.0 , UpperCAmelCase__ : int=0.2 , UpperCAmelCase__ : Optional[Any]=1 , UpperCAmelCase__ : Dict=0 , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : Any=False , **UpperCAmelCase__ : int , ) -> Any:
_a : str = vocab_size
_a : Dict = max_position_embeddings
_a : Optional[Any] = d_model
_a : Tuple = encoder_ffn_dim
_a : int = encoder_layers
_a : Optional[int] = encoder_attention_heads
_a : Optional[Any] = decoder_ffn_dim
_a : Tuple = decoder_layers
_a : int = decoder_attention_heads
_a : Tuple = dropout
_a : List[str] = attention_dropout
_a : Any = activation_dropout
_a : Optional[int] = activation_function
_a : Tuple = init_std
_a : Optional[int] = encoder_layerdrop
_a : List[str] = decoder_layerdrop
_a : List[Any] = use_cache
_a : List[Any] = encoder_layers
_a : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
_a : Tuple = router_z_loss_coef
_a : int = router_aux_loss_coef
_a : Union[str, Any] = decoder_sparse_step
_a : Dict = encoder_sparse_step
_a : List[Any] = num_experts
_a : Union[str, Any] = expert_capacity
_a : Dict = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" )
_a : Tuple = router_dtype
_a : Tuple = router_ignore_padding_tokens
_a : Optional[int] = batch_prioritized_routing
_a : Dict = second_expert_policy
_a : List[str] = normalize_router_prob_before_dropping
_a : List[Any] = moe_eval_capacity_token_fraction
_a : int = moe_token_dropout
_a : List[str] = output_router_logits
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , is_encoder_decoder=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 389 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Union[str, Any] = '''wav2vec2'''
def __init__( self : Tuple , UpperCAmelCase__ : str=32 , UpperCAmelCase__ : int=768 , UpperCAmelCase__ : Optional[Any]=12 , UpperCAmelCase__ : Union[str, Any]=12 , UpperCAmelCase__ : Optional[int]=3072 , UpperCAmelCase__ : List[Any]="gelu" , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Tuple=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Any=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : Dict=0.0_2 , UpperCAmelCase__ : List[Any]=1E-5 , UpperCAmelCase__ : Tuple="group" , UpperCAmelCase__ : Optional[Any]="gelu" , UpperCAmelCase__ : List[str]=(512, 512, 512, 512, 512, 512, 512) , UpperCAmelCase__ : Dict=(5, 2, 2, 2, 2, 2, 2) , UpperCAmelCase__ : Any=(10, 3, 3, 3, 3, 2, 2) , UpperCAmelCase__ : Any=False , UpperCAmelCase__ : Tuple=128 , UpperCAmelCase__ : int=16 , UpperCAmelCase__ : Tuple=False , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]=0.0_5 , UpperCAmelCase__ : List[Any]=10 , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : Optional[int]=0.0 , UpperCAmelCase__ : Optional[int]=10 , UpperCAmelCase__ : List[str]=0 , UpperCAmelCase__ : List[str]=320 , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : Tuple=0.1 , UpperCAmelCase__ : str=100 , UpperCAmelCase__ : Any=256 , UpperCAmelCase__ : Dict=256 , UpperCAmelCase__ : List[Any]=0.1 , UpperCAmelCase__ : Optional[int]="sum" , UpperCAmelCase__ : Any=False , UpperCAmelCase__ : List[Any]=False , UpperCAmelCase__ : Union[str, Any]=256 , UpperCAmelCase__ : Dict=(512, 512, 512, 512, 1500) , UpperCAmelCase__ : Optional[Any]=(5, 3, 3, 1, 1) , UpperCAmelCase__ : Optional[Any]=(1, 2, 3, 1, 1) , UpperCAmelCase__ : int=512 , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : List[str]=2 , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : int=3 , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : int , ) -> List[Any]:
super().__init__(**UpperCAmelCase__ , pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ )
_a : int = hidden_size
_a : Union[str, Any] = feat_extract_norm
_a : Any = feat_extract_activation
_a : int = list(UpperCAmelCase__ )
_a : List[str] = list(UpperCAmelCase__ )
_a : Optional[Any] = list(UpperCAmelCase__ )
_a : str = conv_bias
_a : Dict = num_conv_pos_embeddings
_a : List[Any] = num_conv_pos_embedding_groups
_a : List[str] = len(self.conv_dim )
_a : Tuple = num_hidden_layers
_a : List[str] = intermediate_size
_a : Any = hidden_act
_a : Union[str, Any] = num_attention_heads
_a : List[str] = hidden_dropout
_a : Optional[Any] = attention_dropout
_a : Dict = activation_dropout
_a : Optional[int] = feat_proj_dropout
_a : Optional[int] = final_dropout
_a : int = layerdrop
_a : Union[str, Any] = layer_norm_eps
_a : Optional[int] = initializer_range
_a : int = vocab_size
_a : Any = do_stable_layer_norm
_a : int = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_a : Optional[Any] = apply_spec_augment
_a : List[str] = mask_time_prob
_a : str = mask_time_length
_a : Optional[Any] = mask_time_min_masks
_a : Union[str, Any] = mask_feature_prob
_a : Tuple = mask_feature_length
_a : Tuple = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_a : List[str] = num_codevectors_per_group
_a : Any = num_codevector_groups
_a : List[str] = contrastive_logits_temperature
_a : Dict = feat_quantizer_dropout
_a : Dict = num_negatives
_a : Optional[Any] = codevector_dim
_a : Tuple = proj_codevector_dim
_a : str = diversity_loss_weight
# ctc loss
_a : Optional[int] = ctc_loss_reduction
_a : Union[str, Any] = ctc_zero_infinity
# adapter
_a : Union[str, Any] = add_adapter
_a : List[str] = adapter_kernel_size
_a : Dict = adapter_stride
_a : Dict = num_adapter_layers
_a : Any = output_hidden_size or hidden_size
_a : Union[str, Any] = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_a : Optional[int] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_a : Tuple = list(UpperCAmelCase__ )
_a : Any = list(UpperCAmelCase__ )
_a : Optional[Any] = list(UpperCAmelCase__ )
_a : int = xvector_output_dim
@property
def _lowercase ( self : List[Any] ) -> Optional[int]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 389 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : int = IFPipeline
__snake_case : Tuple = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
__snake_case : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
__snake_case : Any = PipelineTesterMixin.required_optional_params - {"latents"}
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
return self._get_dummy_components()
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[Any]=0 ) -> Any:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" ,reason="""float16 requires CUDA""" )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> List[str]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
self._test_save_load_local()
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 ,)
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" ,variant="""fp16""" ,torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" ,variant="""fp16""" ,torch_dtype=torch.floataa ,text_encoder=lowerCamelCase__ ,tokenizer=lowerCamelCase__ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = pipe_a.encode_prompt("""anime turtle""" ,device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
SCREAMING_SNAKE_CASE = IFImgaImgPipeline(**pipe_a.components )
SCREAMING_SNAKE_CASE = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
SCREAMING_SNAKE_CASE = IFInpaintingPipeline(**pipe_a.components )
SCREAMING_SNAKE_CASE = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[Any] ) -> int:
'''simple docstring'''
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe_a(
prompt_embeds=lowerCamelCase__ ,negative_prompt_embeds=lowerCamelCase__ ,num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = pipe_a(
prompt_embeds=lowerCamelCase__ ,negative_prompt_embeds=lowerCamelCase__ ,image=lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (256, 256, 3)
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe_a(
prompt_embeds=lowerCamelCase__ ,negative_prompt_embeds=lowerCamelCase__ ,image=lowerCamelCase__ ,num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 256, 256) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = pipe_a(
prompt_embeds=lowerCamelCase__ ,negative_prompt_embeds=lowerCamelCase__ ,image=lowerCamelCase__ ,original_image=lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (256, 256, 3)
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Any ) -> List[str]:
'''simple docstring'''
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) ,rng=random.Random(1 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe_a(
prompt_embeds=lowerCamelCase__ ,negative_prompt_embeds=lowerCamelCase__ ,image=lowerCamelCase__ ,mask_image=lowerCamelCase__ ,num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 256, 256) ,rng=random.Random(0 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 256, 256) ,rng=random.Random(1 ) ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = pipe_a(
prompt_embeds=lowerCamelCase__ ,negative_prompt_embeds=lowerCamelCase__ ,image=lowerCamelCase__ ,mask_image=lowerCamelCase__ ,original_image=lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (256, 256, 3)
SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
def __lowercase ( ) -> Tuple:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 116 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : int ,*lowerCamelCase__ : int ,**lowerCamelCase__ : List[Any] ) -> None:
'''simple docstring'''
warnings.warn(
"""The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use DeformableDetrImageProcessor instead.""" ,lowerCamelCase__ ,)
super().__init__(*lowerCamelCase__ ,**lowerCamelCase__ )
| 116 | 1 |
"""simple docstring"""
UpperCAmelCase_ : str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase_ : Any = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A (__a , __a , __a ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = True
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__a , __a , __a )
order.append(__a )
return order
def _A (__a , __a , __a ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = True
SCREAMING_SNAKE_CASE_ : Tuple = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__a , __a , __a )
return component
def _A (__a ) -> list[list[int]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = len(__a ) * [False]
SCREAMING_SNAKE_CASE_ : dict[int, list[int]] = {vert: [] for vert in range(len(__a ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__a )
SCREAMING_SNAKE_CASE_ : Tuple = []
for i, was_visited in enumerate(__a ):
if not was_visited:
order += topology_sort(__a , __a , __a )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
SCREAMING_SNAKE_CASE_ : List[Any] = len(__a ) * [False]
for i in range(len(__a ) ):
SCREAMING_SNAKE_CASE_ : Any = order[len(__a ) - i - 1]
if not visited[vert]:
SCREAMING_SNAKE_CASE_ : int = find_components(__a , __a , __a )
components_list.append(__a )
return components_list
| 512 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCAmelCase_ : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Dict , *lowercase_ : List[str] , **lowercase_ : Optional[int]):
'''simple docstring'''
warnings.warn(
'''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use SegformerImageProcessor instead.''' , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_)
| 512 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Union[str, Any] = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
'uclanlp/visualbert-vqa-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
'uclanlp/visualbert-vcr-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
),
'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
'uclanlp/visualbert-nlvr2-coco-pre': (
'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class UpperCamelCase( _a ):
snake_case_ : Any = """visual_bert"""
def __init__( self : str , SCREAMING_SNAKE_CASE : Dict=3_0_5_2_2 , SCREAMING_SNAKE_CASE : int=7_6_8 , SCREAMING_SNAKE_CASE : Dict=5_1_2 , SCREAMING_SNAKE_CASE : Union[str, Any]=1_2 , SCREAMING_SNAKE_CASE : List[str]=1_2 , SCREAMING_SNAKE_CASE : Optional[int]=3_0_7_2 , SCREAMING_SNAKE_CASE : List[str]="gelu" , SCREAMING_SNAKE_CASE : List[str]=0.1 , SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE : int=5_1_2 , SCREAMING_SNAKE_CASE : Optional[Any]=2 , SCREAMING_SNAKE_CASE : Optional[int]=0.02 , SCREAMING_SNAKE_CASE : Union[str, Any]=1e-1_2 , SCREAMING_SNAKE_CASE : str=False , SCREAMING_SNAKE_CASE : Tuple=True , SCREAMING_SNAKE_CASE : Dict=1 , SCREAMING_SNAKE_CASE : List[str]=0 , SCREAMING_SNAKE_CASE : Tuple=2 , **SCREAMING_SNAKE_CASE : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
__snake_case = vocab_size
__snake_case = max_position_embeddings
__snake_case = hidden_size
__snake_case = visual_embedding_dim
__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 = initializer_range
__snake_case = type_vocab_size
__snake_case = layer_norm_eps
__snake_case = bypass_transformer
__snake_case = special_visual_initialize
| 703 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase:
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[Any]=1_3 , SCREAMING_SNAKE_CASE : Optional[Any]=6_4 , SCREAMING_SNAKE_CASE : Union[str, Any]=2 , SCREAMING_SNAKE_CASE : Union[str, Any]=3 , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : Any=3_2 , SCREAMING_SNAKE_CASE : Any=5 , SCREAMING_SNAKE_CASE : List[Any]=4 , SCREAMING_SNAKE_CASE : Optional[int]=3_7 , SCREAMING_SNAKE_CASE : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE : Dict=0.1 , SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE : Optional[int]=1_0 , SCREAMING_SNAKE_CASE : int=0.02 , SCREAMING_SNAKE_CASE : Dict=[1, 1_6, 4, 4] , SCREAMING_SNAKE_CASE : List[str]=None , ) -> int:
'''simple docstring'''
__snake_case = parent
__snake_case = batch_size
__snake_case = image_size
__snake_case = patch_size
__snake_case = num_channels
__snake_case = is_training
__snake_case = use_labels
__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 = type_sequence_label_size
__snake_case = initializer_range
__snake_case = scope
__snake_case = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
__snake_case = (self.image_size // 3_2) ** 2
__snake_case = num_patches + 1
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case = None
if self.use_labels:
__snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
__snake_case = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [4, 8, 1_6, 3_2],
"num_groups": 2,
}
return ViTHybridConfig(
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=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=SCREAMING_SNAKE_CASE , )
def SCREAMING_SNAKE_CASE_ ( self : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str ) -> int:
'''simple docstring'''
__snake_case = ViTHybridModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__snake_case = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
'''simple docstring'''
__snake_case = self.type_sequence_label_size
__snake_case = ViTHybridForImageClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__snake_case = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase( _a , _a , unittest.TestCase ):
snake_case_ : Union[str, Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
snake_case_ : str = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
snake_case_ : Tuple = False
snake_case_ : Optional[Any] = False
snake_case_ : Dict = False
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case = ViTHybridModelTester(self )
__snake_case = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case = model_class(SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , nn.Linear ) )
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Optional[Any]:
'''simple docstring'''
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case = model_class(SCREAMING_SNAKE_CASE )
__snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case = [*signature.parameters.keys()]
__snake_case = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Dict:
'''simple docstring'''
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case = _config_zero_init(SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__snake_case = model_class(config=SCREAMING_SNAKE_CASE )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
__snake_case = [f'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Tuple:
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case = ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( ) -> List[str]:
'''simple docstring'''
__snake_case = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCamelCase( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> int:
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ) -> str:
'''simple docstring'''
__snake_case = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
SCREAMING_SNAKE_CASE )
__snake_case = self.default_image_processor
__snake_case = prepare_img()
__snake_case = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
__snake_case = model(**SCREAMING_SNAKE_CASE )
# verify the logits
__snake_case = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
__snake_case = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
@require_accelerate
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case = ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384" )
__snake_case = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384" , device_map="auto" )
__snake_case = prepare_img()
__snake_case = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="pt" )
__snake_case = model(**SCREAMING_SNAKE_CASE )
__snake_case = outputs.logits
# model predicts one of the 1000 ImageNet classes
__snake_case = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , "tabby, tabby cat" )
| 473 | 0 |
from math import factorial
def A__ ( snake_case_ : int , snake_case_ : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(snake_case_ ) // (factorial(snake_case_ ) * factorial(n - k ))
if __name__ == "__main__":
print(
'The number of five-card hands possible from a standard',
f'''fifty-two card deck is: {combinations(5_2, 5)}\n''',
)
print(
'If a class of 40 students must be arranged into groups of',
f'''4 for group projects, there are {combinations(4_0, 4)} ways''',
'to arrange them.\n',
)
print(
'If 10 teams are competing in a Formula One race, there',
f'''are {combinations(1_0, 3)} ways that first, second and''',
'third place can be awarded.',
)
| 64 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Dict=None , _UpperCamelCase : Dict=None , _UpperCamelCase : int=None , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : List[Any]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
SCREAMING_SNAKE_CASE = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_UpperCamelCase )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_UpperCamelCase )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_UpperCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class UpperCamelCase :
def __init__( self : Optional[Any] , snake_case__ : List[str] , snake_case__ : Any=1_3 , snake_case__ : List[str]=7 , snake_case__ : Optional[int]=True , snake_case__ : Tuple=False , snake_case__ : Optional[int]=9_9 , snake_case__ : List[str]=1_6 , snake_case__ : int=2 , snake_case__ : Optional[int]=4 , snake_case__ : str=4 , snake_case__ : Dict="relu" , snake_case__ : Tuple=0.1 , snake_case__ : Union[str, Any]=0.1 , snake_case__ : Any=0.0 , snake_case__ : Dict=0.0 , snake_case__ : Optional[Any]=2_0 , snake_case__ : int=2 , snake_case__ : Optional[Any]=1 , snake_case__ : Any=0 , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
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 = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = pad_token_id
SCREAMING_SNAKE_CASE = bos_token_id
def UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE = self.eos_token_id # Eos Token
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE = self.get_config()
SCREAMING_SNAKE_CASE = prepare_mam_aaa_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCamelCase ( self : int ):
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase ( self : Tuple , snake_case__ : List[str] , snake_case__ : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MaMaaaModel(config=snake_case__ ).get_decoder().to(snake_case__ ).eval()
SCREAMING_SNAKE_CASE = inputs_dict['input_ids']
SCREAMING_SNAKE_CASE = inputs_dict['attention_mask']
SCREAMING_SNAKE_CASE = inputs_dict['head_mask']
# first forward pass
SCREAMING_SNAKE_CASE = model(snake_case__ , attention_mask=snake_case__ , head_mask=snake_case__ , use_cache=snake_case__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE = model(snake_case__ , attention_mask=snake_case__ )['last_hidden_state']
SCREAMING_SNAKE_CASE = model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1E-2 ) )
def UpperCamelCase ( self : List[str] , snake_case__ : Any , snake_case__ : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MaMaaaModel(config=snake_case__ ).to(snake_case__ ).eval()
SCREAMING_SNAKE_CASE = model(**snake_case__ )
SCREAMING_SNAKE_CASE = outputs.encoder_last_hidden_state
SCREAMING_SNAKE_CASE = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = model.get_encoder()
encoder.save_pretrained(snake_case__ )
SCREAMING_SNAKE_CASE = MaMaaaEncoder.from_pretrained(snake_case__ ).to(snake_case__ )
SCREAMING_SNAKE_CASE = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = model.get_decoder()
decoder.save_pretrained(snake_case__ )
SCREAMING_SNAKE_CASE = MaMaaaDecoder.from_pretrained(snake_case__ ).to(snake_case__ )
SCREAMING_SNAKE_CASE = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=snake_case__ , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
__UpperCamelCase =(
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
__UpperCamelCase =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
__UpperCamelCase =(
{
"conversational": MaMaaaForConditionalGeneration,
"feature-extraction": MaMaaaModel,
"summarization": MaMaaaForConditionalGeneration,
"text2text-generation": MaMaaaForConditionalGeneration,
"translation": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
__UpperCamelCase =True
__UpperCamelCase =True
__UpperCamelCase =False
__UpperCamelCase =False
def UpperCamelCase ( self : Optional[Any] , snake_case__ : Any , snake_case__ : Tuple , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : Optional[Any] ):
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MaMaaaModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=snake_case__ )
def UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model_class.from_pretrained(snake_case__ , output_loading_info=snake_case__ )
self.assertEqual(info['missing_keys'] , [] )
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case__ )
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*snake_case__ )
def UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
SCREAMING_SNAKE_CASE = model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
SCREAMING_SNAKE_CASE = copy.deepcopy(self._prepare_for_class(snake_case__ , snake_case__ ) )
if not self.is_encoder_decoder:
SCREAMING_SNAKE_CASE = inputs['input_ids']
del inputs["input_ids"]
else:
SCREAMING_SNAKE_CASE = inputs['input_ids']
SCREAMING_SNAKE_CASE = inputs.get('decoder_input_ids' , snake_case__ )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , snake_case__ )
SCREAMING_SNAKE_CASE = model.get_input_embeddings()
if not self.is_encoder_decoder:
SCREAMING_SNAKE_CASE = wte(snake_case__ )
else:
SCREAMING_SNAKE_CASE = wte(snake_case__ )
SCREAMING_SNAKE_CASE = wte(snake_case__ )
with torch.no_grad():
model(**snake_case__ )[0]
def UpperCamelCase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = input_dict['input_ids']
SCREAMING_SNAKE_CASE = input_ids.ne(1 ).to(snake_case__ )
SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(snake_case__ ).eval().to(snake_case__ )
if torch_device == "cuda":
model.half()
model.generate(snake_case__ , attention_mask=snake_case__ )
model.generate(num_beams=4 , do_sample=snake_case__ , early_stopping=snake_case__ , num_return_sequences=3 )
def __lowerCAmelCase ( _UpperCamelCase : Tuple ) -> Dict:
'''simple docstring'''
return torch.tensor(_UpperCamelCase , dtype=torch.long , device=_UpperCamelCase )
a_ : Optional[int] = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class UpperCamelCase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self : Any ):
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(snake_case__ )
SCREAMING_SNAKE_CASE = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
SCREAMING_SNAKE_CASE = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
SCREAMING_SNAKE_CASE = prepare_mam_aaa_inputs_dict(model.config , snake_case__ , snake_case__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**snake_case__ )[0]
SCREAMING_SNAKE_CASE = torch.Size((1, 1_1, 1_0_2_4) )
self.assertEqual(output.shape , snake_case__ )
# change to expected output here
SCREAMING_SNAKE_CASE = torch.tensor(
[[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]] , device=snake_case__ )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case__ , atol=snake_case__ ) )
def UpperCamelCase ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(snake_case__ )
# change to intended input
SCREAMING_SNAKE_CASE = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
SCREAMING_SNAKE_CASE = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
SCREAMING_SNAKE_CASE = prepare_mam_aaa_inputs_dict(model.config , snake_case__ , snake_case__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**snake_case__ )[0]
SCREAMING_SNAKE_CASE = torch.Size((1, 1_1, model.config.vocab_size) )
self.assertEqual(output.shape , snake_case__ )
# change to expected output here
SCREAMING_SNAKE_CASE = torch.tensor(
[[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]] , device=snake_case__ )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case__ , atol=snake_case__ ) )
def UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(snake_case__ )
SCREAMING_SNAKE_CASE = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
SCREAMING_SNAKE_CASE = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
SCREAMING_SNAKE_CASE = tokenizer(snake_case__ , padding=snake_case__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model.generate(
input_ids=dct['input_ids'].to(snake_case__ ) , attention_mask=dct['attention_mask'].to(snake_case__ ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
SCREAMING_SNAKE_CASE = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=snake_case__ , skip_special_tokens=snake_case__ )
assert generated == expected_en
| 439 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InformerForPrediction""",
"""InformerModel""",
"""InformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 719 |
'''simple docstring'''
def A (__lowerCamelCase :list , __lowerCamelCase :list , __lowerCamelCase :int ):
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = [[0] * n for i in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
_lowerCAmelCase = y_points[i]
for i in range(2 , __lowerCamelCase ):
for j in range(__lowerCamelCase , __lowerCamelCase ):
_lowerCAmelCase = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 162 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class lowerCAmelCase__ :
def __init__( self , a , a=13 , a=30 , a=2 , a=3 , a=True , a=True , a=32 , a=2 , a=4 , a=37 , a="gelu" , a=0.1 , a=0.1 , a=10 , a=0.02 , a=3 , a=None , a=2 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = patch_size
_UpperCamelCase = num_channels
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_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 = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
_UpperCamelCase = (image_size // patch_size) ** 2
_UpperCamelCase = num_patches + 2
def A_ ( self ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def A_ ( self ) -> Union[str, Any]:
'''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=a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def A_ ( self , a , a , a ) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDeiTModel(config=a )
_UpperCamelCase = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self , a , a , a ) -> Any:
'''simple docstring'''
_UpperCamelCase = TFDeiTForMaskedImageModeling(config=a )
_UpperCamelCase = model(a )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_UpperCamelCase = 1
_UpperCamelCase = TFDeiTForMaskedImageModeling(a )
_UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCamelCase = model(a )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def A_ ( self , a , a , a ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.type_sequence_label_size
_UpperCamelCase = TFDeiTForImageClassification(a )
_UpperCamelCase = model(a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_UpperCamelCase = 1
_UpperCamelCase = TFDeiTForImageClassification(a )
_UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCamelCase = model(a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A_ ( self ) -> int:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase = config_and_inputs
_UpperCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase__ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Dict = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : int = (
{
"feature-extraction": TFDeiTModel,
"image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[str] = False
UpperCamelCase_ : List[Any] = False
def A_ ( self ) -> str:
'''simple docstring'''
_UpperCamelCase = TFDeiTModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37 )
def A_ ( self ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def A_ ( self ) -> Dict:
'''simple docstring'''
pass
def A_ ( self ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
_UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , tf.keras.layers.Dense ) )
def A_ ( self ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(a )
_UpperCamelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def A_ ( self ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def A_ ( self ) -> str:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a )
def A_ ( self ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a )
def A_ ( self , a , a , a=False ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def A_ ( self ) -> str:
'''simple docstring'''
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDeiTModel.from_pretrained(a )
self.assertIsNotNone(a )
def __A() -> Any:
"""simple docstring"""
_UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
@cached_property
def A_ ( self ) -> List[str]:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def A_ ( self ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=a , return_tensors="""tf""" )
# forward pass
_UpperCamelCase = model(**a )
# verify the logits
_UpperCamelCase = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , a )
_UpperCamelCase = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
| 612 |
UpperCAmelCase__ : Dict = tuple[float, float, float]
UpperCAmelCase__ : Tuple = tuple[float, float, float]
def _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Vectorad:
UpperCamelCase__ : Optional[int] = end_pointa[0] - end_pointa[0]
UpperCamelCase__ : Tuple = end_pointa[1] - end_pointa[1]
UpperCamelCase__ : Dict = end_pointa[2] - end_pointa[2]
return (x, y, z)
def _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Vectorad:
UpperCamelCase__ : Optional[int] = ab[1] * ac[2] - ab[2] * ac[1] # *i
UpperCamelCase__ : Any = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
UpperCamelCase__ : Any = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> bool:
return tuple(round(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for x in vector ) == (0, 0, 0)
def _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 10 ) -> bool:
UpperCamelCase__ : str = create_vector(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = create_vector(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return is_zero_vector(get_ad_vectors_cross(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
| 410 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
__magic_name__ = logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
_A : List[str] = 'sequence-classification'
def __init__( self , lowerCamelCase ):
if type(lowerCamelCase ) == dict:
snake_case__ = Namespace(**lowerCamelCase )
snake_case__ = glue_output_modes[hparams.task]
snake_case__ = glue_tasks_num_labels[hparams.task]
super().__init__(lowerCamelCase , lowerCamelCase , self.mode )
def A_ ( self , **lowerCamelCase ):
return self.model(**lowerCamelCase )
def A_ ( self , lowerCamelCase , lowerCamelCase ):
snake_case__ = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
snake_case__ = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
snake_case__ = self(**lowerCamelCase )
snake_case__ = outputs[0]
snake_case__ = self.trainer.lr_schedulers[0]["scheduler"]
snake_case__ = {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def A_ ( self ):
snake_case__ = self.hparams
snake_case__ = processors[args.task]()
snake_case__ = processor.get_labels()
for mode in ["train", "dev"]:
snake_case__ = self._feature_file(lowerCamelCase )
if os.path.exists(lowerCamelCase ) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" , lowerCamelCase )
else:
logger.info("Creating features from dataset file at %s" , args.data_dir )
snake_case__ = (
processor.get_dev_examples(args.data_dir )
if mode == "dev"
else processor.get_train_examples(args.data_dir )
)
snake_case__ = convert_examples_to_features(
lowerCamelCase , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info("Saving features into cached file %s" , lowerCamelCase )
torch.save(lowerCamelCase , lowerCamelCase )
def A_ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = False ):
snake_case__ = "dev" if mode == "test" else mode
snake_case__ = self._feature_file(lowerCamelCase )
logger.info("Loading features from cached file %s" , lowerCamelCase )
snake_case__ = torch.load(lowerCamelCase )
snake_case__ = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
snake_case__ = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
snake_case__ = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
snake_case__ = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
snake_case__ = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , batch_size=lowerCamelCase , shuffle=lowerCamelCase , )
def A_ ( self , lowerCamelCase , lowerCamelCase ):
snake_case__ = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
snake_case__ = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
snake_case__ = self(**lowerCamelCase )
snake_case__ , snake_case__ = outputs[:2]
snake_case__ = logits.detach().cpu().numpy()
snake_case__ = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def A_ ( self , lowerCamelCase ):
snake_case__ = torch.stack([x["val_loss"] for x in outputs] ).mean().detach().cpu().item()
snake_case__ = np.concatenate([x["pred"] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
snake_case__ = np.argmax(lowerCamelCase , axis=1 )
elif self.hparams.glue_output_mode == "regression":
snake_case__ = np.squeeze(lowerCamelCase )
snake_case__ = np.concatenate([x["target"] for x in outputs] , axis=0 )
snake_case__ = [[] for _ in range(out_label_ids.shape[0] )]
snake_case__ = [[] for _ in range(out_label_ids.shape[0] )]
snake_case__ = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task , lowerCamelCase , lowerCamelCase )}
snake_case__ = dict(results.items() )
snake_case__ = results
return ret, preds_list, out_label_list
def A_ ( self , lowerCamelCase ):
snake_case__ , snake_case__ , snake_case__ = self._eval_end(lowerCamelCase )
snake_case__ = ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def A_ ( self , lowerCamelCase ):
snake_case__ , snake_case__ , snake_case__ = self._eval_end(lowerCamelCase )
snake_case__ = ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def A_ ( lowerCamelCase , lowerCamelCase ):
BaseTransformer.add_model_specific_args(lowerCamelCase , lowerCamelCase )
parser.add_argument(
"--max_seq_length" , default=1_28 , type=lowerCamelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--task" , default="" , type=lowerCamelCase , required=lowerCamelCase , help="The GLUE task to run" , )
parser.add_argument(
"--gpus" , default=0 , type=lowerCamelCase , help="The number of GPUs allocated for this, it is by default 0 meaning none" , )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
return parser
def SCREAMING_SNAKE_CASE__ ( ):
snake_case__ = argparse.ArgumentParser()
add_generic_args(__lowerCAmelCase , os.getcwd() )
snake_case__ = GLUETransformer.add_model_specific_args(__lowerCAmelCase , os.getcwd() )
snake_case__ = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
snake_case__ = os.path.join(
"./results" , F"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}""" , )
os.makedirs(args.output_dir )
snake_case__ = GLUETransformer(__lowerCAmelCase )
snake_case__ = generic_train(__lowerCAmelCase , __lowerCAmelCase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
snake_case__ = sorted(glob.glob(os.path.join(args.output_dir , "checkpoint-epoch=*.ckpt" ) , recursive=__lowerCAmelCase ) )
snake_case__ = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(__lowerCAmelCase )
if __name__ == "__main__":
main()
| 530 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase = 100 ):
return sum(int(__lowerCAmelCase ) for x in str(factorial(__lowerCAmelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 530 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = iter(lowerCAmelCase_ )
while True:
__SCREAMING_SNAKE_CASE = tuple(itertools.islice(lowerCAmelCase_ , lowerCAmelCase_ ) )
if not chunk:
return
yield chunk
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "".join([c.upper() for c in dirty if c in string.ascii_letters] )
__SCREAMING_SNAKE_CASE = ""
if len(lowerCAmelCase_ ) < 2:
return dirty
for i in range(len(lowerCAmelCase_ ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(lowerCAmelCase_ ) & 1:
clean += "X"
return clean
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
__SCREAMING_SNAKE_CASE = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(lowerCAmelCase_ )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(lowerCAmelCase_ )
return table
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = generate_table(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = prepare_input(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowerCAmelCase_ , 2 ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(table.index(lowerCAmelCase_ ) , 5 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(table.index(lowerCAmelCase_ ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = generate_table(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowerCAmelCase_ , 2 ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(table.index(lowerCAmelCase_ ) , 5 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(table.index(lowerCAmelCase_ ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 682 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
a__ : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase__ : Union[str, Any]=None , **UpperCAmelCase__ : Union[str, Any] ) -> Any:
warnings.warn(
"`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
"instead." , UpperCAmelCase__ , )
super().__init__(args=UpperCAmelCase__ , **UpperCAmelCase__ )
| 682 | 1 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (snake_case__ ):
"""simple docstring"""
__a =CLIPConfig
__a =['CLIPEncoderLayer']
def __init__( self : Union[str, Any] , __a : Tuple ):
super().__init__(lowercase_ )
_a = CLIPVisionModelWithProjection(config.vision_config )
_a = nn.Linear(config.vision_config.projection_dim , 1 )
_a = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def UpperCamelCase__ ( self : Dict , __a : Any , __a : List[Any] , __a : Optional[int]=0.5 , __a : Any=0.5 ):
_a = self.vision_model(lowercase_ )[0]
_a = self.p_head(lowercase_ )
_a = nsfw_detected.flatten()
_a = nsfw_detected > p_threshold
_a = nsfw_detected.tolist()
if any(lowercase_ ):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, nsfw_detected_ in enumerate(lowercase_ ):
if nsfw_detected_:
_a = np.zeros(images[idx].shape )
_a = self.w_head(lowercase_ )
_a = watermark_detected.flatten()
_a = watermark_detected > w_threshold
_a = watermark_detected.tolist()
if any(lowercase_ ):
logger.warning(
"Potential watermarked content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, watermark_detected_ in enumerate(lowercase_ ):
if watermark_detected_:
_a = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 704 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def _lowerCamelCase ( lowercase : List[str] ) -> List[str]:
_a = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
"`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
F'{test_file} instead.' )
_a = components[-1]
if not test_fn.endswith("py" ):
raise ValueError(F'`test_file` should be a python file. Got {test_fn} instead.' )
if not test_fn.startswith("test_modeling_" ):
raise ValueError(
F'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.' )
_a = components[:-1] + [test_fn.replace(".py" , "" )]
_a = ".".join(lowercase )
return test_module_path
def _lowerCamelCase ( lowercase : List[str] ) -> Optional[Any]:
_a = get_module_path(lowercase )
_a = importlib.import_module(lowercase )
return test_module
def _lowerCamelCase ( lowercase : Optional[Any] ) -> List[str]:
_a = []
_a = get_test_module(lowercase )
for attr in dir(lowercase ):
if attr.endswith("ModelTester" ):
tester_classes.append(getattr(lowercase , lowercase ) )
# sort with class names
return sorted(lowercase , key=lambda lowercase : x.__name__ )
def _lowerCamelCase ( lowercase : List[str] ) -> Any:
_a = []
_a = get_test_module(lowercase )
for attr in dir(lowercase ):
_a = getattr(lowercase , lowercase )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
_a = getattr(lowercase , "all_model_classes" , [] )
if len(lowercase ) > 0:
test_classes.append(lowercase )
# sort with class names
return sorted(lowercase , key=lambda lowercase : x.__name__ )
def _lowerCamelCase ( lowercase : int ) -> str:
_a = get_test_classes(lowercase )
_a = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(lowercase , key=lambda lowercase : x.__name__ )
def _lowerCamelCase ( lowercase : Optional[Any] ) -> Dict:
_a = test_class()
if hasattr(lowercase , "setUp" ):
test.setUp()
_a = None
if hasattr(lowercase , "model_tester" ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
_a = test.model_tester.__class__
return model_tester
def _lowerCamelCase ( lowercase : str , lowercase : Dict ) -> str:
_a = get_test_classes(lowercase )
_a = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(lowercase )
# sort with class names
return sorted(lowercase , key=lambda lowercase : x.__name__ )
def _lowerCamelCase ( lowercase : Dict , lowercase : Union[str, Any] ) -> Dict:
_a = get_test_classes_for_model(lowercase , lowercase )
_a = []
for test_class in test_classes:
_a = get_model_tester_from_test_class(lowercase )
if tester_class is not None:
tester_classes.append(lowercase )
# sort with class names
return sorted(lowercase , key=lambda lowercase : x.__name__ )
def _lowerCamelCase ( lowercase : List[Any] ) -> Tuple:
_a = get_test_classes(lowercase )
_a = {test_class: get_model_tester_from_test_class(lowercase ) for test_class in test_classes}
return test_tester_mapping
def _lowerCamelCase ( lowercase : List[str] ) -> Union[str, Any]:
_a = get_model_classes(lowercase )
_a = {
model_class: get_test_classes_for_model(lowercase , lowercase ) for model_class in model_classes
}
return model_test_mapping
def _lowerCamelCase ( lowercase : Optional[Any] ) -> str:
_a = get_model_classes(lowercase )
_a = {
model_class: get_tester_classes_for_model(lowercase , lowercase ) for model_class in model_classes
}
return model_to_tester_mapping
def _lowerCamelCase ( lowercase : List[str] ) -> Tuple:
if isinstance(lowercase , lowercase ):
return o
elif isinstance(lowercase , lowercase ):
return o.__name__
elif isinstance(lowercase , (list, tuple) ):
return [to_json(lowercase ) for x in o]
elif isinstance(lowercase , lowercase ):
return {to_json(lowercase ): to_json(lowercase ) for k, v in o.items()}
else:
return o
| 521 | 0 |
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=A__ ):
A = ['keras_nlp']
def __init__( self : Tuple,*_A : List[Any],**_A : List[Any] ):
"""simple docstring"""
requires_backends(self,["keras_nlp"] )
| 216 | from scipy.stats import pearsonr
import datasets
__lowerCamelCase : Union[str, Any] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
__lowerCamelCase : Optional[int] = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
__lowerCamelCase : Tuple = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def __UpperCamelCase ( self : int ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ),reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"],)
def __UpperCamelCase ( self : int,_A : List[str],_A : Optional[int],_A : int=False ):
"""simple docstring"""
if return_pvalue:
SCREAMING_SNAKE_CASE_ : Any = pearsonr(_A,_A )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(_A,_A )[0] )}
| 216 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
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""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
UpperCAmelCase_ =[
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def UpperCAmelCase ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
for attribute in key.split('''.''' ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
lowerCAmelCase = '''lm_head'''
lowerCAmelCase = getattr(_snake_case , _snake_case )
if weight_type is not None:
lowerCAmelCase = getattr(_snake_case , _snake_case ).shape
else:
lowerCAmelCase = 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":
lowerCAmelCase = value
elif weight_type == "weight_g":
lowerCAmelCase = value
elif weight_type == "weight_v":
lowerCAmelCase = value
elif weight_type == "bias":
lowerCAmelCase = value
else:
lowerCAmelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def UpperCAmelCase ( _snake_case , _snake_case , _snake_case ):
lowerCAmelCase = []
lowerCAmelCase = fairseq_model.state_dict()
lowerCAmelCase = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == '''group''' , )
lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
lowerCAmelCase = '''unispeech.''' + 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]:
lowerCAmelCase = True
if "*" in mapped_key:
lowerCAmelCase = name.split(_snake_case )[0].split('''.''' )[-2]
lowerCAmelCase = mapped_key.replace('''*''' , _snake_case )
if "weight_g" in name:
lowerCAmelCase = '''weight_g'''
elif "weight_v" in name:
lowerCAmelCase = '''weight_v'''
elif "bias" in name:
lowerCAmelCase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCAmelCase = '''weight'''
else:
lowerCAmelCase = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F"""Unused weights: {unused_weights}""" )
def UpperCAmelCase ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
lowerCAmelCase = full_name.split('''conv_layers.''' )[-1]
lowerCAmelCase = name.split('''.''' )
lowerCAmelCase = int(items[0] )
lowerCAmelCase = 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."""
)
lowerCAmelCase = 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."""
)
lowerCAmelCase = 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."
)
lowerCAmelCase = 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."""
)
lowerCAmelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_snake_case )
@torch.no_grad()
def UpperCAmelCase ( _snake_case , _snake_case , _snake_case=None , _snake_case=None , _snake_case=True ):
if config_path is not None:
lowerCAmelCase = UniSpeechConfig.from_pretrained(_snake_case )
else:
lowerCAmelCase = UniSpeechConfig()
if is_finetuned:
if dict_path:
lowerCAmelCase = Dictionary.load_from_json(_snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCAmelCase = target_dict.pad_index
lowerCAmelCase = target_dict.bos_index
lowerCAmelCase = target_dict.eos_index
lowerCAmelCase = len(target_dict.symbols )
lowerCAmelCase = os.path.join(_snake_case , '''vocab.json''' )
if not os.path.isdir(_snake_case ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCAmelCase = 42
lowerCAmelCase = 43
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(_snake_case , _snake_case )
lowerCAmelCase = WavaVecaPhonemeCTCTokenizer(
_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=_snake_case , )
lowerCAmelCase = True if config.feat_extract_norm == '''layer''' else False
lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
lowerCAmelCase = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
lowerCAmelCase = UniSpeechForCTC(_snake_case )
else:
lowerCAmelCase = UniSpeechForPreTraining(_snake_case )
if is_finetuned:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path} )
else:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
lowerCAmelCase = model[0].eval()
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_unispeech.save_pretrained(_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_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 716 |
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 __UpperCamelCase ( __UpperCAmelCase ):
'''simple docstring'''
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = True , UpperCAmelCase_ = None , UpperCAmelCase_ = False , UpperCAmelCase_ = None , UpperCAmelCase_ = True , UpperCAmelCase_ = "arrow" , **UpperCAmelCase_ , ):
super().__init__(
split=UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , keep_in_memory=UpperCAmelCase_ , streaming=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowerCAmelCase = load_from_cache_file
lowerCAmelCase = file_format
lowerCAmelCase = Spark(
df=UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , working_dir=UpperCAmelCase_ , **UpperCAmelCase_ , )
def __snake_case ( self ):
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowerCAmelCase = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=UpperCAmelCase_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 33 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Tuple = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : str = [
"GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GitForCausalLM",
"GitModel",
"GitPreTrainedModel",
"GitVisionModel",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85 | import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def lowerCAmelCase( __lowerCamelCase ):
__a = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
f'''{test_file} instead.''' )
__a = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.''' )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.''' )
__a = components[:-1] + [test_fn.replace('.py' , '' )]
__a = '.'.join(__lowerCamelCase )
return test_module_path
def lowerCAmelCase( __lowerCamelCase ):
__a = get_module_path(__lowerCamelCase )
__a = importlib.import_module(__lowerCamelCase )
return test_module
def lowerCAmelCase( __lowerCamelCase ):
__a = []
__a = get_test_module(__lowerCamelCase )
for attr in dir(__lowerCamelCase ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(__lowerCamelCase , __lowerCamelCase ) )
# sort with class names
return sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x.__name__ )
def lowerCAmelCase( __lowerCamelCase ):
__a = []
__a = get_test_module(__lowerCamelCase )
for attr in dir(__lowerCamelCase ):
__a = getattr(__lowerCamelCase , __lowerCamelCase )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
__a = getattr(__lowerCamelCase , 'all_model_classes' , [] )
if len(__lowerCamelCase ) > 0:
test_classes.append(__lowerCamelCase )
# sort with class names
return sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x.__name__ )
def lowerCAmelCase( __lowerCamelCase ):
__a = get_test_classes(__lowerCamelCase )
__a = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x.__name__ )
def lowerCAmelCase( __lowerCamelCase ):
__a = test_class()
if hasattr(__lowerCamelCase , 'setUp' ):
test.setUp()
__a = None
if hasattr(__lowerCamelCase , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
__a = test.model_tester.__class__
return model_tester
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
__a = get_test_classes(__lowerCamelCase )
__a = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(__lowerCamelCase )
# sort with class names
return sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x.__name__ )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
__a = get_test_classes_for_model(__lowerCamelCase , __lowerCamelCase )
__a = []
for test_class in test_classes:
__a = get_model_tester_from_test_class(__lowerCamelCase )
if tester_class is not None:
tester_classes.append(__lowerCamelCase )
# sort with class names
return sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x.__name__ )
def lowerCAmelCase( __lowerCamelCase ):
__a = get_test_classes(__lowerCamelCase )
__a = {test_class: get_model_tester_from_test_class(__lowerCamelCase ) for test_class in test_classes}
return test_tester_mapping
def lowerCAmelCase( __lowerCamelCase ):
__a = get_model_classes(__lowerCamelCase )
__a = {
model_class: get_test_classes_for_model(__lowerCamelCase , __lowerCamelCase ) for model_class in model_classes
}
return model_test_mapping
def lowerCAmelCase( __lowerCamelCase ):
__a = get_model_classes(__lowerCamelCase )
__a = {
model_class: get_tester_classes_for_model(__lowerCamelCase , __lowerCamelCase ) for model_class in model_classes
}
return model_to_tester_mapping
def lowerCAmelCase( __lowerCamelCase ):
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return o
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
return o.__name__
elif isinstance(__lowerCamelCase , (list, tuple) ):
return [to_json(__lowerCamelCase ) for x in o]
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
return {to_json(__lowerCamelCase ): to_json(__lowerCamelCase ) for k, v in o.items()}
else:
return o
| 559 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################
lowerCamelCase__ : List[str] = 1_6
lowerCamelCase__ : Optional[Any] = 3_2
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = 16 ) -> Optional[Any]:
'''simple docstring'''
lowercase__ : List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase__ : List[str] = DatasetDict(
{
"""train""": dataset["""train"""].select(lowercase_ ),
"""validation""": dataset["""train"""].select(lowercase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowercase_ ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : str = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase_ , max_length=lowercase_ )
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():
lowercase__ : Optional[int] = datasets.map(
lowercase_ , batched=lowercase_ , 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
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : Any = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : Optional[int] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : Tuple = 8
else:
lowercase__ : Optional[Any] = None
return tokenizer.pad(
lowercase_ , padding="""longest""" , max_length=lowercase_ , pad_to_multiple_of=lowercase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase__ : str = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
lowercase__ : str = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
lowercase__ : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
return train_dataloader, eval_dataloader, test_dataloader
def UpperCamelCase ( lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
lowercase__ : Any = []
# Download the dataset
lowercase__ : Tuple = load_dataset("""glue""" , """mrpc""" )
# Create our splits
lowercase__ : List[str] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
lowercase__ : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[Any] = config["""lr"""]
lowercase__ : Union[str, Any] = int(config["""num_epochs"""] )
lowercase__ : str = int(config["""seed"""] )
lowercase__ : str = int(config["""batch_size"""] )
lowercase__ : List[str] = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowercase__ : Dict = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase__ : int = batch_size // MAX_GPU_BATCH_SIZE
lowercase__ : List[str] = MAX_GPU_BATCH_SIZE
set_seed(lowercase_ )
# New Code #
# Create our folds:
lowercase__ : Optional[Any] = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
lowercase__ : Optional[int] = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowercase_ ):
lowercase__ , lowercase__ , lowercase__ : Optional[int] = get_fold_dataloaders(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase_ )
# 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).
lowercase__ : int = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Any = AdamW(params=model.parameters() , lr=lowercase_ )
# Instantiate scheduler
lowercase__ : Dict = get_linear_schedule_with_warmup(
optimizer=lowercase_ , num_warmup_steps=1_00 , num_training_steps=(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps , )
# 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.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = accelerator.prepare(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Now we train the model
for epoch in range(lowercase_ ):
model.train()
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : List[str] = model(**lowercase_ )
lowercase__ : Dict = outputs.loss
lowercase__ : List[Any] = loss / gradient_accumulation_steps
accelerator.backward(lowercase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : int = model(**lowercase_ )
lowercase__ : Any = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : Any = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase_ , references=lowercase_ , )
lowercase__ : Optional[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , lowercase_ )
# New Code #
# We also run predictions on the test set at the very end
lowercase__ : Dict = []
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**lowercase_ )
lowercase__ : str = outputs.logits
lowercase__ , lowercase__ : List[str] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowercase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
lowercase__ : Dict = torch.cat(lowercase_ , dim=0 )
lowercase__ : Union[str, Any] = torch.stack(lowercase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
lowercase__ : Any = metric.compute(predictions=lowercase_ , references=lowercase_ )
accelerator.print("""Average test metrics from all folds:""" , lowercase_ )
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
lowercase__ : Optional[int] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase_ , default=lowercase_ , 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.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowercase_ , default=3 , help="""The number of splits to perform across the dataset""" )
lowercase__ : Any = parser.parse_args()
lowercase__ : Union[str, Any] = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowercase_ , lowercase_ )
if __name__ == "__main__":
main()
| 495 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _snake_case :
def __init__( self , SCREAMING_SNAKE_CASE_ = None):
'''simple docstring'''
if components is None:
lowercase__ : List[str] = []
lowercase__ : Dict = list(SCREAMING_SNAKE_CASE_)
def __len__( self):
'''simple docstring'''
return len(self.__components)
def __str__( self):
'''simple docstring'''
return "(" + ",".join(map(SCREAMING_SNAKE_CASE_ , self.__components)) + ")"
def __add__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Optional[Any] = len(self)
if size == len(SCREAMING_SNAKE_CASE_):
lowercase__ : List[str] = [self.__components[i] + other.component(SCREAMING_SNAKE_CASE_) for i in range(SCREAMING_SNAKE_CASE_)]
return Vector(SCREAMING_SNAKE_CASE_)
else:
raise Exception("""must have the same size""")
def __sub__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : List[Any] = len(self)
if size == len(SCREAMING_SNAKE_CASE_):
lowercase__ : Optional[Any] = [self.__components[i] - other.component(SCREAMING_SNAKE_CASE_) for i in range(SCREAMING_SNAKE_CASE_)]
return Vector(SCREAMING_SNAKE_CASE_)
else: # error case
raise Exception("""must have the same size""")
@overload
def __mul__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
...
@overload
def __mul__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
...
def __mul__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , (float, int)):
lowercase__ : Optional[int] = [c * other for c in self.__components]
return Vector(SCREAMING_SNAKE_CASE_)
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_) and len(self) == len(SCREAMING_SNAKE_CASE_):
lowercase__ : Dict = len(self)
lowercase__ : Optional[Any] = [self.__components[i] * other.component(SCREAMING_SNAKE_CASE_) for i in range(SCREAMING_SNAKE_CASE_)]
return sum(SCREAMING_SNAKE_CASE_)
else: # error case
raise Exception("""invalid operand!""")
def lowercase__ ( self):
'''simple docstring'''
return Vector(self.__components)
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_) and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("""index out of range""")
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
assert -len(self.__components) <= pos < len(self.__components)
lowercase__ : List[Any] = value
def lowercase__ ( self):
'''simple docstring'''
if len(self.__components) == 0:
raise Exception("""Vector is empty""")
lowercase__ : Union[str, Any] = [c**2 for c in self.__components]
return math.sqrt(sum(SCREAMING_SNAKE_CASE_))
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False):
'''simple docstring'''
lowercase__ : Union[str, Any] = self * other
lowercase__ : Optional[Any] = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den))
else:
return math.acos(num / den)
def UpperCamelCase ( lowercase_ ) -> Vector:
'''simple docstring'''
assert isinstance(lowercase_ , lowercase_ )
return Vector([0] * dimension )
def UpperCamelCase ( lowercase_ , lowercase_ ) -> Vector:
'''simple docstring'''
assert isinstance(lowercase_ , lowercase_ ) and (isinstance(lowercase_ , lowercase_ ))
lowercase__ : Union[str, Any] = [0] * dimension
lowercase__ : Any = 1
return Vector(lowercase_ )
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> Vector:
'''simple docstring'''
assert (
isinstance(lowercase_ , lowercase_ )
and isinstance(lowercase_ , lowercase_ )
and (isinstance(lowercase_ , (int, float) ))
)
return x * scalar + y
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> Vector:
'''simple docstring'''
random.seed(lowercase_ )
lowercase__ : int = [random.randint(lowercase_ , lowercase_ ) for _ in range(lowercase_ )]
return Vector(lowercase_ )
class _snake_case :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : List[Any] = matrix
lowercase__ : Any = w
lowercase__ : Any = h
def __str__( self):
'''simple docstring'''
lowercase__ : str = """"""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def __add__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
lowercase__ : Tuple = []
for i in range(self.__height):
lowercase__ : Tuple = [
self.__matrix[i][j] + other.component(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
for j in range(self.__width)
]
matrix.append(SCREAMING_SNAKE_CASE_)
return Matrix(SCREAMING_SNAKE_CASE_ , self.__width , self.__height)
else:
raise Exception("""matrix must have the same dimension!""")
def __sub__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
lowercase__ : Optional[int] = []
for i in range(self.__height):
lowercase__ : List[str] = [
self.__matrix[i][j] - other.component(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
for j in range(self.__width)
]
matrix.append(SCREAMING_SNAKE_CASE_)
return Matrix(SCREAMING_SNAKE_CASE_ , self.__width , self.__height)
else:
raise Exception("""matrices must have the same dimension!""")
@overload
def __mul__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
...
@overload
def __mul__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
...
def __mul__( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_): # matrix-vector
if len(SCREAMING_SNAKE_CASE_) == self.__width:
lowercase__ : List[Any] = zero_vector(self.__height)
for i in range(self.__height):
lowercase__ : Union[str, Any] = [
self.__matrix[i][j] * other.component(SCREAMING_SNAKE_CASE_)
for j in range(self.__width)
]
ans.change_component(SCREAMING_SNAKE_CASE_ , sum(SCREAMING_SNAKE_CASE_))
return ans
else:
raise Exception(
"""vector must have the same size as the """
"""number of columns of the matrix!""")
elif isinstance(SCREAMING_SNAKE_CASE_ , (int, float)): # matrix-scalar
lowercase__ : Tuple = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(SCREAMING_SNAKE_CASE_ , self.__width , self.__height)
return None
def lowercase__ ( self):
'''simple docstring'''
return self.__height
def lowercase__ ( self):
'''simple docstring'''
return self.__width
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("""change_component: indices out of bounds""")
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
lowercase__ : Tuple = value
else:
raise Exception("""change_component: indices out of bounds""")
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if self.__height != self.__width:
raise Exception("""Matrix is not square""")
lowercase__ : List[Any] = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(SCREAMING_SNAKE_CASE_)):
lowercase__ : List[str] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(SCREAMING_SNAKE_CASE_ , self.__width - 1 , self.__height - 1).determinant()
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if self.__height != self.__width:
raise Exception("""Matrix is not square""")
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
else:
raise Exception("""Indices out of bounds""")
def lowercase__ ( self):
'''simple docstring'''
if self.__height != self.__width:
raise Exception("""Matrix is not square""")
if self.__height < 1:
raise Exception("""Matrix has no element""")
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
lowercase__ : Optional[int] = [
self.__matrix[0][y] * self.cofactor(0 , SCREAMING_SNAKE_CASE_) for y in range(self.__width)
]
return sum(SCREAMING_SNAKE_CASE_)
def UpperCamelCase ( lowercase_ ) -> Matrix:
'''simple docstring'''
lowercase__ : list[list[float]] = [[0] * n for _ in range(lowercase_ )]
return Matrix(lowercase_ , lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Matrix:
'''simple docstring'''
random.seed(lowercase_ )
lowercase__ : list[list[float]] = [
[random.randint(lowercase_ , lowercase_ ) for _ in range(lowercase_ )] for _ in range(lowercase_ )
]
return Matrix(lowercase_ , lowercase_ , lowercase_ )
| 495 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase=False )-> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = OmegaConf.load(UpperCAmelCase )
if display:
print(yaml.dump(OmegaConf.to_container(UpperCAmelCase ) ) )
return config
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase=None ,UpperCAmelCase=None )-> Dict:
'''simple docstring'''
if conf_path is None:
SCREAMING_SNAKE_CASE_ = '''./model_checkpoints/vqgan_only.yaml'''
SCREAMING_SNAKE_CASE_ = load_config(UpperCAmelCase ,display=UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = VQModel(**config.model.params )
if ckpt_path is None:
SCREAMING_SNAKE_CASE_ = '''./model_checkpoints/vqgan_only.pt'''
SCREAMING_SNAKE_CASE_ = torch.load(UpperCAmelCase ,map_location=UpperCAmelCase )
if ".ckpt" in ckpt_path:
SCREAMING_SNAKE_CASE_ = sd['''state_dict''']
model.load_state_dict(UpperCAmelCase ,strict=UpperCAmelCase )
model.to(UpperCAmelCase )
del sd
return model
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase )-> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = model.encode(UpperCAmelCase )
print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''' )
SCREAMING_SNAKE_CASE_ = model.decode(UpperCAmelCase )
return xrec
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase=False )-> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = string.rsplit('''.''' ,1 )
if reload:
SCREAMING_SNAKE_CASE_ = importlib.import_module(UpperCAmelCase )
importlib.reload(UpperCAmelCase )
return getattr(importlib.import_module(UpperCAmelCase ,package=UpperCAmelCase ) ,cls )
def UpperCAmelCase ( UpperCAmelCase )-> Any:
'''simple docstring'''
if "target" not in config:
raise KeyError('''Expected key `target` to instantiate.''' )
return get_obj_from_str(config['''target'''] )(**config.get('''params''' ,{} ) )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=True ,UpperCAmelCase=True )-> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = instantiate_from_config(UpperCAmelCase )
if sd is not None:
model.load_state_dict(UpperCAmelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )-> List[Any]:
'''simple docstring'''
if ckpt:
SCREAMING_SNAKE_CASE_ = torch.load(UpperCAmelCase ,map_location='''cpu''' )
SCREAMING_SNAKE_CASE_ = pl_sd['''global_step''']
print(f'''loaded model from global step {global_step}.''' )
else:
SCREAMING_SNAKE_CASE_ = {'''state_dict''': None}
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = load_model_from_config(config.model ,pl_sd['''state_dict'''] ,gpu=UpperCAmelCase ,eval_mode=UpperCAmelCase )['''model''']
return model, global_step
| 393 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def UpperCAmelCase ( UpperCAmelCase )-> int:
'''simple docstring'''
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class snake_case ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase_ : nn.Module , lowerCAmelCase_ : int ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE_ = module
SCREAMING_SNAKE_CASE_ = nn.Sequential(
nn.Linear(module.in_features , lowerCAmelCase_ , bias=lowerCAmelCase_ ) , nn.Linear(lowerCAmelCase_ , module.out_features , bias=lowerCAmelCase_ ) , )
SCREAMING_SNAKE_CASE_ = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=lowerCAmelCase_ )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def _lowercase ( self : List[Any] , lowerCAmelCase_ : Any , *lowerCAmelCase_ : int , **lowerCAmelCase_ : Optional[int] ) -> Dict:
"""simple docstring"""
return self.module(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_ ) + self.adapter(lowerCAmelCase_ )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class snake_case ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : int = """bigscience/bloom-1b7"""
# Constant values
UpperCAmelCase : List[Any] = 2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4
UpperCAmelCase : int = """Hello my name is"""
UpperCAmelCase : List[str] = set()
EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
UpperCAmelCase : Dict = 10
def _lowercase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(self.model_name )
class snake_case ( lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# Models and tokenizer
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
def _lowercase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.model_abit.config
self.assertTrue(hasattr(lowerCAmelCase_ , '''quantization_config''' ) )
SCREAMING_SNAKE_CASE_ = config.to_dict()
SCREAMING_SNAKE_CASE_ = config.to_diff_dict()
SCREAMING_SNAKE_CASE_ = config.to_json_string()
def _lowercase ( self : int ) -> List[Any]:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
SCREAMING_SNAKE_CASE_ = self.model_fpaa.get_memory_footprint()
SCREAMING_SNAKE_CASE_ = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
SCREAMING_SNAKE_CASE_ = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def _lowercase ( self : List[Any] ) -> str:
"""simple docstring"""
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(lowerCAmelCase_ , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def _lowercase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE_ = self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCAmelCase_ ) , self.EXPECTED_OUTPUTS )
def _lowercase ( self : List[str] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = BitsAndBytesConfig()
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=lowerCAmelCase_ , device_map='''auto''' )
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE_ = model_abit_from_config.generate(
input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCAmelCase_ ) , self.EXPECTED_OUTPUTS )
def _lowercase ( self : Optional[int] ) -> str:
"""simple docstring"""
with self.assertRaises(lowerCAmelCase_ ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(lowerCAmelCase_ )
def _lowercase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = BitsAndBytesConfig()
with self.assertRaises(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=lowerCAmelCase_ , load_in_abit=lowerCAmelCase_ , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def _lowercase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises(lowerCAmelCase_ ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE_ = self.model_fpaa.to(torch.floataa )
SCREAMING_SNAKE_CASE_ = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
SCREAMING_SNAKE_CASE_ = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
SCREAMING_SNAKE_CASE_ = self.model_fpaa.half()
# Check this does not throw an error
SCREAMING_SNAKE_CASE_ = self.model_fpaa.float()
def _lowercase ( self : List[str] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class snake_case ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _lowercase ( cls : Dict ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = '''t5-small'''
SCREAMING_SNAKE_CASE_ = '''google/flan-t5-small''' # flan-t5 uses dense-act instead of dense-relu-dense
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(cls.model_name )
SCREAMING_SNAKE_CASE_ = '''Translate in German: Hello, my dog is cute'''
def _lowercase ( self : Any ) -> str:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : Tuple ) -> List[str]:
"""simple docstring"""
from transformers import TaForConditionalGeneration
SCREAMING_SNAKE_CASE_ = TaForConditionalGeneration._keep_in_fpaa_modules
SCREAMING_SNAKE_CASE_ = None
# test with `t5-small`
SCREAMING_SNAKE_CASE_ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
SCREAMING_SNAKE_CASE_ = model.generate(**lowerCAmelCase_ )
# test with `flan-t5-small`
SCREAMING_SNAKE_CASE_ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
SCREAMING_SNAKE_CASE_ = model.generate(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ = modules
def _lowercase ( self : Optional[Any] ) -> int:
"""simple docstring"""
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
SCREAMING_SNAKE_CASE_ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
SCREAMING_SNAKE_CASE_ = model.generate(**lowerCAmelCase_ )
# test with `flan-t5-small`
SCREAMING_SNAKE_CASE_ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
SCREAMING_SNAKE_CASE_ = model.generate(**lowerCAmelCase_ )
class snake_case ( lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# model_name
SCREAMING_SNAKE_CASE_ = '''bigscience/bloom-560m'''
SCREAMING_SNAKE_CASE_ = '''t5-small'''
# Different types of model
SCREAMING_SNAKE_CASE_ = AutoModel.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
# Sequence classification model
SCREAMING_SNAKE_CASE_ = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
# CausalLM model
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
# Seq2seq model
SCREAMING_SNAKE_CASE_ = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=lowerCAmelCase_ , device_map='''auto''' )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class snake_case ( lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
super().setUp()
def _lowercase ( self : List[str] ) -> Dict:
"""simple docstring"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = pipeline(
'''text-generation''' , model=self.model_name , model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
SCREAMING_SNAKE_CASE_ = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class snake_case ( lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
super().setUp()
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=lowerCAmelCase_ , device_map='''balanced''' )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
SCREAMING_SNAKE_CASE_ = model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=lowerCAmelCase_ ) , self.EXPECTED_OUTPUTS )
class snake_case ( lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = '''facebook/opt-350m'''
super().setUp()
def _lowercase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
SCREAMING_SNAKE_CASE_ = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
SCREAMING_SNAKE_CASE_ = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE_ = LoRALayer(module.q_proj , rank=16 )
SCREAMING_SNAKE_CASE_ = LoRALayer(module.k_proj , rank=16 )
SCREAMING_SNAKE_CASE_ = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
SCREAMING_SNAKE_CASE_ = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
SCREAMING_SNAKE_CASE_ = model.forward(**lowerCAmelCase_ )
out.logits.norm().backward()
for module in model.modules():
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(lowerCAmelCase_ , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class snake_case ( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase : Tuple = """gpt2-xl"""
UpperCAmelCase : str = 3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7
| 393 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> bool:
"""simple docstring"""
__UpperCamelCase = [int(lowercase_ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(lowercase_ ) == 4 and all(0 <= int(lowercase_ ) <= 2_54 for octet in octets )
if __name__ == "__main__":
a_ = input().strip()
a_ = "valid" if is_ip_va_address_valid(ip) else "invalid"
print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 375 |
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class _lowerCamelCase :
"""simple docstring"""
def __init__( self : str , snake_case : Any , snake_case : str=14 , snake_case : Dict=7 , snake_case : Any=True , snake_case : Any=True , snake_case : str=True , snake_case : List[str]=True , snake_case : int=True , snake_case : List[Any]=99 , snake_case : Optional[int]=32 , snake_case : str=5 , snake_case : int=4 , snake_case : str=37 , snake_case : Union[str, Any]="gelu" , snake_case : List[str]=0.1 , snake_case : Optional[int]=0.1 , snake_case : Tuple=512 , snake_case : int=16 , snake_case : Any=2 , snake_case : List[str]=0.02 , snake_case : List[Any]=3 , snake_case : str=4 , snake_case : Tuple=None , ):
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = seq_length
__UpperCamelCase = is_training
__UpperCamelCase = use_token_type_ids
__UpperCamelCase = use_input_mask
__UpperCamelCase = use_labels
__UpperCamelCase = use_mc_token_ids
__UpperCamelCase = vocab_size
__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 = type_sequence_label_size
__UpperCamelCase = initializer_range
__UpperCamelCase = num_labels
__UpperCamelCase = num_choices
__UpperCamelCase = scope
__UpperCamelCase = self.vocab_size - 1
def snake_case ( self : Optional[Any] ):
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase = None
if self.use_input_mask:
__UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase = None
if self.use_token_type_ids:
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase = None
if self.use_mc_token_ids:
__UpperCamelCase = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
if self.use_labels:
__UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase = self.get_config()
__UpperCamelCase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case ( self : Optional[int] ):
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def snake_case ( self : Any , snake_case : Optional[int] , snake_case : Any , snake_case : Union[str, Any] , snake_case : Dict , snake_case : List[Any] , *snake_case : Union[str, Any] ):
__UpperCamelCase = CTRLModel(config=snake_case )
model.to(snake_case )
model.eval()
model(snake_case , token_type_ids=snake_case , head_mask=snake_case )
model(snake_case , token_type_ids=snake_case )
__UpperCamelCase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def snake_case ( self : Any , snake_case : Tuple , snake_case : Tuple , snake_case : Union[str, Any] , snake_case : Dict , snake_case : List[str] , *snake_case : Union[str, Any] ):
__UpperCamelCase = CTRLLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
__UpperCamelCase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self : Any ):
__UpperCamelCase = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) = config_and_inputs
__UpperCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def snake_case ( self : List[Any] , snake_case : int , snake_case : Dict , snake_case : Optional[Any] , snake_case : Any , *snake_case : str ):
__UpperCamelCase = self.num_labels
__UpperCamelCase = CTRLForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
__UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class _lowerCamelCase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ : int = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
lowerCAmelCase__ : List[str] = (CTRLLMHeadModel,) if is_torch_available() else ()
lowerCAmelCase__ : str = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ : Dict = True
lowerCAmelCase__ : List[Any] = False
lowerCAmelCase__ : str = False
def snake_case ( self : str , snake_case : Optional[int] , snake_case : List[str] , snake_case : Tuple , snake_case : Dict , snake_case : List[str] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def snake_case ( self : Optional[int] ):
__UpperCamelCase = CTRLModelTester(self )
__UpperCamelCase = ConfigTester(self , config_class=snake_case , n_embd=37 )
def snake_case ( self : Union[str, Any] ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self : Dict ):
self.config_tester.run_common_tests()
def snake_case ( self : Optional[int] ):
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*snake_case )
def snake_case ( self : Tuple ):
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def snake_case ( self : List[Any] ):
pass
@slow
def snake_case ( self : Optional[Any] ):
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase = CTRLModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def snake_case ( self : List[Any] ):
pass
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : int ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def snake_case ( self : Optional[int] ):
__UpperCamelCase = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(snake_case )
__UpperCamelCase = torch.tensor(
[[11859, 0, 1611, 8]] , dtype=torch.long , device=snake_case ) # Legal the president is
__UpperCamelCase = [
11859,
0,
1611,
8,
5,
150,
26449,
2,
19,
348,
469,
3,
2595,
48,
20740,
246533,
246533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
__UpperCamelCase = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].tolist() , snake_case )
| 375 | 1 |
from collections.abc import Callable
import numpy as np
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = int(np.ceil((x_end - xa) / step_size ) )
lowercase = np.zeros((n + 1,) )
lowercase = ya
lowercase = xa
for k in range(__SCREAMING_SNAKE_CASE ):
lowercase = y[k] + step_size * ode_func(__SCREAMING_SNAKE_CASE , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
a : torch.FloatTensor
a : Optional[torch.FloatTensor] = None
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=0.999 ,SCREAMING_SNAKE_CASE_="cosine" ,) -> str:
if alpha_transform_type == "cosine":
def alpha_bar_fn(SCREAMING_SNAKE_CASE_ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(SCREAMING_SNAKE_CASE_ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowercase__ : List[str] = []
for i in range(SCREAMING_SNAKE_CASE_ ):
lowercase__ : int = i / num_diffusion_timesteps
lowercase__ : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_ ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ ) )
return torch.tensor(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
class UpperCAmelCase( snake_case_ , snake_case_ ):
"""simple docstring"""
a : int = 1
@register_to_config
def __init__( self , lowerCamelCase = 1000 , lowerCamelCase = 0.00_01 , lowerCamelCase = 0.02 , lowerCamelCase = "linear" , lowerCamelCase = None , lowerCamelCase = True , lowerCamelCase = True , lowerCamelCase = 0 , lowerCamelCase = "epsilon" , lowerCamelCase = 1.0 , **lowerCamelCase , ) -> List[Any]:
"""simple docstring"""
if kwargs.get("set_alpha_to_one" , lowerCamelCase ) is not None:
lowercase__ : Any = (
"The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
)
deprecate("set_alpha_to_one" , "1.0.0" , lowerCamelCase , standard_warn=lowerCamelCase )
lowercase__ : Optional[int] = kwargs["set_alpha_to_one"]
if trained_betas is not None:
lowercase__ : Optional[int] = torch.tensor(lowerCamelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
lowercase__ : int = torch.linspace(lowerCamelCase , lowerCamelCase , lowerCamelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowercase__ : List[str] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCamelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowercase__ : Optional[Any] = betas_for_alpha_bar(lowerCamelCase )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
lowercase__ : List[str] = 1.0 - self.betas
lowercase__ : List[str] = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
lowercase__ : Dict = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
lowercase__ : Optional[int] = 1.0
# setable values
lowercase__ : int = None
lowercase__ : int = torch.from_numpy(np.arange(0 , lowerCamelCase ).copy().astype(np.intaa ) )
def __a ( self , lowerCamelCase , lowerCamelCase = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def __a ( self , lowerCamelCase , lowerCamelCase = None ) -> Optional[Any]:
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
f""" maximal {self.config.num_train_timesteps} timesteps.""" )
lowercase__ : Optional[Any] = num_inference_steps
lowercase__ : Dict = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase__ : Dict = (np.arange(0 , lowerCamelCase ) * step_ratio).round().copy().astype(np.intaa )
lowercase__ : Any = torch.from_numpy(lowerCamelCase ).to(lowerCamelCase )
self.timesteps += self.config.steps_offset
def __a ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = False , lowerCamelCase = None , lowerCamelCase = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
"""simple docstring"""
lowercase__ : int = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
lowercase__ : Optional[int] = self.alphas_cumprod[timestep]
lowercase__ : Dict = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
lowercase__ : Optional[int] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
lowercase__ : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
lowercase__ : Optional[int] = model_output
elif self.config.prediction_type == "sample":
lowercase__ : Any = model_output
lowercase__ : Optional[int] = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
lowercase__ : List[Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
lowercase__ : Any = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
" `v_prediction`" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
lowercase__ : Optional[Any] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowercase__ : int = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowercase__ : Dict = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowerCamelCase , pred_original_sample=lowerCamelCase )
def __len__( self ) -> Tuple:
"""simple docstring"""
return self.config.num_train_timesteps | 397 | 0 |
from __future__ import annotations
import math
def _a ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : bool , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if not scores:
raise ValueError('Scores cannot be empty' )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1 , node_index * 2 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , )
if is_max
else min(
minimax(depth + 1 , node_index * 2 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , )
)
def _a ( ):
"""simple docstring"""
_lowerCAmelCase = [90, 23, 6, 33, 21, 65, 123, 34423]
_lowerCAmelCase = math.log(len(__SCREAMING_SNAKE_CASE ) , 2 )
print(f'''Optimal value : {minimax(0 , 0 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 711 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class __lowercase( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Dict , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any]=7 , _lowerCAmelCase : List[str]=3 , _lowerCAmelCase : int=30 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , _lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : Dict=1 / 255 , _lowerCAmelCase : int=True , ) -> Union[str, Any]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_lowerCAmelCase = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean
_lowerCAmelCase = image_std
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = do_pad
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[int]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : Dict=False ) -> Dict:
if not batched:
_lowerCAmelCase = image_inputs[0]
if isinstance(_lowerCAmelCase , Image.Image ):
_lowerCAmelCase , _lowerCAmelCase = image.size
else:
_lowerCAmelCase , _lowerCAmelCase = image.shape[1], image.shape[2]
if w < h:
_lowerCAmelCase = int(self.size['shortest_edge'] * h / w )
_lowerCAmelCase = self.size['shortest_edge']
elif w > h:
_lowerCAmelCase = self.size['shortest_edge']
_lowerCAmelCase = int(self.size['shortest_edge'] * w / h )
else:
_lowerCAmelCase = self.size['shortest_edge']
_lowerCAmelCase = self.size['shortest_edge']
else:
_lowerCAmelCase = []
for image in image_inputs:
_lowerCAmelCase , _lowerCAmelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_lowerCAmelCase = max(_lowerCAmelCase , key=lambda _lowerCAmelCase : item[0] )[0]
_lowerCAmelCase = max(_lowerCAmelCase , key=lambda _lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowercase( SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = ConditionalDetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Tuple:
_lowerCAmelCase = ConditionalDetrImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[Any]:
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCAmelCase , 'image_mean' ) )
self.assertTrue(hasattr(_lowerCAmelCase , 'image_std' ) )
self.assertTrue(hasattr(_lowerCAmelCase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowerCAmelCase , 'do_resize' ) )
self.assertTrue(hasattr(_lowerCAmelCase , 'size' ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[Any]:
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1333} )
self.assertEqual(image_processor.do_pad , _lowerCAmelCase )
_lowerCAmelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_lowerCAmelCase )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _lowerCAmelCase )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> str:
pass
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[int]:
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_lowerCAmelCase , _lowerCAmelCase = self.image_processor_tester.get_expected_values(_lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_lowerCAmelCase , _lowerCAmelCase = self.image_processor_tester.get_expected_values(_lowerCAmelCase , batched=_lowerCAmelCase )
_lowerCAmelCase = 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,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> List[str]:
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = 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
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_lowerCAmelCase , _lowerCAmelCase = self.image_processor_tester.get_expected_values(_lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_lowerCAmelCase = image_processing(_lowerCAmelCase , return_tensors='pt' ).pixel_values
_lowerCAmelCase , _lowerCAmelCase = self.image_processor_tester.get_expected_values(_lowerCAmelCase , batched=_lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> str:
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = 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
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_lowerCAmelCase , _lowerCAmelCase = self.image_processor_tester.get_expected_values(_lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_lowerCAmelCase = image_processing(_lowerCAmelCase , return_tensors='pt' ).pixel_values
_lowerCAmelCase , _lowerCAmelCase = self.image_processor_tester.get_expected_values(_lowerCAmelCase , batched=_lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Dict:
# prepare image and target
_lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
_lowerCAmelCase = json.loads(f.read() )
_lowerCAmelCase = {'image_id': 3_9769, 'annotations': target}
# encode them
_lowerCAmelCase = ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
_lowerCAmelCase = image_processing(images=_lowerCAmelCase , annotations=_lowerCAmelCase , return_tensors='pt' )
# verify pixel values
_lowerCAmelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _lowerCAmelCase )
_lowerCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _lowerCAmelCase , atol=1e-4 ) )
# verify area
_lowerCAmelCase = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _lowerCAmelCase ) )
# verify boxes
_lowerCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _lowerCAmelCase )
_lowerCAmelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _lowerCAmelCase , atol=1e-3 ) )
# verify image_id
_lowerCAmelCase = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _lowerCAmelCase ) )
# verify is_crowd
_lowerCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _lowerCAmelCase ) )
# verify class_labels
_lowerCAmelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _lowerCAmelCase ) )
# verify orig_size
_lowerCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _lowerCAmelCase ) )
# verify size
_lowerCAmelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _lowerCAmelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Optional[Any]:
# prepare image, target and masks_path
_lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
_lowerCAmelCase = json.loads(f.read() )
_lowerCAmelCase = {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}
_lowerCAmelCase = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
_lowerCAmelCase = ConditionalDetrImageProcessor(format='coco_panoptic' )
_lowerCAmelCase = image_processing(images=_lowerCAmelCase , annotations=_lowerCAmelCase , masks_path=_lowerCAmelCase , return_tensors='pt' )
# verify pixel values
_lowerCAmelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _lowerCAmelCase )
_lowerCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _lowerCAmelCase , atol=1e-4 ) )
# verify area
_lowerCAmelCase = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _lowerCAmelCase ) )
# verify boxes
_lowerCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _lowerCAmelCase )
_lowerCAmelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _lowerCAmelCase , atol=1e-3 ) )
# verify image_id
_lowerCAmelCase = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _lowerCAmelCase ) )
# verify is_crowd
_lowerCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _lowerCAmelCase ) )
# verify class_labels
_lowerCAmelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _lowerCAmelCase ) )
# verify masks
_lowerCAmelCase = 82_2873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _lowerCAmelCase )
# verify orig_size
_lowerCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _lowerCAmelCase ) )
# verify size
_lowerCAmelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _lowerCAmelCase ) )
| 585 | 0 |
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 convert_to_rgb, 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
if is_vision_available():
import PIL
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Dict = ["pixel_values"]
def __init__( self, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = 1 / 255, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = True, **SCREAMING_SNAKE_CASE_, ) -> None:
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = size if size is not None else {'height': 384, 'width': 384}
UpperCamelCase : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_, default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = do_resize
UpperCamelCase : Tuple = size
UpperCamelCase : Optional[int] = resample
UpperCamelCase : Union[str, Any] = do_rescale
UpperCamelCase : Union[str, Any] = rescale_factor
UpperCamelCase : Optional[int] = do_normalize
UpperCamelCase : Tuple = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCamelCase : Any = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCamelCase : Dict = do_convert_rgb
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> np.ndarray:
UpperCamelCase : str = get_size_dict(SCREAMING_SNAKE_CASE_, default_to_square=SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
UpperCamelCase : Optional[int] = (size['height'], size['width'])
return resize(SCREAMING_SNAKE_CASE_, size=SCREAMING_SNAKE_CASE_, resample=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> Tuple:
return rescale(SCREAMING_SNAKE_CASE_, scale=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> np.ndarray:
return normalize(SCREAMING_SNAKE_CASE_, mean=SCREAMING_SNAKE_CASE_, std=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST, **SCREAMING_SNAKE_CASE_, ) -> PIL.Image.Image:
UpperCamelCase : Tuple = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : Optional[Any] = resample if resample is not None else self.resample
UpperCamelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : str = image_std if image_std is not None else self.image_std
UpperCamelCase : Tuple = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase : Dict = size if size is not None else self.size
UpperCamelCase : Optional[int] = get_size_dict(SCREAMING_SNAKE_CASE_, default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_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:
UpperCamelCase : Any = [convert_to_rgb(SCREAMING_SNAKE_CASE_ ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase : Any = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Optional[int] = [self.resize(image=SCREAMING_SNAKE_CASE_, size=SCREAMING_SNAKE_CASE_, resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : Optional[Any] = [self.rescale(image=SCREAMING_SNAKE_CASE_, scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[Any] = [self.normalize(image=SCREAMING_SNAKE_CASE_, mean=SCREAMING_SNAKE_CASE_, std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Optional[int] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Tuple = BatchFeature(data={'pixel_values': images}, tensor_type=SCREAMING_SNAKE_CASE_ )
return encoded_outputs
| 40 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructTextConfig''',
'''Pix2StructVisionConfig''',
],
'''processing_pix2struct''': ['''Pix2StructProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['''Pix2StructImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Pix2StructPreTrainedModel''',
'''Pix2StructForConditionalGeneration''',
'''Pix2StructVisionModel''',
'''Pix2StructTextModel''',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 40 | 1 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : List[str] , UpperCamelCase : str , **UpperCamelCase : Tuple ) -> Dict:
"""simple docstring"""
a_ = AutoConfig.from_pretrained(a__ , **a__ )
a_ = AutoModelForSeqaSeqLM.from_config(a__ )
model.save_pretrained(a__ )
AutoTokenizer.from_pretrained(a__ ).save_pretrained(a__ )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version) | 714 |
# Copyright 2023 The HuggingFace Inc. 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 re
from ..utils import cached_file
# docstyle-ignore
_A = '\nHuman: <<task>>\n\nAssistant: '
_A = 'huggingface-tools/default-prompts'
_A = {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : str , UpperCamelCase : Optional[int] , UpperCamelCase : Union[str, Any]="run" ) -> int:
"""simple docstring"""
if prompt_or_repo_id is None:
a_ = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("""\\s""" , UpperCamelCase ) is not None:
return prompt_or_repo_id
a_ = cached_file(
UpperCamelCase , PROMPT_FILES[mode] , repo_type="""dataset""" , user_agent={"""agent""": agent_name} )
with open(UpperCamelCase , """r""" , encoding="""utf-8""" ) as f:
return f.read() | 403 | 0 |
'''simple docstring'''
import os
import sys
lowerCAmelCase: Optional[Any] = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
lowerCAmelCase: Union[str, Any] = [
'torch',
'numpy',
'tokenizers',
'filelock',
'requests',
'tqdm',
'regex',
'sentencepiece',
'sacremoses',
'importlib_metadata',
'huggingface_hub',
]
@add_start_docstrings(AutoConfig.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoConfig.from_pretrained(*_A , **_A )
@add_start_docstrings(AutoTokenizer.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoTokenizer.from_pretrained(*_A , **_A )
@add_start_docstrings(AutoModel.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoModel.from_pretrained(*_A , **_A )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoModelForCausalLM.from_pretrained(*_A , **_A )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoModelForMaskedLM.from_pretrained(*_A , **_A )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoModelForSequenceClassification.from_pretrained(*_A , **_A )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def lowerCamelCase__ ( *_A , **_A ):
return AutoModelForQuestionAnswering.from_pretrained(*_A , **_A ) | 526 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase: Dict = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Optional[int] = [
'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMAEForPreTraining',
'ViTMAELayer',
'ViTMAEModel',
'ViTMAEPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: List[Any] = [
'TFViTMAEForPreTraining',
'TFViTMAEModel',
'TFViTMAEPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowerCAmelCase: Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 526 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__SCREAMING_SNAKE_CASE : str = 'pt'
elif is_tf_available():
__SCREAMING_SNAKE_CASE : str = 'tf'
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = 'jax'
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase , unittest.TestCase ):
lowercase__ = PerceiverTokenizer
lowercase__ = False
def __lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
__a : int = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowerCamelCase ( self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
def __lowerCamelCase ( self , **__UpperCamelCase ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase=False , __UpperCamelCase=20 , __UpperCamelCase=5 ):
'''simple docstring'''
__a : Union[str, Any] = []
for i in range(len(__UpperCamelCase ) ):
try:
__a : str = tokenizer.decode([i] , clean_up_tokenization_spaces=__UpperCamelCase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
__a : str = list(filter(lambda __UpperCamelCase : re.match(r"""^[ a-zA-Z]+$""" , t[1] ) , __UpperCamelCase ) )
__a : Tuple = list(filter(lambda __UpperCamelCase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__UpperCamelCase ) , __UpperCamelCase ) )
if max_length is not None and len(__UpperCamelCase ) > max_length:
__a : List[Any] = toks[:max_length]
if min_length is not None and len(__UpperCamelCase ) < min_length and len(__UpperCamelCase ) > 0:
while len(__UpperCamelCase ) < min_length:
__a : Optional[Any] = toks + toks
# toks_str = [t[1] for t in toks]
__a : int = [t[0] for t in toks]
# Ensure consistency
__a : Tuple = tokenizer.decode(__UpperCamelCase , clean_up_tokenization_spaces=__UpperCamelCase )
if " " not in output_txt and len(__UpperCamelCase ) > 1:
__a : List[str] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__UpperCamelCase )
+ """ """
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__UpperCamelCase )
)
if with_prefix_space:
__a : List[Any] = """ """ + output_txt
__a : Any = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
return output_txt, output_ids
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : Any = self.perceiver_tokenizer
__a : Optional[Any] = """Unicode €."""
__a : List[str] = tokenizer(__UpperCamelCase )
__a : str = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded["""input_ids"""] , __UpperCamelCase )
# decoding
__a : Tuple = tokenizer.decode(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , """[CLS]Unicode €.[SEP]""" )
__a : str = tokenizer("""e è é ê ë""" )
__a : Tuple = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded["""input_ids"""] , __UpperCamelCase )
# decoding
__a : Tuple = tokenizer.decode(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , """[CLS]e è é ê ë[SEP]""" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) ) , """[CLS]e è é ê ë[SEP]""" )
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : Dict = self.perceiver_tokenizer
__a : int = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
# fmt: off
__a : str = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
__a : List[str] = tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors=__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
if FRAMEWORK != "jax":
__a : Union[str, Any] = list(batch.input_ids.numpy()[0] )
else:
__a : Tuple = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : Dict = self.perceiver_tokenizer
__a : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
__a : Tuple = tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors=__UpperCamelCase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("""input_ids""" , __UpperCamelCase )
self.assertIn("""attention_mask""" , __UpperCamelCase )
self.assertNotIn("""decoder_input_ids""" , __UpperCamelCase )
self.assertNotIn("""decoder_attention_mask""" , __UpperCamelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : List[str] = self.perceiver_tokenizer
__a : str = [
"""Summary of the text.""",
"""Another summary.""",
]
__a : List[Any] = tokenizer(
text_target=__UpperCamelCase , max_length=32 , padding="""max_length""" , truncation=__UpperCamelCase , return_tensors=__UpperCamelCase )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
__a : int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
__a : Union[str, Any] = tempfile.mkdtemp()
__a : Optional[int] = """ He is very happy, UNwant\u00E9d,running"""
__a : Any = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
tokenizer.save_pretrained(__UpperCamelCase )
__a : str = tokenizer.__class__.from_pretrained(__UpperCamelCase )
__a : Any = after_tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
shutil.rmtree(__UpperCamelCase )
__a : Union[str, Any] = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
__a : Optional[Any] = tempfile.mkdtemp()
__a : int = """ He is very happy, UNwant\u00E9d,running"""
tokenizer.add_tokens(["""bim""", """bambam"""] )
__a : Optional[Any] = tokenizer.additional_special_tokens
additional_special_tokens.append("""new_additional_special_token""" )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
__a : Any = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
tokenizer.save_pretrained(__UpperCamelCase )
__a : List[Any] = tokenizer.__class__.from_pretrained(__UpperCamelCase )
__a : str = after_tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
self.assertIn("""new_additional_special_token""" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
__a : Tuple = tokenizer.__class__.from_pretrained(__UpperCamelCase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(__UpperCamelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : int = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__UpperCamelCase )
with open(os.path.join(__UpperCamelCase , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
__a : Any = json.load(__UpperCamelCase )
with open(os.path.join(__UpperCamelCase , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
__a : Dict = json.load(__UpperCamelCase )
__a : Union[str, Any] = [f"""<extra_id_{i}>""" for i in range(125 )]
__a : List[Any] = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
__a : Union[str, Any] = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
with open(os.path.join(__UpperCamelCase , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__UpperCamelCase , __UpperCamelCase )
with open(os.path.join(__UpperCamelCase , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__UpperCamelCase , __UpperCamelCase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__a : int = tokenizer_class.from_pretrained(
__UpperCamelCase , )
self.assertIn(
"""an_additional_special_token""" , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
["""an_additional_special_token"""] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__a : Optional[Any] = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" , lstrip=__UpperCamelCase )]
__a : str = tokenizer_class.from_pretrained(
__UpperCamelCase , additional_special_tokens=__UpperCamelCase , )
self.assertIn("""a_new_additional_special_token""" , tokenizer.additional_special_tokens )
self.assertEqual(
["""a_new_additional_special_token"""] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) ) , )
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : List[str] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , """�""" )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
__a : List[str] = self.get_tokenizers(fast=__UpperCamelCase , do_lower_case=__UpperCamelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
__a : Tuple = ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
__a : Optional[Any] = tokenizer.convert_tokens_to_string(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase ) | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowercase ) -> Any:
# Construct model
if gpta_config_file == "":
__a : Dict = GPTaConfig()
else:
__a : Optional[Any] = GPTaConfig.from_json_file(lowercase )
__a : Union[str, Any] = GPTaModel(lowercase )
# Load weights from numpy
load_tf_weights_in_gpta(lowercase , lowercase , lowercase )
# Save pytorch-model
__a : Optional[int] = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
__a : Dict = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , lowercase )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--gpt2_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained OpenAI model. \n'
'This specifies the model architecture.'
),
)
__SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path) | 697 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[List[PIL.Image.Image], np.ndarray]
__UpperCamelCase: Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 244 | '''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "M-CLIP"
def __init__( self : Union[str, Any] , A : Optional[Any]=1024 , A : List[str]=768 , **A : Union[str, Any] ):
_UpperCAmelCase : str = transformerDimSize
_UpperCAmelCase : int = imageDimSize
super().__init__(**A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = MCLIPConfig
def __init__( self : Optional[int] , A : Union[str, Any] , *A : Any , **A : Optional[int] ):
super().__init__(A , *A , **A )
_UpperCAmelCase : Optional[int] = XLMRobertaModel(A )
_UpperCAmelCase : int = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def _A ( self : int , A : int , A : int ):
_UpperCAmelCase : Optional[int] = self.transformer(input_ids=A , attention_mask=A )[0]
_UpperCAmelCase : Optional[int] = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(A ), embs
| 244 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def snake_case_ ( ):
__lowercase = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )
return image
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias") )
# fmt: on
return rename_keys
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = dct.pop(_SCREAMING_SNAKE_CASE )
__lowercase = val
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__lowercase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""" )
__lowercase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
__lowercase = torch.cat((q_bias, torch.zeros_like(_SCREAMING_SNAKE_CASE , requires_grad=_SCREAMING_SNAKE_CASE ), v_bias) )
__lowercase = qkv_bias
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = 3_6_4 if "coco" in model_name else 2_2_4
__lowercase = InstructBlipVisionConfig(image_size=_SCREAMING_SNAKE_CASE ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
__lowercase = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__lowercase = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
__lowercase = LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf" , vocab_size=3_2_0_0_1 ).to_dict()
elif "vicuna-13b" in model_name:
__lowercase = LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf" , vocab_size=3_2_0_0_1 ).to_dict()
else:
raise ValueError("Model name not supported" )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
__lowercase = InstructBlipQFormerConfig(vocab_size=3_0_5_2_3 ).to_dict()
__lowercase = InstructBlipConfig(vision_config=_SCREAMING_SNAKE_CASE , text_config=_SCREAMING_SNAKE_CASE , qformer_config=_SCREAMING_SNAKE_CASE )
return config, image_size
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False ):
__lowercase = AutoTokenizer.from_pretrained("bert-base-uncased" , truncation_side="left" )
qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"} )
if "t5" in model_name:
__lowercase = TaTokenizerFast.from_pretrained("google/flan-t5-xl" , truncation_side="left" )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
__lowercase = LlamaTokenizerFast.from_pretrained(
"huggyllama/llama-7b" , truncation_side="left" , bos_token="</s>" , unk_token="</s>" )
tokenizer.add_special_tokens({"pad_token": "[PAD]"} )
__lowercase , __lowercase = get_blipa_config(_SCREAMING_SNAKE_CASE )
__lowercase = InstructBlipForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval()
__lowercase = {
"instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
"instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
"instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
"instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
}
__lowercase , __lowercase = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__lowercase = "cuda:1" if torch.cuda.is_available() else "cpu"
__lowercase = "cuda:2" if torch.cuda.is_available() else "cpu"
__lowercase , __lowercase , __lowercase = load_model_and_preprocess(
name=_SCREAMING_SNAKE_CASE , model_type=_SCREAMING_SNAKE_CASE , is_eval=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
original_model.eval()
print("Done!" )
# update state dict keys
__lowercase = original_model.state_dict()
__lowercase = create_rename_keys(_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__lowercase = state_dict.pop(_SCREAMING_SNAKE_CASE )
if key.startswith("Qformer.bert" ):
__lowercase = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__lowercase = key.replace("self" , "attention" )
if "llm_proj" in key:
__lowercase = key.replace("llm_proj" , "language_projection" )
if "t5_proj" in key:
__lowercase = key.replace("t5_proj" , "language_projection" )
if key.startswith("llm_model" ):
__lowercase = key.replace("llm_model" , "language_model" )
if key.startswith("t5" ):
__lowercase = key.replace("t5" , "language" )
__lowercase = val
# read in qv biases
read_in_q_v_bias(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
__lowercase = load_demo_image()
__lowercase = "What is unusual about this image?"
# create processor
__lowercase = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=_SCREAMING_SNAKE_CASE , image_std=_SCREAMING_SNAKE_CASE )
__lowercase = InstructBlipProcessor(
image_processor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE , )
__lowercase = processor(images=_SCREAMING_SNAKE_CASE , text=_SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(_SCREAMING_SNAKE_CASE )
# make sure processor creates exact same pixel values
__lowercase = vis_processors["eval"](_SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(_SCREAMING_SNAKE_CASE )
__lowercase = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , _SCREAMING_SNAKE_CASE )
original_model.to(_SCREAMING_SNAKE_CASE )
hf_model.to(_SCREAMING_SNAKE_CASE )
with torch.no_grad():
if "vicuna" in model_name:
__lowercase = original_model({"image": original_pixel_values, "text_input": [prompt]} ).logits
__lowercase = hf_model(**_SCREAMING_SNAKE_CASE ).logits
else:
__lowercase = original_model(
{"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]} ).logits
__lowercase = tokenizer("\n" , return_tensors="pt" ).input_ids.to(_SCREAMING_SNAKE_CASE )
__lowercase = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -1_0_0 )
__lowercase = hf_model(**_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE ).logits
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
__lowercase = 1E-4 if "vicuna" in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ) , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE )
print("Looks ok!" )
print("Generating with original model..." )
__lowercase = original_model.generate({"image": original_pixel_values, "prompt": prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print("Generating with HF model..." )
__lowercase = hf_model.generate(
**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , num_beams=5 , max_length=2_5_6 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
__lowercase = 2
print("Original generation:" , _SCREAMING_SNAKE_CASE )
__lowercase = processor.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
__lowercase = [text.strip() for text in output_text]
print("HF generation:" , _SCREAMING_SNAKE_CASE )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
processor.push_to_hub(F"""Salesforce/{model_name}""" )
hf_model.push_to_hub(F"""Salesforce/{model_name}""" )
if __name__ == "__main__":
snake_case__ : Optional[Any] = argparse.ArgumentParser()
snake_case__ : Tuple = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
snake_case__ : Dict = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 713 |
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 _A :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = 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 = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = 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=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__lowercase = 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 _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["prompt"]
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
if "image" in inputs:
__lowercase = inputs["image"]
else:
__lowercase = None
if "mask_image" in inputs:
__lowercase = inputs["mask_image"]
else:
__lowercase = None
if "original_image" in inputs:
__lowercase = inputs["original_image"]
else:
__lowercase = None
__lowercase , __lowercase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
__lowercase = {
"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 = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = 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 = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
# inputs with prompt converted to embeddings
__lowercase = {
"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 = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = 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 = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 655 | 0 |
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 lowercase :
def __init__( self : Any , _UpperCamelCase : Optional[int] , _UpperCamelCase : List[Any]=13 , _UpperCamelCase : Union[str, Any]=7 , _UpperCamelCase : List[Any]=True , _UpperCamelCase : Optional[Any]=True , _UpperCamelCase : List[Any]=True , _UpperCamelCase : Optional[int]=True , _UpperCamelCase : Dict=99 , _UpperCamelCase : Dict=24 , _UpperCamelCase : Optional[Any]=2 , _UpperCamelCase : List[str]=6 , _UpperCamelCase : Union[str, Any]=37 , _UpperCamelCase : Dict="gelu" , _UpperCamelCase : Optional[Any]=0.1 , _UpperCamelCase : int=0.1 , _UpperCamelCase : str=512 , _UpperCamelCase : Optional[int]=16 , _UpperCamelCase : List[str]=2 , _UpperCamelCase : Dict=0.0_2 , _UpperCamelCase : Optional[Any]=3 , _UpperCamelCase : int=None , _UpperCamelCase : Optional[int]=1_000 , ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_input_mask
SCREAMING_SNAKE_CASE = use_token_type_ids
SCREAMING_SNAKE_CASE = use_labels
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 = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = range_bbox
def __snake_case( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_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]:
SCREAMING_SNAKE_CASE = bbox[i, j, 3]
SCREAMING_SNAKE_CASE = bbox[i, j, 1]
SCREAMING_SNAKE_CASE = t
if bbox[i, j, 2] < bbox[i, j, 0]:
SCREAMING_SNAKE_CASE = bbox[i, j, 2]
SCREAMING_SNAKE_CASE = bbox[i, j, 0]
SCREAMING_SNAKE_CASE = t
SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __snake_case( self : int ) -> 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 __snake_case( self : Optional[Any] , _UpperCamelCase : Any , _UpperCamelCase : List[Any] , _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Any , _UpperCamelCase : List[Any] , _UpperCamelCase : List[str] , ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = LiltModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , bbox=_UpperCamelCase , token_type_ids=_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , bbox=_UpperCamelCase )
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 __snake_case( self : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : Tuple , _UpperCamelCase : Tuple , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any , _UpperCamelCase : List[Any] , _UpperCamelCase : int , ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = LiltForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __snake_case( self : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : int , _UpperCamelCase : Optional[int] , _UpperCamelCase : int , _UpperCamelCase : List[str] , _UpperCamelCase : Tuple , _UpperCamelCase : Optional[Any] , ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = LiltForQuestionAnswering(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(
_UpperCamelCase , bbox=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , start_positions=_UpperCamelCase , end_positions=_UpperCamelCase , )
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 __snake_case( self : int ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
SCREAMING_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 lowercase ( a , a , a , unittest.TestCase ):
lowercase__ : List[Any] = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase__ : List[Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : Union[str, Any] = False
def __snake_case( self : str , _UpperCamelCase : Dict , _UpperCamelCase : List[str] , _UpperCamelCase : str , _UpperCamelCase : List[Any] , _UpperCamelCase : int ) -> str:
'''simple docstring'''
return True
def __snake_case( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = LiltModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37 )
def __snake_case( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __snake_case( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def __snake_case( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def __snake_case( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
def __snake_case( self : Tuple ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
@slow
def __snake_case( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = LiltModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
@require_torch
@slow
class lowercase ( unittest.TestCase ):
def __snake_case( self : Any ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(_UpperCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([[1, 2]] , device=_UpperCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_UpperCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(input_ids=_UpperCamelCase , bbox=_UpperCamelCase )
SCREAMING_SNAKE_CASE = torch.Size([1, 2, 768] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]] , device=_UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , _UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _UpperCamelCase , atol=1e-3 ) )
| 403 | import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class lowercase ( unittest.TestCase ):
def __init__( self : str , _UpperCamelCase : Any , _UpperCamelCase : Tuple=13 , _UpperCamelCase : Tuple=7 , _UpperCamelCase : Dict=True , _UpperCamelCase : str=True , _UpperCamelCase : Dict=True , _UpperCamelCase : Optional[int]=True , _UpperCamelCase : int=99 , _UpperCamelCase : Optional[int]=32 , _UpperCamelCase : str=5 , _UpperCamelCase : List[str]=4 , _UpperCamelCase : List[Any]=37 , _UpperCamelCase : int="gelu" , _UpperCamelCase : List[str]=0.1 , _UpperCamelCase : Dict=0.1 , _UpperCamelCase : Optional[Any]=512 , _UpperCamelCase : Optional[Any]=16 , _UpperCamelCase : Tuple=2 , _UpperCamelCase : Union[str, Any]=0.0_2 , _UpperCamelCase : str=4 , ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_attention_mask
SCREAMING_SNAKE_CASE = use_token_type_ids
SCREAMING_SNAKE_CASE = use_labels
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 = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_choices
def __snake_case( self : Dict ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE = RobertaPreLayerNormConfig(
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=_UpperCamelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __snake_case( self : Dict ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def __snake_case( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class lowercase ( a , unittest.TestCase ):
lowercase__ : List[Any] = True
lowercase__ : str = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __snake_case( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxRobertaPreLayerNormModelTester(self )
@slow
def __snake_case( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCamelCase )
@require_flax
class lowercase ( unittest.TestCase ):
@slow
def __snake_case( self : List[str] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_UpperCamelCase )
SCREAMING_SNAKE_CASE = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
SCREAMING_SNAKE_CASE = model(_UpperCamelCase )[0]
SCREAMING_SNAKE_CASE = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _UpperCamelCase )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCamelCase , atol=1e-4 ) )
@slow
def __snake_case( self : List[str] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_UpperCamelCase )
SCREAMING_SNAKE_CASE = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
SCREAMING_SNAKE_CASE = model(_UpperCamelCase )[0]
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCamelCase , atol=1e-4 ) )
| 403 | 1 |
def snake_case__ ( ) -> Union[str, Any]:
"""simple docstring"""
A__ : Tuple = []
A__ : Optional[int] = 1
while len(__lowercase ) < 1E6:
constant.append(str(__lowercase ) )
i += 1
A__ : Any = "".join(__lowercase )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[9_9] )
* int(constant[9_9_9] )
* int(constant[9_9_9_9] )
* int(constant[9_9_9_9_9] )
* int(constant[9_9_9_9_9_9] )
)
if __name__ == "__main__":
print(solution()) | 706 |
from collections import Counter
from timeit import timeit
def snake_case__ ( __lowercase = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def snake_case__ ( __lowercase = "" ) -> bool:
"""simple docstring"""
if len(__lowercase ) == 0:
return True
A__ : Any = input_str.replace(" " , "" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
A__ : dict[str, int] = {}
for character in lower_case_input_str:
A__ : Optional[int] = character_freq_dict.get(__lowercase , 0 ) + 1
A__ : List[Any] = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def snake_case__ ( __lowercase = "" ) -> None:
"""simple docstring"""
print("\nFor string = " , __lowercase , ":" )
print(
"> can_string_be_rearranged_as_palindrome_counter()" , "\tans =" , can_string_be_rearranged_as_palindrome_counter(__lowercase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
print(
"> can_string_be_rearranged_as_palindrome()" , "\tans =" , can_string_be_rearranged_as_palindrome(__lowercase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
if __name__ == "__main__":
snake_case : Dict = input(
'Enter string to determine if it can be rearranged as a palindrome or not: '
).strip()
benchmark(check_str)
snake_case : Dict = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""") | 182 | 0 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Optional[int] = DownBlockaD # noqa F405
_A : Optional[Any] = """down"""
def __lowerCamelCase ( self ):
__UpperCAmelCase = [-0.0_2_3_2, -0.9_8_6_9, 0.8_0_5_4, -0.0_6_3_7, -0.1_6_8_8, -1.4_2_6_4, 0.4_4_7_0, -1.3_3_9_4, 0.0_9_0_4]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : int = ResnetDownsampleBlockaD # noqa F405
_A : int = """down"""
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.0_7_1_0, 0.2_4_1_0, -0.7_3_2_0, -1.0_7_5_7, -1.1_3_4_3, 0.3_5_4_0, -0.0_1_3_3, -0.2_5_7_6, 0.0_9_4_8]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : List[Any] = AttnDownBlockaD # noqa F405
_A : str = """down"""
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.0_6_3_6, 0.8_9_6_4, -0.6_2_3_4, -1.0_1_3_1, 0.0_8_4_4, 0.4_9_3_5, 0.3_4_3_7, 0.0_9_1_1, -0.2_9_5_7]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Optional[int] = CrossAttnDownBlockaD # noqa F405
_A : int = """down"""
def __lowerCamelCase ( self ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.2_2_3_8, -0.7_3_9_6, -0.2_2_5_5, -0.3_8_2_9, 0.1_9_2_5, 1.1_6_6_5, 0.0_6_0_3, -0.7_2_9_5, 0.1_9_8_3]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Optional[int] = SimpleCrossAttnDownBlockaD # noqa F405
_A : List[str] = """down"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_encoder_hidden_states=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == 'mps' , 'MPS result is not consistent' )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.7_9_2_1, -0.0_9_9_2, -0.1_9_6_2, -0.7_6_9_5, -0.4_2_4_2, 0.7_8_0_4, 0.4_7_3_7, 0.2_7_6_5, 0.3_3_3_8]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : List[str] = SkipDownBlockaD # noqa F405
_A : Tuple = """down"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_skip_sample=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [-0.0_8_4_5, -0.2_0_8_7, -0.2_4_6_5, 0.0_9_7_1, 0.1_9_0_0, -0.0_4_8_4, 0.2_6_6_4, 0.4_1_7_9, 0.5_0_6_9]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : str = AttnSkipDownBlockaD # noqa F405
_A : List[str] = """down"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_skip_sample=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.5_5_3_9, 0.1_6_0_9, 0.4_9_2_4, 0.0_5_3_7, -0.1_9_9_5, 0.4_0_5_0, 0.0_9_7_9, -0.2_7_2_1, -0.0_6_4_2]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Tuple = DownEncoderBlockaD # noqa F405
_A : Optional[Any] = """down"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_temb=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = {
"in_channels": 32,
"out_channels": 32,
}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [1.1_1_0_2, 0.5_3_0_2, 0.4_8_7_2, -0.0_0_2_3, -0.8_0_4_2, 0.0_4_8_3, -0.3_4_8_9, -0.5_6_3_2, 0.7_6_2_6]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Union[str, Any] = AttnDownEncoderBlockaD # noqa F405
_A : Any = """down"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_temb=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = {
"in_channels": 32,
"out_channels": 32,
}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.8_9_6_6, -0.1_4_8_6, 0.8_5_6_8, 0.8_1_4_1, -0.9_0_4_6, -0.1_3_4_2, -0.0_9_7_2, -0.7_4_1_7, 0.1_5_3_8]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : str = UNetMidBlockaD # noqa F405
_A : Any = """mid"""
def __lowerCamelCase ( self ):
__UpperCAmelCase = {
"in_channels": 32,
"temb_channels": 128,
}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [-0.1_0_6_2, 1.7_2_4_8, 0.3_4_9_4, 1.4_5_6_9, -0.0_9_1_0, -1.2_4_2_1, -0.9_9_8_4, 0.6_7_3_6, 1.0_0_2_8]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Dict = UNetMidBlockaDCrossAttn # noqa F405
_A : int = """mid"""
def __lowerCamelCase ( self ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.0_1_8_7, 2.4_2_2_0, 0.4_4_8_4, 1.1_2_0_3, -0.6_1_2_1, -1.5_1_2_2, -0.8_2_7_0, 0.7_8_5_1, 1.8_3_3_5]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Tuple = UNetMidBlockaDSimpleCrossAttn # noqa F405
_A : List[Any] = """mid"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_encoder_hidden_states=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.7_1_4_3, 1.9_9_7_4, 0.5_4_4_8, 1.3_9_7_7, 0.1_2_8_2, -1.1_2_3_7, -1.4_2_3_8, 0.5_5_3_0, 0.8_8_8_0]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : str = UpBlockaD # noqa F405
_A : Dict = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [-0.2_0_4_1, -0.4_1_6_5, -0.3_0_2_2, 0.0_0_4_1, -0.6_6_2_8, -0.7_0_5_3, 0.1_9_2_8, -0.0_3_2_5, 0.0_5_2_3]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : List[Any] = ResnetUpsampleBlockaD # noqa F405
_A : Optional[Any] = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.2_2_8_7, 0.3_5_4_9, -0.1_3_4_6, 0.4_7_9_7, -0.1_7_1_5, -0.9_6_4_9, 0.7_3_0_5, -0.5_8_6_4, -0.6_2_4_4]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Optional[int] = CrossAttnUpBlockaD # noqa F405
_A : str = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [-0.1_4_0_3, -0.3_5_1_5, -0.0_4_2_0, -0.1_4_2_5, 0.3_1_6_7, 0.5_0_9_4, -0.2_1_8_1, 0.5_9_3_1, 0.5_5_8_2]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Optional[Any] = SimpleCrossAttnUpBlockaD # noqa F405
_A : List[Any] = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ , include_encoder_hidden_states=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.2_6_4_5, 0.1_4_8_0, 0.0_9_0_9, 0.8_0_4_4, -0.9_7_5_8, -0.9_0_8_3, 0.0_9_9_4, -1.1_4_5_3, -0.7_4_0_2]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Any = AttnUpBlockaD # noqa F405
_A : List[Any] = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ )
@unittest.skipIf(torch_device == 'mps' , 'MPS result is not consistent' )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.0_9_7_9, 0.1_3_2_6, 0.0_0_2_1, 0.0_6_5_9, 0.2_2_4_9, 0.0_0_5_9, 0.1_1_3_2, 0.5_9_5_2, 0.1_0_3_3]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Union[str, Any] = SkipUpBlockaD # noqa F405
_A : Any = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [-0.0_8_9_3, -0.1_2_3_4, -0.1_5_0_6, -0.0_3_3_2, 0.0_1_2_3, -0.0_2_1_1, 0.0_5_6_6, 0.0_1_4_3, 0.0_3_6_2]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Union[str, Any] = AttnSkipUpBlockaD # noqa F405
_A : Optional[int] = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_res_hidden_states_tuple=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.0_3_6_1, 0.0_6_1_7, 0.2_7_8_7, -0.0_3_5_0, 0.0_3_4_2, 0.3_4_2_1, -0.0_8_4_3, 0.0_9_1_3, 0.3_0_1_5]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : Tuple = UpDecoderBlockaD # noqa F405
_A : List[str] = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_temb=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = {"in_channels": 32, "out_channels": 32}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.4_4_0_4, 0.1_9_9_8, -0.9_8_8_6, -0.3_3_2_0, -0.3_1_2_8, -0.7_0_3_4, -0.6_9_5_5, -0.2_3_3_8, -0.3_1_3_7]
super().test_output(lowercase_ )
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
_A : str = AttnUpDecoderBlockaD # noqa F405
_A : Optional[int] = """up"""
@property
def __lowerCamelCase ( self ):
return super().get_dummy_input(include_temb=lowercase_ )
def __lowerCamelCase ( self ):
__UpperCAmelCase = {"in_channels": 32, "out_channels": 32}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def __lowerCamelCase ( self ):
__UpperCAmelCase = [0.6_7_3_8, 0.4_4_9_1, 0.1_0_5_5, 1.0_7_1_0, 0.7_3_1_6, 0.3_3_3_9, 0.3_3_5_2, 0.1_0_2_3, 0.3_5_6_8]
super().test_output(lowercase_ )
| 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance cannot be negative" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 670 | 0 |
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
_lowercase = logging.get_logger(__name__)
def _snake_case ( snake_case__ : int ):
if isinstance(__lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__lowerCAmelCase ):
return [[videos]]
raise ValueError(F'Could not make batched video from {videos}' )
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: str = ['''pixel_values''']
def __init__( self : Union[str, Any] ,A_ : str = True ,A_ : int = None ,A_ : Optional[int] = PILImageResampling.BILINEAR ,A_ : Optional[Any] = True ,A_ : Any = None ,A_ : str = True ,A_ : Tuple = 1 / 255 ,A_ : Any = True ,A_ : Optional[Any] = True ,A_ : Union[str, Any] = None ,A_ : Union[str, Any] = None ,**A_ : str ,) -> Optional[Any]:
super().__init__(**_lowerCAmelCase )
A = size if size is not None else {'shortest_edge': 256}
A = get_size_dict(_lowerCAmelCase ,default_to_square=_lowerCAmelCase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowerCAmelCase ,param_name='crop_size' )
A = do_resize
A = size
A = do_center_crop
A = crop_size
A = resample
A = do_rescale
A = rescale_factor
A = offset
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : str ,A_ : Dict ,A_ : int = PILImageResampling.BILINEAR ,A_ : int = None ,**A_ : int ,) -> Tuple:
A = get_size_dict(_lowerCAmelCase ,default_to_square=_lowerCAmelCase )
if "shortest_edge" in size:
A = get_resize_output_image_size(_lowerCAmelCase ,size['shortest_edge'] ,default_to_square=_lowerCAmelCase )
elif "height" in size and "width" in size:
A = (size['height'], size['width'])
else:
raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(_lowerCAmelCase ,size=_lowerCAmelCase ,resample=_lowerCAmelCase ,data_format=_lowerCAmelCase ,**_lowerCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,A_ : Any ,A_ : Optional[Any] ,A_ : Dict = None ,**A_ : List[str] ,) -> str:
A = get_size_dict(_lowerCAmelCase )
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(_lowerCAmelCase ,size=(size['height'], size['width']) ,data_format=_lowerCAmelCase ,**_lowerCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ,A_ : int ,A_ : Union[str, Any] ,A_ : Optional[Any] = True ,A_ : str = None ,**A_ : Tuple ,) -> str:
A = image.astype(np.floataa )
if offset:
A = image - (scale / 2)
return rescale(_lowerCAmelCase ,scale=_lowerCAmelCase ,data_format=_lowerCAmelCase ,**_lowerCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : Tuple ,A_ : Dict ,A_ : Tuple ,A_ : str = None ,**A_ : Tuple ,) -> Dict:
return normalize(_lowerCAmelCase ,mean=_lowerCAmelCase ,std=_lowerCAmelCase ,data_format=_lowerCAmelCase ,**_lowerCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ,A_ : Any ,A_ : Tuple = None ,A_ : Union[str, Any] = None ,A_ : str = None ,A_ : List[str] = None ,A_ : str = None ,A_ : Union[str, Any] = None ,A_ : str = None ,A_ : List[Any] = None ,A_ : Optional[int] = None ,A_ : Union[str, Any] = None ,A_ : List[Any] = None ,A_ : Tuple = ChannelDimension.FIRST ,) -> str:
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.
A = to_numpy_array(_lowerCAmelCase )
if do_resize:
A = self.resize(image=_lowerCAmelCase ,size=_lowerCAmelCase ,resample=_lowerCAmelCase )
if do_center_crop:
A = self.center_crop(_lowerCAmelCase ,size=_lowerCAmelCase )
if do_rescale:
A = self.rescale(image=_lowerCAmelCase ,scale=_lowerCAmelCase ,offset=_lowerCAmelCase )
if do_normalize:
A = self.normalize(image=_lowerCAmelCase ,mean=_lowerCAmelCase ,std=_lowerCAmelCase )
A = to_channel_dimension_format(_lowerCAmelCase ,_lowerCAmelCase )
return image
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : Union[str, Any] ,A_ : List[str] = None ,A_ : Optional[Any] = None ,A_ : List[str] = None ,A_ : Dict = None ,A_ : Union[str, Any] = None ,A_ : Optional[Any] = None ,A_ : Optional[Any] = None ,A_ : Dict = None ,A_ : List[str] = None ,A_ : Dict = None ,A_ : Union[str, Any] = None ,A_ : Dict = None ,A_ : str = ChannelDimension.FIRST ,**A_ : Any ,) -> Dict:
A = do_resize if do_resize is not None else self.do_resize
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = offset if offset is not None else self.offset
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = size if size is not None else self.size
A = get_size_dict(_lowerCAmelCase ,default_to_square=_lowerCAmelCase )
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowerCAmelCase ,param_name='crop_size' )
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.' )
A = make_batched(_lowerCAmelCase )
A = [
[
self._preprocess_image(
image=_lowerCAmelCase ,do_resize=_lowerCAmelCase ,size=_lowerCAmelCase ,resample=_lowerCAmelCase ,do_center_crop=_lowerCAmelCase ,crop_size=_lowerCAmelCase ,do_rescale=_lowerCAmelCase ,rescale_factor=_lowerCAmelCase ,offset=_lowerCAmelCase ,do_normalize=_lowerCAmelCase ,image_mean=_lowerCAmelCase ,image_std=_lowerCAmelCase ,data_format=_lowerCAmelCase ,)
for img in video
]
for video in videos
]
A = {'pixel_values': videos}
return BatchFeature(data=_lowerCAmelCase ,tensor_type=_lowerCAmelCase ) | 709 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _snake_case ( snake_case__ : int ):
A = SwinvaConfig()
A = swinva_name.split('_' )
A = name_split[1]
if "to" in name_split[3]:
A = int(name_split[3][-3:] )
else:
A = int(name_split[3] )
if "to" in name_split[2]:
A = int(name_split[2][-2:] )
else:
A = int(name_split[2][6:] )
if model_size == "tiny":
A = 96
A = (2, 2, 6, 2)
A = (3, 6, 12, 24)
elif model_size == "small":
A = 96
A = (2, 2, 18, 2)
A = (3, 6, 12, 24)
elif model_size == "base":
A = 128
A = (2, 2, 18, 2)
A = (4, 8, 16, 32)
else:
A = 192
A = (2, 2, 18, 2)
A = (6, 12, 24, 48)
if "to" in swinva_name:
A = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
A = 2_1841
A = 'huggingface/label-files'
A = 'imagenet-22k-id2label.json'
A = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
A = {int(snake_case__ ): v for k, v in idalabel.items()}
A = idalabel
A = {v: k for k, v in idalabel.items()}
else:
A = 1000
A = 'huggingface/label-files'
A = 'imagenet-1k-id2label.json'
A = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
A = {int(snake_case__ ): v for k, v in idalabel.items()}
A = idalabel
A = {v: k for k, v in idalabel.items()}
A = img_size
A = num_classes
A = embed_dim
A = depths
A = num_heads
A = window_size
return config
def _snake_case ( snake_case__ : List[Any] ):
if "patch_embed.proj" in name:
A = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
A = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
A = 'encoder.' + name
if "attn.proj" in name:
A = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
A = name.replace('attn' , 'attention.self' )
if "norm1" in name:
A = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
A = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
A = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
A = name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
A = name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
A = name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
A = name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
A = name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if name == "norm.weight":
A = 'layernorm.weight'
if name == "norm.bias":
A = 'layernorm.bias'
if "head" in name:
A = name.replace('head' , 'classifier' )
else:
A = 'swinv2.' + name
return name
def _snake_case ( snake_case__ : List[Any] , snake_case__ : List[Any] ):
for key in orig_state_dict.copy().keys():
A = orig_state_dict.pop(snake_case__ )
if "mask" in key:
continue
elif "qkv" in key:
A = key.split('.' )
A = int(key_split[1] )
A = int(key_split[3] )
A = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
A = val[:dim, :]
A = val[dim : dim * 2, :]
A = val[-dim:, :]
else:
A = val[:dim]
A = val[
dim : dim * 2
]
A = val[-dim:]
else:
A = val
return orig_state_dict
def _snake_case ( snake_case__ : Optional[int] , snake_case__ : Tuple ):
A = timm.create_model(snake_case__ , pretrained=snake_case__ )
timm_model.eval()
A = get_swinva_config(snake_case__ )
A = SwinvaForImageClassification(snake_case__ )
model.eval()
A = convert_state_dict(timm_model.state_dict() , snake_case__ )
model.load_state_dict(snake_case__ )
A = 'http://images.cocodataset.org/val2017/000000039769.jpg'
A = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_' , '-' ) ) )
A = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
A = image_processor(images=snake_case__ , return_tensors='pt' )
A = timm_model(inputs['pixel_values'] )
A = model(**snake_case__ ).logits
assert torch.allclose(snake_case__ , snake_case__ , atol=1e-3 )
print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case__ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(snake_case__ )
model.push_to_hub(
repo_path_or_name=Path(snake_case__ , snake_case__ ) , organization='nandwalritik' , commit_message='Add model' , )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 timm 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.'''
)
_lowercase = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path) | 22 | 0 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def UpperCAmelCase ( a_ , a_=False ) -> str:
"""simple docstring"""
try:
__A = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
__A = default
else:
# KEY is set, convert it to True or False.
try:
__A = strtobool(a_ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
SCREAMING_SNAKE_CASE :List[Any] = parse_flag_from_env('RUN_SLOW', default=False)
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
return unittest.skip("Test was skipped" )(a_ )
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(_run_slow_tests , "test is slow" )(a_ )
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU" )(a_ )
def UpperCAmelCase ( a_ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_xpu_available() , "test requires a XPU" )(a_ )
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`" )(a_ )
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
return unittest.skipUnless(is_tpu_available() , "test requires TPU" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU" )(a_ )
def UpperCAmelCase ( a_ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs" )(a_ )
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(is_safetensors_available() , "test requires safetensors" )(a_ )
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed" )(a_ )
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_torch_version(">=" , "1.12.0" ) , "test requires torch version >= 1.12.0" )(a_ )
def UpperCAmelCase ( a_=None , a_=None ) -> Dict:
"""simple docstring"""
if test_case is None:
return partial(a_ , version=a_ )
return unittest.skipUnless(is_torch_version(">=" , a_ ) , F'''test requires torch version >= {version}''' )(a_ )
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard" )(a_ )
def UpperCAmelCase ( a_ ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_wandb_available() , "test requires wandb" )(a_ )
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml" )(a_ )
SCREAMING_SNAKE_CASE :Optional[int] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(
_atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(a_ )
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
snake_case_ = True
@classmethod
def UpperCamelCase_ ( cls : Any ):
__A = tempfile.mkdtemp()
@classmethod
def UpperCamelCase_ ( cls : Optional[Any] ):
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def UpperCamelCase_ ( self : Tuple ):
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("**/*" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A )
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : Optional[Any] ):
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : Optional[Any] ,A : Union[mock.Mock, List[mock.Mock]] ):
__A = mocks if isinstance(A ,(tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = AcceleratorState()
__A = tensor[None].clone().to(state.device )
__A = gather(a_ ).cpu()
__A = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , a_ ):
return False
return True
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,A : List[str] ,A : Tuple ,A : Dict ):
__A = returncode
__A = stdout
__A = stderr
async def UpperCAmelCase ( a_ , a_ ) -> Union[str, Any]:
"""simple docstring"""
while True:
__A = await stream.readline()
if line:
callback(a_ )
else:
break
async def UpperCAmelCase ( a_ , a_=None , a_=None , a_=None , a_=False , a_=False ) -> _RunOutput:
"""simple docstring"""
if echo:
print("\nRunning: " , " ".join(a_ ) )
__A = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=a_ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=a_ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
__A = []
__A = []
def tee(a_ , a_ , a_ , a_="" ):
__A = line.decode("utf-8" ).rstrip()
sink.append(a_ )
if not quiet:
print(a_ , a_ , file=a_ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda a_ : tee(a_ , a_ , sys.stdout , label="stdout:" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda a_ : tee(a_ , a_ , sys.stderr , label="stderr:" ) ) ),
] , timeout=a_ , )
return _RunOutput(await p.wait() , a_ , a_ )
def UpperCAmelCase ( a_ , a_=None , a_=None , a_=1_8_0 , a_=False , a_=True ) -> _RunOutput:
"""simple docstring"""
__A = asyncio.get_event_loop()
__A = loop.run_until_complete(
_stream_subprocess(a_ , env=a_ , stdin=a_ , timeout=a_ , quiet=a_ , echo=a_ ) )
__A = " ".join(a_ )
if result.returncode > 0:
__A = "\n".join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
return result
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
def UpperCAmelCase ( a_ , a_=False ) -> Dict:
"""simple docstring"""
try:
__A = subprocess.check_output(a_ , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(a_ , "decode" ):
__A = output.decode("utf-8" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F'''Command `{' '.join(a_ )}` failed with the following error:\n\n{e.output.decode()}''' ) from e
| 55 | import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase_ : Any = logging.getLogger(__name__)
lowerCamelCase_ : int = 50 # max width of layer names
lowerCamelCase_ : Any = 70 # max width of quantizer names
def lowerCAmelCase( __lowerCamelCase ):
__a = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=__lowerCamelCase , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=__lowerCamelCase , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=__lowerCamelCase , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=__lowerCamelCase , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=__lowerCamelCase , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=__lowerCamelCase , type=__lowerCamelCase , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=__lowerCamelCase , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def lowerCAmelCase( __lowerCamelCase ):
if args.calibrator == "max":
__a = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
__a = 'histogram'
elif args.calibrator == "mse":
__a = 'histogram'
else:
raise ValueError(f'''Invalid calibrator {args.calibrator}''' )
__a = QuantDescriptor(num_bits=args.aprec , calib_method=__lowerCamelCase )
__a = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(__lowerCamelCase )
quant_nn.QuantLinear.set_default_quant_desc_weight(__lowerCamelCase )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase=False ):
logger.info('Configuring Model for Quantization' )
logger.info(f'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(__lowerCamelCase , ['embeddings'] , which='weight' , _disabled=__lowerCamelCase )
if args.quant_disable:
set_quantizer_by_name(__lowerCamelCase , [''] , _disabled=__lowerCamelCase )
if args.quant_disable_keyword:
set_quantizer_by_name(__lowerCamelCase , args.quant_disable_keyword , _disabled=__lowerCamelCase )
if args.quant_disable_layer_module:
set_quantizer_by_name(__lowerCamelCase , [r'layer.\d+.' + args.quant_disable_layer_module] , _disabled=__lowerCamelCase )
if args.quant_enable_layer_module:
set_quantizer_by_name(__lowerCamelCase , [r'layer.\d+.' + args.quant_enable_layer_module] , _disabled=__lowerCamelCase )
if args.recalibrate_weights:
recalibrate_weights(__lowerCamelCase )
if args.fuse_qkv:
fuse_qkv(__lowerCamelCase , __lowerCamelCase )
if args.clip_gelu:
clip_gelu(__lowerCamelCase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(__lowerCamelCase )
def lowerCAmelCase( __lowerCamelCase ):
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f'''{name:80}: {module}''' )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(__lowerCamelCase )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
def fusea(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
for mod in [qq, qk, qv]:
if not hasattr(__lowerCamelCase , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
__a = qq._amax.detach().item()
__a = qk._amax.detach().item()
__a = qv._amax.detach().item()
__a = max(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
qq._amax.fill_(__lowerCamelCase )
qk._amax.fill_(__lowerCamelCase )
qv._amax.fill_(__lowerCamelCase )
logger.info(f''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(f'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
__a = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCamelCase )
__a = mod._input_quantizer._amax.data.detach().item()
logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def lowerCAmelCase( __lowerCamelCase ):
for name, mod in model.named_modules():
if hasattr(__lowerCamelCase , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
__a = mod.weight.shape[0]
__a = mod._weight_quantizer._amax.detach()
__a = torch.ones(__lowerCamelCase , dtype=amax.dtype , device=amax.device ) * amax
print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def lowerCAmelCase( __lowerCamelCase ):
for name, mod in model.named_modules():
if hasattr(__lowerCamelCase , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
__a = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
__a = set(range(len(mod.weight.size() ) ) ) - axis_set
__a = pytorch_quantization.utils.reduce_amax(mod.weight , axis=__lowerCamelCase , keepdims=__lowerCamelCase ).detach()
logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
__a = amax
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase=25 , __lowerCamelCase=180 , __lowerCamelCase=None ):
if ignore is None:
__a = []
elif not isinstance(__lowerCamelCase , __lowerCamelCase ):
__a = [ignore]
__a = 0
for name, mod in model.named_modules():
if not hasattr(__lowerCamelCase , 'weight' ):
continue
__a = max(__lowerCamelCase , len(__lowerCamelCase ) )
for name, mod in model.named_modules():
__a = getattr(__lowerCamelCase , '_input_quantizer' , __lowerCamelCase )
__a = getattr(__lowerCamelCase , '_weight_quantizer' , __lowerCamelCase )
if not hasattr(__lowerCamelCase , 'weight' ):
continue
if type(__lowerCamelCase ) in ignore:
continue
if [True for s in ignore if type(__lowerCamelCase ) is str and s in name]:
continue
__a = f'''Act:{input_q.extra_repr()}'''
__a = f'''Wgt:{weight_q.extra_repr()}'''
__a = f'''{name:{name_width}} {act_str} {wgt_str}'''
if len(__lowerCamelCase ) <= line_width:
logger.info(__lowerCamelCase )
else:
logger.info(f'''{name:{name_width}} {act_str}''' )
logger.info(f'''{" ":{name_width}} {wgt_str}''' )
def lowerCAmelCase( __lowerCamelCase ):
__a = 0
for name, mod in model.named_modules():
if isinstance(__lowerCamelCase , pytorch_quantization.nn.TensorQuantizer ):
print(f'''{name:80} {mod}''' )
count += 1
print(f'''{count} TensorQuantizers found in model''' )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__a = getattr(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if quantizer_mod is not None:
assert hasattr(__lowerCamelCase , __lowerCamelCase )
setattr(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
logger.warning(f'''{name} has no {quantizer}''' )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase="both" , **__lowerCamelCase ):
__a = f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(__lowerCamelCase , __lowerCamelCase , '_input_quantizer' , __lowerCamelCase , __lowerCamelCase )
if which in ["weight", "both"]:
set_quantizer(__lowerCamelCase , __lowerCamelCase , '_weight_quantizer' , __lowerCamelCase , __lowerCamelCase )
logger.info(__lowerCamelCase )
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase ):
for name, mod in model.named_modules():
if hasattr(__lowerCamelCase , '_input_quantizer' ) or hasattr(__lowerCamelCase , '_weight_quantizer' ):
for n in names:
if re.search(__lowerCamelCase , __lowerCamelCase ):
set_quantizers(__lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(__lowerCamelCase , __lowerCamelCase ):
__a = f'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
setattr(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
logger.info(__lowerCamelCase )
| 559 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""tiiuae/falcon-40b""": """https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json""",
"""tiiuae/falcon-7b""": """https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json""",
}
class lowercase ( _lowercase ):
"""simple docstring"""
a__ = "falcon"
a__ = ["past_key_values"]
def __init__( self , __snake_case=6_50_24 , __snake_case=45_44 , __snake_case=32 , __snake_case=71 , __snake_case=1e-5 , __snake_case=0.0_2 , __snake_case=True , __snake_case=0.0 , __snake_case=0.0 , __snake_case=None , __snake_case=False , __snake_case=False , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=11 , __snake_case=11 , **__snake_case , ):
_UpperCamelCase : List[Any] = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCamelCase : Tuple = kwargs.pop('n_embed' , __snake_case)
_UpperCamelCase : Union[str, Any] = hidden_size if n_embed is None else n_embed
_UpperCamelCase : str = num_hidden_layers
_UpperCamelCase : str = num_attention_heads
_UpperCamelCase : Optional[Any] = layer_norm_epsilon
_UpperCamelCase : str = initializer_range
_UpperCamelCase : Optional[Any] = use_cache
_UpperCamelCase : List[str] = hidden_dropout
_UpperCamelCase : Optional[Any] = attention_dropout
_UpperCamelCase : Optional[Any] = bos_token_id
_UpperCamelCase : Union[str, Any] = eos_token_id
_UpperCamelCase : str = num_attention_heads if num_kv_heads is None else num_kv_heads
_UpperCamelCase : List[Any] = alibi
_UpperCamelCase : Union[str, Any] = new_decoder_architecture
_UpperCamelCase : str = multi_query # Ignored when new_decoder_architecture is True
_UpperCamelCase : int = parallel_attn
_UpperCamelCase : List[str] = bias
super().__init__(bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case)
@property
def A__ ( self):
return self.hidden_size // self.num_attention_heads
@property
def A__ ( self):
return not self.alibi
| 648 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 648 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# ===== initialization =====
_snake_case : int = Mock()
_snake_case : Optional[int] = conn, Mock()
_snake_case : int = iter([1, None] )
_snake_case : Dict = lambda __lowerCAmelCase : next(__lowerCAmelCase )
# ===== invoke =====
send_file(filename='mytext.txt' , testing=__lowerCAmelCase )
# ===== 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()
| 304 | """simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _A( lowerCAmelCase ):
def decorator(lowerCAmelCase ):
A__ : Any = getattr(lowerCAmelCase , """handle_key""" , [] )
handle += [key]
setattr(lowerCAmelCase , """handle_key""" , lowerCAmelCase )
return func
return decorator
def _A( *lowerCAmelCase ):
def decorator(lowerCAmelCase ):
A__ : Optional[Any] = getattr(lowerCAmelCase , """handle_key""" , [] )
handle += keys
setattr(lowerCAmelCase , """handle_key""" , lowerCAmelCase )
return func
return decorator
class __UpperCAmelCase (__A ):
'''simple docstring'''
def __new__( cls , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
A__ : Union[str, Any] = super().__new__(cls , snake_case_ , snake_case_ , snake_case_ )
if not hasattr(snake_case_ , """key_handler""" ):
setattr(snake_case_ , """key_handler""" , {} )
setattr(snake_case_ , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
A__ : Dict = getattr(snake_case_ , """handle_key""" , [] )
for key in handled_keys:
A__ : Optional[Any] = value
return new_cls
@staticmethod
def lowerCamelCase ( cls ):
'''simple docstring'''
A__ : int = get_character()
if char != KEYMAP["undefined"]:
A__ : Union[str, Any] = ord(snake_case_ )
A__ : int = cls.key_handler.get(snake_case_ )
if handler:
A__ : int = char
return handler(cls )
else:
return None
def _A( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 363 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
def _lowerCAmelCase ( A__: Tuple , A__: Any ):
'''simple docstring'''
UpperCAmelCase = nn.functional.normalize(A__ )
UpperCAmelCase = nn.functional.normalize(A__ )
return torch.mm(A__ , normalized_text_embeds.t() )
class lowercase ( A__ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = CLIPConfig
__SCREAMING_SNAKE_CASE = ["""CLIPEncoderLayer"""]
def __init__( self , _snake_case ) -> Optional[Any]:
"""simple docstring"""
super().__init__(_snake_case )
UpperCAmelCase = CLIPVisionModel(config.vision_config )
UpperCAmelCase = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=_snake_case )
UpperCAmelCase = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=_snake_case )
UpperCAmelCase = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=_snake_case )
UpperCAmelCase = nn.Parameter(torch.ones(17 ) , requires_grad=_snake_case )
UpperCAmelCase = nn.Parameter(torch.ones(3 ) , requires_grad=_snake_case )
@torch.no_grad()
def snake_case_ ( self , _snake_case , _snake_case ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.vision_model(_snake_case )[1] # pooled_output
UpperCAmelCase = self.visual_projection(_snake_case )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase = cosine_distance(_snake_case , self.special_care_embeds ).cpu().float().numpy()
UpperCAmelCase = cosine_distance(_snake_case , self.concept_embeds ).cpu().float().numpy()
UpperCAmelCase = []
UpperCAmelCase = image_embeds.shape[0]
for i in range(_snake_case ):
UpperCAmelCase = {'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
UpperCAmelCase = special_cos_dist[i][concept_idx]
UpperCAmelCase = self.special_care_embeds_weights[concept_idx].item()
UpperCAmelCase = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]} )
UpperCAmelCase = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
UpperCAmelCase = cos_dist[i][concept_idx]
UpperCAmelCase = self.concept_embeds_weights[concept_idx].item()
UpperCAmelCase = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(_snake_case )
result.append(_snake_case )
UpperCAmelCase = [len(res['''bad_concepts'''] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def snake_case_ ( self , _snake_case , _snake_case ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.vision_model(_snake_case )[1] # pooled_output
UpperCAmelCase = self.visual_projection(_snake_case )
UpperCAmelCase = cosine_distance(_snake_case , self.special_care_embeds )
UpperCAmelCase = cosine_distance(_snake_case , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase = 0.0
UpperCAmelCase = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
UpperCAmelCase = torch.any(special_scores > 0 , dim=1 )
UpperCAmelCase = special_care * 0.01
UpperCAmelCase = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
UpperCAmelCase = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
UpperCAmelCase = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 391 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
__magic_name__ = logging.getLogger(__name__)
class lowercase ( A__ ):
'''simple docstring'''
def snake_case_ ( self , _snake_case , _snake_case , _snake_case=None , _snake_case=None ) -> Any:
"""simple docstring"""
UpperCAmelCase = self.layer[current_layer](_snake_case , _snake_case , head_mask[current_layer] )
UpperCAmelCase = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"""The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.""" , A__ , )
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case ) -> Optional[Any]:
"""simple docstring"""
super().__init__(_snake_case )
UpperCAmelCase = BertEncoderWithPabee(_snake_case )
self.init_weights()
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
def snake_case_ ( self , _snake_case ) -> int:
"""simple docstring"""
UpperCAmelCase = threshold
def snake_case_ ( self , _snake_case ) -> str:
"""simple docstring"""
UpperCAmelCase = patience
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = 0
UpperCAmelCase = 0
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.inference_layers_num / self.inference_instances_num
UpperCAmelCase = (
f"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
f""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(_snake_case )
@add_start_docstrings_to_model_forward(_snake_case )
def snake_case_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=False , ) -> List[Any]:
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
UpperCAmelCase = input_ids.size()
elif inputs_embeds is not None:
UpperCAmelCase = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
UpperCAmelCase = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
UpperCAmelCase = torch.ones(_snake_case , device=_snake_case )
if token_type_ids is None:
UpperCAmelCase = torch.zeros(_snake_case , dtype=torch.long , device=_snake_case )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
UpperCAmelCase = self.get_extended_attention_mask(_snake_case , _snake_case , _snake_case )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = encoder_hidden_states.size()
UpperCAmelCase = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
UpperCAmelCase = torch.ones(_snake_case , device=_snake_case )
UpperCAmelCase = self.invert_attention_mask(_snake_case )
else:
UpperCAmelCase = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
UpperCAmelCase = self.get_head_mask(_snake_case , self.config.num_hidden_layers )
UpperCAmelCase = self.embeddings(
input_ids=_snake_case , position_ids=_snake_case , token_type_ids=_snake_case , inputs_embeds=_snake_case )
UpperCAmelCase = embedding_output
if self.training:
UpperCAmelCase = []
for i in range(self.config.num_hidden_layers ):
UpperCAmelCase = self.encoder.adaptive_forward(
_snake_case , current_layer=_snake_case , attention_mask=_snake_case , head_mask=_snake_case )
UpperCAmelCase = self.pooler(_snake_case )
UpperCAmelCase = output_layers[i](output_dropout(_snake_case ) )
res.append(_snake_case )
elif self.patience == 0: # Use all layers for inference
UpperCAmelCase = self.encoder(
_snake_case , attention_mask=_snake_case , head_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
UpperCAmelCase = self.pooler(encoder_outputs[0] )
UpperCAmelCase = [output_layers[self.config.num_hidden_layers - 1](_snake_case )]
else:
UpperCAmelCase = 0
UpperCAmelCase = None
UpperCAmelCase = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
UpperCAmelCase = self.encoder.adaptive_forward(
_snake_case , current_layer=_snake_case , attention_mask=_snake_case , head_mask=_snake_case )
UpperCAmelCase = self.pooler(_snake_case )
UpperCAmelCase = output_layers[i](_snake_case )
if regression:
UpperCAmelCase = logits.detach()
if patient_result is not None:
UpperCAmelCase = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = logits.detach().argmax(dim=1 )
if patient_result is not None:
UpperCAmelCase = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(_snake_case ) ):
patient_counter += 1
else:
UpperCAmelCase = 0
UpperCAmelCase = logits
if patient_counter == self.patience:
break
UpperCAmelCase = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"""Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """ , A__ , )
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case ) -> Optional[int]:
"""simple docstring"""
super().__init__(_snake_case )
UpperCAmelCase = config.num_labels
UpperCAmelCase = BertModelWithPabee(_snake_case )
UpperCAmelCase = nn.Dropout(config.hidden_dropout_prob )
UpperCAmelCase = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(_snake_case )
def snake_case_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = self.bert(
input_ids=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , position_ids=_snake_case , head_mask=_snake_case , inputs_embeds=_snake_case , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
UpperCAmelCase = (logits[-1],)
if labels is not None:
UpperCAmelCase = None
UpperCAmelCase = 0
for ix, logits_item in enumerate(_snake_case ):
if self.num_labels == 1:
# We are doing regression
UpperCAmelCase = MSELoss()
UpperCAmelCase = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
UpperCAmelCase = CrossEntropyLoss()
UpperCAmelCase = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
UpperCAmelCase = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
UpperCAmelCase = (total_loss / total_weights,) + outputs
return outputs
| 391 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
_lowerCAmelCase = {
'Acehnese Arabic': 'ace_Arab',
'Acehnese Latin': 'ace_Latn',
'Mesopotamian Arabic': 'acm_Arab',
'Ta\'izzi-Adeni Arabic': 'acq_Arab',
'Tunisian Arabic': 'aeb_Arab',
'Afrikaans': 'afr_Latn',
'South Levantine Arabic': 'ajp_Arab',
'Akan': 'aka_Latn',
'Amharic': 'amh_Ethi',
'North Levantine Arabic': 'apc_Arab',
'Modern Standard Arabic': 'arb_Arab',
'Modern Standard Arabic Romanized': 'arb_Latn',
'Najdi Arabic': 'ars_Arab',
'Moroccan Arabic': 'ary_Arab',
'Egyptian Arabic': 'arz_Arab',
'Assamese': 'asm_Beng',
'Asturian': 'ast_Latn',
'Awadhi': 'awa_Deva',
'Central Aymara': 'ayr_Latn',
'South Azerbaijani': 'azb_Arab',
'North Azerbaijani': 'azj_Latn',
'Bashkir': 'bak_Cyrl',
'Bambara': 'bam_Latn',
'Balinese': 'ban_Latn',
'Belarusian': 'bel_Cyrl',
'Bemba': 'bem_Latn',
'Bengali': 'ben_Beng',
'Bhojpuri': 'bho_Deva',
'Banjar Arabic': 'bjn_Arab',
'Banjar Latin': 'bjn_Latn',
'Standard Tibetan': 'bod_Tibt',
'Bosnian': 'bos_Latn',
'Buginese': 'bug_Latn',
'Bulgarian': 'bul_Cyrl',
'Catalan': 'cat_Latn',
'Cebuano': 'ceb_Latn',
'Czech': 'ces_Latn',
'Chokwe': 'cjk_Latn',
'Central Kurdish': 'ckb_Arab',
'Crimean Tatar': 'crh_Latn',
'Welsh': 'cym_Latn',
'Danish': 'dan_Latn',
'German': 'deu_Latn',
'Southwestern Dinka': 'dik_Latn',
'Dyula': 'dyu_Latn',
'Dzongkha': 'dzo_Tibt',
'Greek': 'ell_Grek',
'English': 'eng_Latn',
'Esperanto': 'epo_Latn',
'Estonian': 'est_Latn',
'Basque': 'eus_Latn',
'Ewe': 'ewe_Latn',
'Faroese': 'fao_Latn',
'Fijian': 'fij_Latn',
'Finnish': 'fin_Latn',
'Fon': 'fon_Latn',
'French': 'fra_Latn',
'Friulian': 'fur_Latn',
'Nigerian Fulfulde': 'fuv_Latn',
'Scottish Gaelic': 'gla_Latn',
'Irish': 'gle_Latn',
'Galician': 'glg_Latn',
'Guarani': 'grn_Latn',
'Gujarati': 'guj_Gujr',
'Haitian Creole': 'hat_Latn',
'Hausa': 'hau_Latn',
'Hebrew': 'heb_Hebr',
'Hindi': 'hin_Deva',
'Chhattisgarhi': 'hne_Deva',
'Croatian': 'hrv_Latn',
'Hungarian': 'hun_Latn',
'Armenian': 'hye_Armn',
'Igbo': 'ibo_Latn',
'Ilocano': 'ilo_Latn',
'Indonesian': 'ind_Latn',
'Icelandic': 'isl_Latn',
'Italian': 'ita_Latn',
'Javanese': 'jav_Latn',
'Japanese': 'jpn_Jpan',
'Kabyle': 'kab_Latn',
'Jingpho': 'kac_Latn',
'Kamba': 'kam_Latn',
'Kannada': 'kan_Knda',
'Kashmiri Arabic': 'kas_Arab',
'Kashmiri Devanagari': 'kas_Deva',
'Georgian': 'kat_Geor',
'Central Kanuri Arabic': 'knc_Arab',
'Central Kanuri Latin': 'knc_Latn',
'Kazakh': 'kaz_Cyrl',
'Kabiyè': 'kbp_Latn',
'Kabuverdianu': 'kea_Latn',
'Khmer': 'khm_Khmr',
'Kikuyu': 'kik_Latn',
'Kinyarwanda': 'kin_Latn',
'Kyrgyz': 'kir_Cyrl',
'Kimbundu': 'kmb_Latn',
'Northern Kurdish': 'kmr_Latn',
'Kikongo': 'kon_Latn',
'Korean': 'kor_Hang',
'Lao': 'lao_Laoo',
'Ligurian': 'lij_Latn',
'Limburgish': 'lim_Latn',
'Lingala': 'lin_Latn',
'Lithuanian': 'lit_Latn',
'Lombard': 'lmo_Latn',
'Latgalian': 'ltg_Latn',
'Luxembourgish': 'ltz_Latn',
'Luba-Kasai': 'lua_Latn',
'Ganda': 'lug_Latn',
'Luo': 'luo_Latn',
'Mizo': 'lus_Latn',
'Standard Latvian': 'lvs_Latn',
'Magahi': 'mag_Deva',
'Maithili': 'mai_Deva',
'Malayalam': 'mal_Mlym',
'Marathi': 'mar_Deva',
'Minangkabau Arabic ': 'min_Arab',
'Minangkabau Latin': 'min_Latn',
'Macedonian': 'mkd_Cyrl',
'Plateau Malagasy': 'plt_Latn',
'Maltese': 'mlt_Latn',
'Meitei Bengali': 'mni_Beng',
'Halh Mongolian': 'khk_Cyrl',
'Mossi': 'mos_Latn',
'Maori': 'mri_Latn',
'Burmese': 'mya_Mymr',
'Dutch': 'nld_Latn',
'Norwegian Nynorsk': 'nno_Latn',
'Norwegian Bokmål': 'nob_Latn',
'Nepali': 'npi_Deva',
'Northern Sotho': 'nso_Latn',
'Nuer': 'nus_Latn',
'Nyanja': 'nya_Latn',
'Occitan': 'oci_Latn',
'West Central Oromo': 'gaz_Latn',
'Odia': 'ory_Orya',
'Pangasinan': 'pag_Latn',
'Eastern Panjabi': 'pan_Guru',
'Papiamento': 'pap_Latn',
'Western Persian': 'pes_Arab',
'Polish': 'pol_Latn',
'Portuguese': 'por_Latn',
'Dari': 'prs_Arab',
'Southern Pashto': 'pbt_Arab',
'Ayacucho Quechua': 'quy_Latn',
'Romanian': 'ron_Latn',
'Rundi': 'run_Latn',
'Russian': 'rus_Cyrl',
'Sango': 'sag_Latn',
'Sanskrit': 'san_Deva',
'Santali': 'sat_Olck',
'Sicilian': 'scn_Latn',
'Shan': 'shn_Mymr',
'Sinhala': 'sin_Sinh',
'Slovak': 'slk_Latn',
'Slovenian': 'slv_Latn',
'Samoan': 'smo_Latn',
'Shona': 'sna_Latn',
'Sindhi': 'snd_Arab',
'Somali': 'som_Latn',
'Southern Sotho': 'sot_Latn',
'Spanish': 'spa_Latn',
'Tosk Albanian': 'als_Latn',
'Sardinian': 'srd_Latn',
'Serbian': 'srp_Cyrl',
'Swati': 'ssw_Latn',
'Sundanese': 'sun_Latn',
'Swedish': 'swe_Latn',
'Swahili': 'swh_Latn',
'Silesian': 'szl_Latn',
'Tamil': 'tam_Taml',
'Tatar': 'tat_Cyrl',
'Telugu': 'tel_Telu',
'Tajik': 'tgk_Cyrl',
'Tagalog': 'tgl_Latn',
'Thai': 'tha_Thai',
'Tigrinya': 'tir_Ethi',
'Tamasheq Latin': 'taq_Latn',
'Tamasheq Tifinagh': 'taq_Tfng',
'Tok Pisin': 'tpi_Latn',
'Tswana': 'tsn_Latn',
'Tsonga': 'tso_Latn',
'Turkmen': 'tuk_Latn',
'Tumbuka': 'tum_Latn',
'Turkish': 'tur_Latn',
'Twi': 'twi_Latn',
'Central Atlas Tamazight': 'tzm_Tfng',
'Uyghur': 'uig_Arab',
'Ukrainian': 'ukr_Cyrl',
'Umbundu': 'umb_Latn',
'Urdu': 'urd_Arab',
'Northern Uzbek': 'uzn_Latn',
'Venetian': 'vec_Latn',
'Vietnamese': 'vie_Latn',
'Waray': 'war_Latn',
'Wolof': 'wol_Latn',
'Xhosa': 'xho_Latn',
'Eastern Yiddish': 'ydd_Hebr',
'Yoruba': 'yor_Latn',
'Yue Chinese': 'yue_Hant',
'Chinese Simplified': 'zho_Hans',
'Chinese Traditional': 'zho_Hant',
'Standard Malay': 'zsm_Latn',
'Zulu': 'zul_Latn',
}
class UpperCamelCase (__snake_case ):
_SCREAMING_SNAKE_CASE : Any = """facebook/nllb-200-distilled-600M"""
_SCREAMING_SNAKE_CASE : str = (
"""This is a tool that translates text from a language to another. It takes three inputs: `text`, which should """
"""be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, """
"""which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in """
"""plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."""
)
_SCREAMING_SNAKE_CASE : Optional[int] = """translator"""
_SCREAMING_SNAKE_CASE : Any = AutoTokenizer
_SCREAMING_SNAKE_CASE : str = AutoModelForSeqaSeqLM
_SCREAMING_SNAKE_CASE : str = LANGUAGE_CODES
_SCREAMING_SNAKE_CASE : Dict = ["""text""", """text""", """text"""]
_SCREAMING_SNAKE_CASE : Tuple = ["""text"""]
def __snake_case ( self :Union[str, Any] , __magic_name__ :Optional[int] , __magic_name__ :Dict , __magic_name__ :int ) ->Any:
if src_lang not in self.lang_to_code:
raise ValueError(f"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(f"""{tgt_lang} is not a supported language.""" )
lowercase : Optional[Any] = self.lang_to_code[src_lang]
lowercase : int = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
__magic_name__ , return_tensors="""pt""" , src_lang=__magic_name__ , tgt_lang=__magic_name__ )
def __snake_case ( self :List[str] , __magic_name__ :List[str] ) ->Dict:
return self.model.generate(**__magic_name__ )
def __snake_case ( self :Tuple , __magic_name__ :Optional[int] ) ->List[Any]:
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=__magic_name__ )
| 264 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
_lowerCAmelCase = {
'b0': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_24,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_40,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 14_08,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_60,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 15_36,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_00,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 17_92,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_80,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 20_48,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_56,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 23_04,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_28,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 25_60,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_00,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def UpperCamelCase ( _A ) -> List[str]:
lowercase : List[str] = EfficientNetConfig()
lowercase : Any = CONFIG_MAP[model_name]["""hidden_dim"""]
lowercase : List[str] = CONFIG_MAP[model_name]["""width_coef"""]
lowercase : str = CONFIG_MAP[model_name]["""depth_coef"""]
lowercase : int = CONFIG_MAP[model_name]["""image_size"""]
lowercase : List[Any] = CONFIG_MAP[model_name]["""dropout_rate"""]
lowercase : int = CONFIG_MAP[model_name]["""dw_padding"""]
lowercase : Optional[int] = """huggingface/label-files"""
lowercase : int = """imagenet-1k-id2label.json"""
lowercase : Any = 1_000
lowercase : Any = json.load(open(hf_hub_download(_A , _A , repo_type="""dataset""" ) , """r""" ) )
lowercase : Optional[int] = {int(_A ): v for k, v in idalabel.items()}
lowercase : int = idalabel
lowercase : Optional[Any] = {v: k for k, v in idalabel.items()}
return config
def UpperCamelCase ( ) -> Tuple:
lowercase : Any = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase : Optional[int] = Image.open(requests.get(_A , stream=_A ).raw )
return im
def UpperCamelCase ( _A ) -> Optional[Any]:
lowercase : str = CONFIG_MAP[model_name]["""image_size"""]
lowercase : Optional[int] = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=_A , )
return preprocessor
def UpperCamelCase ( _A ) -> Optional[int]:
lowercase : Union[str, Any] = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
lowercase : Optional[Any] = sorted(set(_A ) )
lowercase : Dict = len(_A )
lowercase : List[str] = {b: str(_A ) for b, i in zip(_A , range(_A ) )}
lowercase : Union[str, Any] = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
lowercase : str = block_name_mapping[b]
rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
rename_keys.append(
(F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
rename_keys.append(
(F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
rename_keys.append(
(F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )
rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
rename_keys.append(
(F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
lowercase : Union[str, Any] = {}
for item in rename_keys:
if item[0] in original_param_names:
lowercase : Optional[int] = """efficientnet.""" + item[1]
lowercase : Any = """classifier.weight"""
lowercase : Tuple = """classifier.bias"""
return key_mapping
def UpperCamelCase ( _A , _A , _A ) -> Optional[Any]:
for key, value in tf_params.items():
if "normalization" in key:
continue
lowercase : List[Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
lowercase : str = torch.from_numpy(_A ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
lowercase : Optional[int] = torch.from_numpy(_A ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
lowercase : List[Any] = torch.from_numpy(np.transpose(_A ) )
else:
lowercase : Optional[int] = torch.from_numpy(_A )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_A )
@torch.no_grad()
def UpperCamelCase ( _A , _A , _A , _A ) -> str:
lowercase : Any = model_classes[model_name](
include_top=_A , weights="""imagenet""" , input_tensor=_A , input_shape=_A , pooling=_A , classes=1_000 , classifier_activation="""softmax""" , )
lowercase : Dict = original_model.trainable_variables
lowercase : Any = original_model.non_trainable_variables
lowercase : Any = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
lowercase : Dict = param.numpy()
lowercase : List[str] = list(tf_params.keys() )
# Load HuggingFace model
lowercase : str = get_efficientnet_config(_A )
lowercase : List[Any] = EfficientNetForImageClassification(_A ).eval()
lowercase : Optional[int] = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
lowercase : int = rename_keys(_A )
replace_params(_A , _A , _A )
# Initialize preprocessor and preprocess input image
lowercase : Optional[int] = convert_image_processor(_A )
lowercase : Any = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
lowercase : Union[str, Any] = hf_model(**_A )
lowercase : List[Any] = outputs.logits.detach().numpy()
# Original model inference
lowercase : Optional[Any] = False
lowercase : str = CONFIG_MAP[model_name]["""image_size"""]
lowercase : Optional[Any] = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
lowercase : Optional[Any] = image.img_to_array(_A )
lowercase : Dict = np.expand_dims(_A , axis=0 )
lowercase : List[str] = original_model.predict(_A )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_A , _A , atol=1e-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(_A ):
os.mkdir(_A )
# Save converted model and image processor
hf_model.save_pretrained(_A )
preprocessor.save_pretrained(_A )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
lowercase : Dict = F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(_A )
hf_model.push_to_hub(_A )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
_lowerCAmelCase = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 264 | 1 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
_lowercase = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
_lowercase = spec.loader.load_module()
_lowercase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowercase = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
_lowercase = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCAmelCase__ ( )-> int:
A__ = []
for config_class in list(CONFIG_MAPPING.values() ):
A__ = False
# source code of `config_class`
A__ = inspect.getsource(UpperCamelCase_ )
A__ = _re_checkpoint.findall(UpperCamelCase_ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
A__ , A__ = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
A__ = f"https://huggingface.co/{ckpt_name}"
if ckpt_link == ckpt_link_from_name:
A__ = True
break
A__ = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(UpperCamelCase_ )
if len(UpperCamelCase_ ) > 0:
A__ = '''\n'''.join(sorted(UpperCamelCase_ ) )
raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 526 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = "▁"
_lowercase = {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}
_lowercase = {
"sentencepiece_model_file": "sentencepiece.bpe.model",
"vocab_file": "vocab.txt",
}
_lowercase = {
"vocab_file": {
"ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
"ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
},
"sentencepiece_model_file": {
"ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
"ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
},
}
_lowercase = {
"ernie-m-base": 514,
"ernie-m-large": 514,
}
_lowercase = {
"ernie-m-base": {"do_lower_case": False},
"ernie-m-large": {"do_lower_case": False},
}
class _UpperCAmelCase ( A__ ):
UpperCamelCase__ = ["input_ids"]
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = RESOURCE_FILES_NAMES
def __init__( self , a__ , a__=None , a__=False , a__="utf8" , a__="[UNK]" , a__="[SEP]" , a__="[PAD]" , a__="[CLS]" , a__="[MASK]" , a__ = None , **a__ , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
A__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=a__ , unk_token=a__ , sep_token=a__ , pad_token=a__ , cls_token=a__ , mask_token=a__ , vocab_file=a__ , encoding=a__ , sp_model_kwargs=self.sp_model_kwargs , **a__ , )
A__ = do_lower_case
A__ = sentencepiece_model_ckpt
A__ = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(a__)
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
A__ = self.load_vocab(filepath=a__)
else:
A__ = {self.sp_model.id_to_piece(a__): id for id in range(self.sp_model.get_piece_size())}
A__ = {v: k for k, v in self.vocab.items()}
def snake_case_ ( self , a__):
if text is None:
return None
A__ = self.tokenize(a__)
A__ , A__ = '''''', []
for i, ch in enumerate(a__):
if ch in self.SP_CHAR_MAPPING:
A__ = self.SP_CHAR_MAPPING.get(a__)
else:
A__ = unicodedata.normalize('''NFKC''' , a__)
if self.is_whitespace(a__):
continue
normalized_text += ch
char_mapping.extend([i] * len(a__))
A__ , A__ , A__ = normalized_text, [], 0
if self.do_lower_case:
A__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
A__ = token[1:]
A__ = text[offset:].index(a__) + offset
A__ = start + len(a__)
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
A__ = end
return token_mapping
@property
def snake_case_ ( self):
return len(self.vocab)
def snake_case_ ( self):
return dict(self.vocab , **self.added_tokens_encoder)
def __getstate__( self):
A__ = self.__dict__.copy()
A__ = None
return state
def __setstate__( self , a__):
A__ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
A__ = {}
A__ = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.sentencepiece_model_ckpt)
def snake_case_ ( self , a__):
return "".join((self.SP_CHAR_MAPPING.get(a__ , a__) for c in text))
def snake_case_ ( self , a__ , a__=False , a__=6_4 , a__=0.1):
if self.sp_model_kwargs.get('''enable_sampling''') is True:
A__ = True
if self.sp_model_kwargs.get('''alpha''') is not None:
A__ = self.sp_model_kwargs.get('''alpha''')
if self.sp_model_kwargs.get('''nbest_size''') is not None:
A__ = self.sp_model_kwargs.get('''nbest_size''')
if not enable_sampling:
A__ = self.sp_model.EncodeAsPieces(a__)
else:
A__ = self.sp_model.SampleEncodeAsPieces(a__ , a__ , a__)
A__ = []
for pi, piece in enumerate(a__):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(a__) and pi != 0:
new_pieces.append(a__)
continue
else:
continue
A__ = 0
for i, chunk in enumerate(a__):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(a__) or self.is_punct(a__):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
new_pieces.append(a__)
A__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
A__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
A__ = i
if len(a__) > lst_i:
new_pieces.append(piece[lst_i:])
return new_pieces
def snake_case_ ( self , a__):
A__ = ''''''.join(a__).replace(a__ , ''' ''').strip()
return out_string
def snake_case_ ( self , a__):
A__ = self.convert_ids_to_tokens(a__)
A__ = ''''''.join(a__).replace(a__ , ''' ''').strip()
return out_string
def snake_case_ ( self , a__):
return self.vocab.get(a__ , self.vocab.get(self.unk_token))
def snake_case_ ( self , a__):
return self.reverse_vocab.get(a__ , self.unk_token)
def snake_case_ ( self , a__ , a__=None):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A__ = [self.cls_token_id]
A__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def snake_case_ ( self , a__ , a__=None):
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def snake_case_ ( self , a__ , a__=None , a__=False):
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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(a__)) + [1, 1] + ([0] * len(a__)) + [1]
return [1] + ([0] * len(a__)) + [1]
def snake_case_ ( self , a__ , a__ = None):
# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
if token_ids_a is None:
# [CLS] X [SEP]
return (len(a__) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(a__) + 1) + [1] * (len(a__) + 3)
def snake_case_ ( self , a__):
if "\u4e00" <= char <= "\u9fff":
return True
return False
def snake_case_ ( self , a__):
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def snake_case_ ( self , a__):
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def snake_case_ ( self , a__):
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(a__) == 1:
A__ = unicodedata.category(a__)
if cat == "Zs":
return True
return False
def snake_case_ ( self , a__):
A__ = {}
with io.open(a__ , '''r''' , encoding='''utf-8''') as f:
for index, line in enumerate(a__):
A__ = line.rstrip('''\n''')
A__ = int(a__)
return token_to_idx
def snake_case_ ( self , a__ , a__ = None):
A__ = 0
if os.path.isdir(a__):
A__ = os.path.join(
a__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
else:
A__ = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(a__ , '''w''' , encoding='''utf-8''') as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda a__: kv[1]):
if index != token_index:
logger.warning(
F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
''' Please check that the vocabulary is not corrupted!''')
A__ = token_index
writer.write(token + '''\n''')
index += 1
A__ = os.path.join(a__ , '''sentencepiece.bpe.model''')
with open(a__ , '''wb''') as fi:
A__ = self.sp_model.serialized_model_proto()
fi.write(a__)
return (vocab_file,)
| 526 | 1 |
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
a : int = "\\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"
a : int = "\\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"
a : Any = "\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 a ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
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 UpperCAmelCase ( self : Optional[Any] , __lowercase : Tuple , __lowercase : str , __lowercase : Optional[Any]=None , __lowercase : List[str]=True , __lowercase : Optional[Any]=False ) -> Dict:
if rouge_types is None:
__UpperCAmelCase : Union[str, Any] = ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]
__UpperCAmelCase : int = rouge_scorer.RougeScorer(rouge_types=__lowercase , use_stemmer=__lowercase )
if use_aggregator:
__UpperCAmelCase : Any = scoring.BootstrapAggregator()
else:
__UpperCAmelCase : Union[str, Any] = []
for ref, pred in zip(__lowercase , __lowercase ):
__UpperCAmelCase : Optional[Any] = scorer.score(__lowercase , __lowercase )
if use_aggregator:
aggregator.add_scores(__lowercase )
else:
scores.append(__lowercase )
if use_aggregator:
__UpperCAmelCase : List[Any] = aggregator.aggregate()
else:
__UpperCAmelCase : Optional[Any] = {}
for key in scores[0]:
__UpperCAmelCase : Union[str, Any] = [score[key] for score in scores]
return result
| 63 |
"""simple docstring"""
from itertools import permutations
def lowercase ( _SCREAMING_SNAKE_CASE : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_SCREAMING_SNAKE_CASE ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 10 ):
'''simple docstring'''
return sum(
int(''''''.join(map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) )
for num in permutations(range(_SCREAMING_SNAKE_CASE ) )
if is_substring_divisible(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 602 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
lowercase__ = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
lowercase__ = {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
raise Exception("requires fairseq >= 0.9.0")
logging.set_verbosity_info()
lowercase__ = logging.get_logger(__name__)
lowercase__ = " Hello world! cécé herlolip"
lowercase__ = [
("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'_float_tensor',
]
for k in ignore_keys:
state_dict.pop(UpperCAmelCase_ , UpperCAmelCase_ )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = dct.pop(UpperCAmelCase_ )
UpperCAmelCase : List[str] = val
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Dict = torch.load(UpperCAmelCase_ , map_location='cpu' )
UpperCAmelCase : List[str] = torch.hub.load('pytorch/fairseq' , 'bart.large.cnn' ).eval()
hub_interface.model.load_state_dict(sd['model'] )
return hub_interface
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = emb.weight.shape
UpperCAmelCase : Optional[int] = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ , bias=UpperCAmelCase_ )
UpperCAmelCase : Tuple = emb.weight.data
return lin_layer
@torch.no_grad()
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None ):
if not os.path.exists(UpperCAmelCase_ ):
UpperCAmelCase : str = torch.hub.load('pytorch/fairseq' , UpperCAmelCase_ ).eval()
else:
UpperCAmelCase : Union[str, Any] = load_xsum_checkpoint(UpperCAmelCase_ )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
UpperCAmelCase : Tuple = checkpoint_path.replace('.' , '-' )
UpperCAmelCase : Tuple = BartConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Any = bart.encode(UpperCAmelCase_ ).unsqueeze(0 )
UpperCAmelCase : Tuple = BartTokenizer.from_pretrained(UpperCAmelCase_ ).encode(UpperCAmelCase_ , return_tensors='pt' ).unsqueeze(0 )
if not torch.eq(UpperCAmelCase_ , UpperCAmelCase_ ).all():
raise ValueError(
F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}""" )
if checkpoint_path == "bart.large.mnli":
UpperCAmelCase : str = bart.state_dict()
remove_ignore_keys_(UpperCAmelCase_ )
UpperCAmelCase : Any = state_dict['model.decoder.embed_tokens.weight']
for src, dest in mnli_rename_keys:
rename_key(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
UpperCAmelCase : int = BartForSequenceClassification(UpperCAmelCase_ ).eval()
model.load_state_dict(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = bart.predict('mnli' , UpperCAmelCase_ , return_logits=UpperCAmelCase_ )
UpperCAmelCase : Tuple = model(UpperCAmelCase_ )[0] # logits
else: # no classification heads to worry about
UpperCAmelCase : Tuple = bart.model.state_dict()
remove_ignore_keys_(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = state_dict['decoder.embed_tokens.weight']
UpperCAmelCase : str = bart.extract_features(UpperCAmelCase_ )
if hf_checkpoint_name == "facebook/bart-large":
UpperCAmelCase : List[str] = BartModel(UpperCAmelCase_ ).eval()
model.load_state_dict(UpperCAmelCase_ )
UpperCAmelCase : str = model(UpperCAmelCase_ ).model[0]
else:
UpperCAmelCase : Dict = BartForConditionalGeneration(UpperCAmelCase_ ).eval() # an existing summarization ckpt
model.model.load_state_dict(UpperCAmelCase_ )
if hasattr(UpperCAmelCase_ , 'lm_head' ):
UpperCAmelCase : List[str] = make_linear_from_emb(model.model.shared )
UpperCAmelCase : Any = model.model(UpperCAmelCase_ )[0]
# Check results
if fairseq_output.shape != new_model_outputs.shape:
raise ValueError(
F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}""" )
if (fairseq_output != new_model_outputs).any().item():
raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
Path(UpperCAmelCase_ ).mkdir(exist_ok=UpperCAmelCase_ )
model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
)
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
)
lowercase__ = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 695 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
lowercase__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
lowercase__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = """whisper"""
UpperCAmelCase_ : Tuple = ["""past_key_values"""]
UpperCAmelCase_ : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : str , lowercase_ : Any=51_865 , lowercase_ : List[Any]=80 , lowercase_ : int=6 , lowercase_ : Dict=4 , lowercase_ : List[Any]=6 , lowercase_ : Any=4 , lowercase_ : Tuple=1_536 , lowercase_ : Tuple=1_536 , lowercase_ : Tuple=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : List[Any]=50_257 , lowercase_ : Optional[int]=True , lowercase_ : Any=True , lowercase_ : str="gelu" , lowercase_ : List[str]=256 , lowercase_ : str=0.0 , lowercase_ : Any=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Dict=0.02 , lowercase_ : Optional[int]=False , lowercase_ : Union[str, Any]=1_500 , lowercase_ : List[Any]=448 , lowercase_ : int=50_256 , lowercase_ : Union[str, Any]=50_256 , lowercase_ : List[Any]=50_256 , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=[220, 50_256] , lowercase_ : Tuple=False , lowercase_ : str=256 , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=0.05 , lowercase_ : Any=10 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.0 , lowercase_ : Optional[int]=10 , lowercase_ : int=0 , lowercase_ : Optional[int]=7 , **lowercase_ : Union[str, Any] , ) -> List[str]:
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Any = num_mel_bins
UpperCAmelCase : List[Any] = d_model
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : str = encoder_attention_heads
UpperCAmelCase : Tuple = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : Tuple = decoder_ffn_dim
UpperCAmelCase : List[str] = encoder_ffn_dim
UpperCAmelCase : int = dropout
UpperCAmelCase : int = attention_dropout
UpperCAmelCase : List[Any] = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : Union[str, Any] = init_std
UpperCAmelCase : Dict = encoder_layerdrop
UpperCAmelCase : str = decoder_layerdrop
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : int = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Tuple = max_source_positions
UpperCAmelCase : List[Any] = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : Optional[int] = classifier_proj_size
UpperCAmelCase : List[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : Optional[Any] = mask_time_prob
UpperCAmelCase : Optional[Any] = mask_time_length
UpperCAmelCase : str = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Tuple = mask_feature_length
UpperCAmelCase : Optional[int] = mask_feature_min_masks
UpperCAmelCase : str = median_filter_width
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , suppress_tokens=lowercase_ , begin_suppress_tokens=lowercase_ , **lowercase_ , )
class A_ ( _snake_case ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : Optional[int] = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
UpperCAmelCase : int = {0: 'batch'}
else:
UpperCAmelCase : List[str] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='inputs' )
return common_inputs
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional["TensorType"] = None , lowercase_ : int = 22_050 , lowercase_ : float = 5.0 , lowercase_ : int = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Tuple = OrderedDict()
UpperCAmelCase : Tuple = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowercase_ , framework=lowercase_ , sampling_rate=lowercase_ , time_duration=lowercase_ , frequency=lowercase_ , )
UpperCAmelCase : Optional[Any] = encoder_inputs['input_features'].shape[2]
UpperCAmelCase : Tuple = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Optional[int] = super().generate_dummy_inputs(
preprocessor.tokenizer , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase : Dict = encoder_inputs.pop('input_features' )
UpperCAmelCase : List[str] = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def UpperCAmelCase_ ( self : Dict ) -> float:
return 1E-3
| 695 | 1 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
a = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
a = {'''facebook/blenderbot_small-90M''': 512}
def _snake_case ( _snake_case : List[str] ) -> Union[str, Any]:
'''simple docstring'''
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
_A = set(_snake_case )
return pairs
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = VOCAB_FILES_NAMES
UpperCAmelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : List[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Tuple="__start__" , _UpperCAmelCase : int="__end__" , _UpperCAmelCase : Optional[Any]="__unk__" , _UpperCAmelCase : List[str]="__null__" , **_UpperCAmelCase : str , ):
super().__init__(unk_token=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , **_UpperCAmelCase )
with open(_UpperCAmelCase , encoding='utf-8' ) as vocab_handle:
_A = json.load(_UpperCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
with open(_UpperCAmelCase , encoding='utf-8' ) as merges_handle:
_A = merges_handle.read().split('\n' )[1:-1]
_A = [tuple(merge.split() ) for merge in merges]
_A = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
_A = {}
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
return len(self.encoder )
def lowerCAmelCase_ ( self : Union[str, Any] ):
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : str ):
if token in self.cache:
return self.cache[token]
_A = re.sub('([.,!?()])' , r' \1' , _UpperCAmelCase )
_A = re.sub('(\')' , r' \1 ' , _UpperCAmelCase )
_A = re.sub(r'\s{2,}' , ' ' , _UpperCAmelCase )
if "\n" in token:
_A = token.replace('\n' , ' __newln__' )
_A = token.split(' ' )
_A = []
for token in tokens:
if not len(_UpperCAmelCase ):
continue
_A = token.lower()
_A = tuple(_UpperCAmelCase )
_A = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
_A = get_pairs(_UpperCAmelCase )
if not pairs:
words.append(_UpperCAmelCase )
continue
while True:
_A = min(_UpperCAmelCase , key=lambda _UpperCAmelCase : self.bpe_ranks.get(_UpperCAmelCase , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(_UpperCAmelCase ):
try:
_A = word.index(_UpperCAmelCase , _UpperCAmelCase )
new_word.extend(word[i:j] )
_A = j
except ValueError:
new_word.extend(word[i:] )
break
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
_A = tuple(_UpperCAmelCase )
_A = new_word
if len(_UpperCAmelCase ) == 1:
break
else:
_A = get_pairs(_UpperCAmelCase )
_A = '@@ '.join(_UpperCAmelCase )
_A = word[:-4]
_A = word
words.append(_UpperCAmelCase )
return " ".join(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : str ):
_A = []
_A = re.findall(r'\S+\n?' , _UpperCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_UpperCAmelCase ).split(' ' ) ) )
return split_tokens
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : str ):
_A = token.lower()
return self.encoder.get(_UpperCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : int ):
return self.decoder.get(_UpperCAmelCase , self.unk_token )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : List[str] ):
_A = ' '.join(_UpperCAmelCase ).replace('@@ ' , '' ).strip()
return out_string
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_A = 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' )
_A = 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!' )
_A = token_index
writer.write(' '.join(_UpperCAmelCase ) + '\n' )
index += 1
return vocab_file, merge_file
| 7 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
return 1 / (1 + np.exp(-z ))
def __UpperCAmelCase ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict ) -> List[str]:
return (-y * np.log(_UpperCAmelCase ) - (1 - y) * np.log(1 - h )).mean()
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] ) -> Optional[Any]:
__snake_case = np.dot(_UpperCAmelCase , _UpperCAmelCase )
return np.sum(y * scores - np.log(1 + np.exp(_UpperCAmelCase ) ) )
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str]=7_00_00 ) -> Union[str, Any]:
__snake_case = np.zeros(x.shape[1] )
for iterations in range(_UpperCAmelCase ):
__snake_case = np.dot(_UpperCAmelCase , _UpperCAmelCase )
__snake_case = sigmoid_function(_UpperCAmelCase )
__snake_case = np.dot(x.T , h - y ) / y.size
__snake_case = theta - alpha * gradient # updating the weights
__snake_case = np.dot(_UpperCAmelCase , _UpperCAmelCase )
__snake_case = sigmoid_function(_UpperCAmelCase )
__snake_case = cost_function(_UpperCAmelCase , _UpperCAmelCase )
if iterations % 1_00 == 0:
print(F'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
a : int = datasets.load_iris()
a : int = iris.data[:, :2]
a : Optional[Any] = (iris.target != 0) * 1
a : Tuple = 0.1
a : List[str] = logistic_reg(alpha, x, y, max_iterations=70_000)
print('''theta: ''', theta) # printing the theta i.e our weights vector
def __UpperCAmelCase ( _UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
return sigmoid_function(
np.dot(_UpperCAmelCase , _UpperCAmelCase ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
((a) , (a)) : Any = (x[:, 0].min(), x[:, 0].max())
((a) , (a)) : Any = (x[:, 1].min(), x[:, 1].max())
((a) , (a)) : Any = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
a : Optional[Any] = np.c_[xxa.ravel(), xxa.ravel()]
a : List[Any] = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')
plt.legend()
plt.show()
| 69 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
_SCREAMING_SNAKE_CASE : Optional[int] = ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
_SCREAMING_SNAKE_CASE : Union[str, Any] = {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
raise Exception('''requires fairseq >= 0.9.0''')
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = ''' Hello world! cécé herlolip'''
_SCREAMING_SNAKE_CASE : Tuple = [
('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def lowerCamelCase__ ( _lowerCamelCase : Tuple ) -> int:
lowerCamelCase_ = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'_float_tensor',
]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def lowerCamelCase__ ( _lowerCamelCase : Any , _lowerCamelCase : List[Any] , _lowerCamelCase : int ) -> Tuple:
lowerCamelCase_ = dct.pop(_lowerCamelCase )
lowerCamelCase_ = val
def lowerCamelCase__ ( _lowerCamelCase : Optional[Any] ) -> Optional[Any]:
lowerCamelCase_ = torch.load(_lowerCamelCase , map_location='cpu' )
lowerCamelCase_ = torch.hub.load('pytorch/fairseq' , 'bart.large.cnn' ).eval()
hub_interface.model.load_state_dict(sd['model'] )
return hub_interface
def lowerCamelCase__ ( _lowerCamelCase : Tuple ) -> Optional[int]:
lowerCamelCase_ , lowerCamelCase_ = emb.weight.shape
lowerCamelCase_ = nn.Linear(_lowerCamelCase , _lowerCamelCase , bias=_lowerCamelCase )
lowerCamelCase_ = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCamelCase__ ( _lowerCamelCase : Any , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int=None ) -> Union[str, Any]:
if not os.path.exists(_lowerCamelCase ):
lowerCamelCase_ = torch.hub.load('pytorch/fairseq' , _lowerCamelCase ).eval()
else:
lowerCamelCase_ = load_xsum_checkpoint(_lowerCamelCase )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
lowerCamelCase_ = checkpoint_path.replace('.' , '-' )
lowerCamelCase_ = BartConfig.from_pretrained(_lowerCamelCase )
lowerCamelCase_ = bart.encode(_lowerCamelCase ).unsqueeze(0 )
lowerCamelCase_ = BartTokenizer.from_pretrained(_lowerCamelCase ).encode(_lowerCamelCase , return_tensors='pt' ).unsqueeze(0 )
if not torch.eq(_lowerCamelCase , _lowerCamelCase ).all():
raise ValueError(
F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )
if checkpoint_path == "bart.large.mnli":
lowerCamelCase_ = bart.state_dict()
remove_ignore_keys_(_lowerCamelCase )
lowerCamelCase_ = state_dict['model.decoder.embed_tokens.weight']
for src, dest in mnli_rename_keys:
rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowerCamelCase_ = BartForSequenceClassification(_lowerCamelCase ).eval()
model.load_state_dict(_lowerCamelCase )
lowerCamelCase_ = bart.predict('mnli' , _lowerCamelCase , return_logits=_lowerCamelCase )
lowerCamelCase_ = model(_lowerCamelCase )[0] # logits
else: # no classification heads to worry about
lowerCamelCase_ = bart.model.state_dict()
remove_ignore_keys_(_lowerCamelCase )
lowerCamelCase_ = state_dict['decoder.embed_tokens.weight']
lowerCamelCase_ = bart.extract_features(_lowerCamelCase )
if hf_checkpoint_name == "facebook/bart-large":
lowerCamelCase_ = BartModel(_lowerCamelCase ).eval()
model.load_state_dict(_lowerCamelCase )
lowerCamelCase_ = model(_lowerCamelCase ).model[0]
else:
lowerCamelCase_ = BartForConditionalGeneration(_lowerCamelCase ).eval() # an existing summarization ckpt
model.model.load_state_dict(_lowerCamelCase )
if hasattr(_lowerCamelCase , 'lm_head' ):
lowerCamelCase_ = make_linear_from_emb(model.model.shared )
lowerCamelCase_ = model.model(_lowerCamelCase )[0]
# Check results
if fairseq_output.shape != new_model_outputs.shape:
raise ValueError(
F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
if (fairseq_output != new_model_outputs).any().item():
raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
model.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
)
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
)
_SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 137 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : int ) -> int:
while a != 0:
lowerCamelCase_ , lowerCamelCase_ = b % a, a
return b
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : int ) -> int:
if gcd(_lowerCamelCase , _lowerCamelCase ) != 1:
lowerCamelCase_ = F'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(_lowerCamelCase )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1, 0, a
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 0, 1, m
while va != 0:
lowerCamelCase_ = ua // va
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 137 | 1 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
return int((input_a, input_a).count(0 ) == 0 )
def lowercase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 29 |
def UpperCAmelCase__ ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] ):
__a : Any = ''
for i in table:
res += inp[i - 1]
return res
def UpperCAmelCase__ ( lowerCamelCase_ : Optional[Any] ):
return data[1:] + data[0]
def UpperCAmelCase__ ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Optional[int] ):
__a : Optional[int] = ''
for i in range(len(lowerCamelCase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def UpperCAmelCase__ ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : str ):
__a : List[str] = int('0b' + data[0] + data[-1] , 2 )
__a : List[str] = int('0b' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def UpperCAmelCase__ ( lowerCamelCase_ : Any , lowerCamelCase_ : List[str] , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
__a : List[Any] = message[:4]
__a : str = message[4:]
__a : Any = apply_table(lowerCamelCase_ , lowerCamelCase_ )
__a : int = xor(lowerCamelCase_ , lowerCamelCase_ )
__a : Dict = apply_sbox(lowerCamelCase_ , temp[:4] ) # noqa: E741
__a : Tuple = apply_sbox(lowerCamelCase_ , temp[4:] )
__a : List[Any] = '0' * (2 - len(lowerCamelCase_ )) + l # noqa: E741
__a : List[str] = '0' * (2 - len(lowerCamelCase_ )) + r
__a : List[Any] = apply_table(l + r , lowerCamelCase_ )
__a : Dict = xor(lowerCamelCase_ , lowerCamelCase_ )
return temp + right
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = input('''Enter 10 bit key: ''')
SCREAMING_SNAKE_CASE__ = input('''Enter 8 bit message: ''')
SCREAMING_SNAKE_CASE__ = [6, 3, 7, 4, 8, 5, 10, 9]
SCREAMING_SNAKE_CASE__ = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
SCREAMING_SNAKE_CASE__ = [2, 4, 3, 1]
SCREAMING_SNAKE_CASE__ = [2, 6, 3, 1, 4, 8, 5, 7]
SCREAMING_SNAKE_CASE__ = [4, 1, 3, 5, 7, 2, 8, 6]
SCREAMING_SNAKE_CASE__ = [4, 1, 2, 3, 2, 3, 4, 1]
SCREAMING_SNAKE_CASE__ = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
SCREAMING_SNAKE_CASE__ = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
SCREAMING_SNAKE_CASE__ = apply_table(key, paa_table)
SCREAMING_SNAKE_CASE__ = temp[:5]
SCREAMING_SNAKE_CASE__ = temp[5:]
SCREAMING_SNAKE_CASE__ = left_shift(left)
SCREAMING_SNAKE_CASE__ = left_shift(right)
SCREAMING_SNAKE_CASE__ = apply_table(left + right, pa_table)
SCREAMING_SNAKE_CASE__ = left_shift(left)
SCREAMING_SNAKE_CASE__ = left_shift(right)
SCREAMING_SNAKE_CASE__ = left_shift(left)
SCREAMING_SNAKE_CASE__ = left_shift(right)
SCREAMING_SNAKE_CASE__ = apply_table(left + right, pa_table)
# encryption
SCREAMING_SNAKE_CASE__ = apply_table(message, IP)
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = temp[4:] + temp[:4]
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
SCREAMING_SNAKE_CASE__ = apply_table(CT, IP)
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = temp[4:] + temp[:4]
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ : Union[str, Any] = {
"configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
"tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : int = [
"TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"AdaptiveEmbedding",
"TransfoXLForSequenceClassification",
"TransfoXLLMHeadModel",
"TransfoXLModel",
"TransfoXLPreTrainedModel",
"load_tf_weights_in_transfo_xl",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Any = [
"TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAdaptiveEmbedding",
"TFTransfoXLForSequenceClassification",
"TFTransfoXLLMHeadModel",
"TFTransfoXLMainLayer",
"TFTransfoXLModel",
"TFTransfoXLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
snake_case_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 169 |
from __future__ import annotations
def A (__A : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
UpperCAmelCase_ = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCAmelCase_ = array[0]
UpperCAmelCase_ = False
UpperCAmelCase_ = 1
UpperCAmelCase_ = []
while not is_found and i < array_length:
if array[i] < pivot:
UpperCAmelCase_ = True
UpperCAmelCase_ = [element for element in array[i:] if element >= array[i]]
UpperCAmelCase_ = longest_subsequence(__A )
if len(__A ) > len(__A ):
UpperCAmelCase_ = temp_array
else:
i += 1
UpperCAmelCase_ = [element for element in array[1:] if element >= pivot]
UpperCAmelCase_ = [pivot, *longest_subsequence(__A )]
if len(__A ) > len(__A ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 169 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( _lowerCamelCase , unittest.TestCase ):
snake_case_ = CLIPTokenizer
snake_case_ = CLIPTokenizerFast
snake_case_ = True
snake_case_ = {}
snake_case_ = False
def A_ ( self : List[Any] ):
super().setUp()
# fmt: off
snake_case_ = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
snake_case_ = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
snake_case_ = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
snake_case_ = {'''unk_token''': '''<unk>'''}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ = 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(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def A_ ( self : Tuple , **lowercase_ : Tuple ):
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def A_ ( self : List[str] , **lowercase_ : Dict ):
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def A_ ( self : Optional[int] , lowercase_ : Optional[int] ):
snake_case_ = '''lower newer'''
snake_case_ = '''lower newer'''
return input_text, output_text
def A_ ( self : Optional[int] ):
snake_case_ = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ = '''lower newer'''
snake_case_ = ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
snake_case_ = tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ = tokens + [tokenizer.unk_token]
snake_case_ = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
@require_ftfy
def A_ ( self : Optional[Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ = '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
snake_case_ = tokenizer_s.tokenize(lowercase_ )
snake_case_ = tokenizer_r.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
snake_case_ = '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
snake_case_ = tokenizer_s.tokenize(lowercase_ )
snake_case_ = tokenizer_r.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Test that the tokenization is identical on unicode of space type
snake_case_ = [
'''\u0009''', # (horizontal tab, '\t')
'''\u000B''', # (vertical tab)
'''\u000C''', # (form feed)
'''\u0020''', # (space, ' ')
'''\u200E''', # (left-to-right mark):w
'''\u200F''', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
snake_case_ = tokenizer_s.tokenize(lowercase_ )
snake_case_ = tokenizer_r.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Test that the tokenization is identical on unicode of line break type
snake_case_ = [
'''\u000A''', # (line feed, '\n')
'''\r\n''', # (carriage return and line feed, '\r\n')
'''\u000D''', # (carriage return, '\r')
'''\r''', # (carriage return, '\r')
'''\u000D''', # (carriage return, '\r')
'''\u2028''', # (line separator)
'''\u2029''', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
snake_case_ = tokenizer_s.tokenize(lowercase_ )
snake_case_ = tokenizer_r.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def A_ ( self : List[str] ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ = '''hello''' # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ = F"{text_of_1_token} {text_of_1_token}"
snake_case_ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , )
snake_case_ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ) + 1, len(lowercase_ ) + 1 + len(lowercase_ )) , )
snake_case_ = F" {text}"
snake_case_ = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , )
snake_case_ = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ) + 1, 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
def A_ ( self : Optional[Any] ):
# Test related to the breaking change introduced in transformers v4.17.0
# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
with self.assertRaises(lowercase_ ) as context:
self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer''' )
self.assertTrue(
context.exception.args[0].startswith(
'''The `backend_tokenizer` provided does not match the expected format.''' ) )
@require_ftfy
def A_ ( self : List[str] ):
super().test_tokenization_python_rust_equals()
def A_ ( self : List[Any] ):
# CLIP always lower cases letters
pass
| 640 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(__UpperCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
a : int = change_brightness(img, 100)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 640 | 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
__A = logging.get_logger(__name__)
__A = {
'''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 _snake_case ( a__ ):
snake_case__ = "deberta-v2"
def __init__( self : Dict , UpperCAmelCase : Dict=128100 , UpperCAmelCase : List[str]=1536 , UpperCAmelCase : str=24 , UpperCAmelCase : Tuple=24 , UpperCAmelCase : str=6144 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : str=512 , UpperCAmelCase : Dict=0 , UpperCAmelCase : Dict=0.0_2 , UpperCAmelCase : str=1E-7 , UpperCAmelCase : Dict=False , UpperCAmelCase : List[Any]=-1 , UpperCAmelCase : List[str]=0 , UpperCAmelCase : List[Any]=True , UpperCAmelCase : str=None , UpperCAmelCase : Union[str, Any]=0 , UpperCAmelCase : List[Any]="gelu" , **UpperCAmelCase : Optional[Any] , ):
super().__init__(**UpperCAmelCase )
__lowerCamelCase : Dict = hidden_size
__lowerCamelCase : Optional[Any] = num_hidden_layers
__lowerCamelCase : List[str] = num_attention_heads
__lowerCamelCase : List[Any] = intermediate_size
__lowerCamelCase : Tuple = hidden_act
__lowerCamelCase : List[Any] = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : List[str] = max_position_embeddings
__lowerCamelCase : List[str] = type_vocab_size
__lowerCamelCase : List[str] = initializer_range
__lowerCamelCase : int = relative_attention
__lowerCamelCase : Tuple = max_relative_positions
__lowerCamelCase : List[str] = pad_token_id
__lowerCamelCase : str = position_biased_input
# Backwards compatibility
if type(UpperCAmelCase ) == str:
__lowerCamelCase : Dict = [x.strip() for x in pos_att_type.lower().split("|" )]
__lowerCamelCase : List[Any] = pos_att_type
__lowerCamelCase : str = vocab_size
__lowerCamelCase : List[Any] = layer_norm_eps
__lowerCamelCase : Tuple = kwargs.get("pooler_hidden_size" , UpperCAmelCase )
__lowerCamelCase : str = pooler_dropout
__lowerCamelCase : int = pooler_hidden_act
class _snake_case ( a__ ):
@property
def lowerCamelCase__ ( self : Optional[Any] ):
if self.task == "multiple-choice":
__lowerCamelCase : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
__lowerCamelCase : Any = {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 lowerCamelCase__ ( self : List[str] ):
return 12
def lowerCamelCase__ ( self : str , 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 , ):
__lowerCamelCase : str = 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 | 366 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _snake_case ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Any=3 , UpperCAmelCase : str=18 , UpperCAmelCase : str=30 , UpperCAmelCase : Any=400 , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Optional[Any]=None , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Dict=None , UpperCAmelCase : List[str]=True , UpperCAmelCase : int=[0.5, 0.5, 0.5] , UpperCAmelCase : Tuple=[0.5, 0.5, 0.5] , ):
__lowerCamelCase : Any = size if size is not None else {"shortest_edge": 18}
__lowerCamelCase : Dict = crop_size if crop_size is not None else {"height": 18, "width": 18}
__lowerCamelCase : List[str] = parent
__lowerCamelCase : int = batch_size
__lowerCamelCase : Any = num_channels
__lowerCamelCase : Tuple = image_size
__lowerCamelCase : int = min_resolution
__lowerCamelCase : List[Any] = max_resolution
__lowerCamelCase : List[str] = do_resize
__lowerCamelCase : str = size
__lowerCamelCase : Tuple = do_center_crop
__lowerCamelCase : Optional[int] = crop_size
__lowerCamelCase : Optional[Any] = do_normalize
__lowerCamelCase : Optional[Any] = image_mean
__lowerCamelCase : List[Any] = image_std
def lowerCamelCase__ ( self : int ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _snake_case ( a__ , unittest.TestCase ):
snake_case__ = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self : Any ):
__lowerCamelCase : Optional[int] = LevitImageProcessingTester(self )
@property
def lowerCamelCase__ ( self : Union[str, Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self : List[str] ):
__lowerCamelCase : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(UpperCAmelCase , "image_std" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCAmelCase , "size" ) )
def lowerCamelCase__ ( self : int ):
__lowerCamelCase : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
__lowerCamelCase : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def lowerCamelCase__ ( self : Dict ):
pass
def lowerCamelCase__ ( self : Any ):
# Initialize image_processing
__lowerCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , Image.Image )
# Test not batched input
__lowerCamelCase : Dict = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
__lowerCamelCase : int = image_processing(UpperCAmelCase , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase__ ( self : Any ):
# Initialize image_processing
__lowerCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCamelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase , numpify=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , np.ndarray )
# Test not batched input
__lowerCamelCase : Optional[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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
__lowerCamelCase : Dict = image_processing(UpperCAmelCase , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase__ ( self : Union[str, Any] ):
# Initialize image_processing
__lowerCamelCase : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCamelCase : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase , torchify=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , torch.Tensor )
# Test not batched input
__lowerCamelCase : 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
__lowerCamelCase : List[str] = image_processing(UpperCAmelCase , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , ) | 366 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def a (self : Optional[Any] ):
"""simple docstring"""
__snake_case = 10
def a (self : Any ):
"""simple docstring"""
__snake_case = [1, 2, 3, 4]
__snake_case = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__UpperCAmelCase , self.block_size , 0 ) , __UpperCAmelCase )
def a (self : Tuple ):
"""simple docstring"""
__snake_case = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
__snake_case = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCAmelCase , self.block_size , 0 ) , __UpperCAmelCase )
def a (self : int ):
"""simple docstring"""
__snake_case = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
__snake_case = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCAmelCase , self.block_size , 0 ) , __UpperCAmelCase )
def a (self : str ):
"""simple docstring"""
__snake_case = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
__snake_case , __snake_case = process_story(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , [] )
def a (self : Optional[Any] ):
"""simple docstring"""
__snake_case = ''''''
__snake_case , __snake_case = process_story(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , [] )
self.assertEqual(__UpperCAmelCase , [] )
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
__snake_case , __snake_case = process_story(__UpperCAmelCase )
__snake_case = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
__snake_case = ['''It was the best of times.''']
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def a (self : Optional[int] ):
"""simple docstring"""
__snake_case = torch.tensor([1, 2, 3, 4] )
__snake_case = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__UpperCAmelCase , 0 ).numpy() , expected.numpy() )
def a (self : Optional[int] ):
"""simple docstring"""
__snake_case = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
__snake_case = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCAmelCase , 23 ).numpy() , expected.numpy() )
def a (self : List[str] ):
"""simple docstring"""
__snake_case = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
__snake_case = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCAmelCase , 1 ).numpy() , expected.numpy() )
def a (self : List[str] ):
"""simple docstring"""
__snake_case = 101
__snake_case = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
__snake_case = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
__snake_case = compute_token_type_ids(__UpperCAmelCase , __UpperCAmelCase )
np.testing.assert_array_equal(__UpperCAmelCase , __UpperCAmelCase )
| 592 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
A_ : int = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
A_ : int = dataset.iloc[:, 1:2].values
A_ : Optional[Any] = dataset.iloc[:, 2].values
A_ , A_ , A_ , A_ : str = train_test_split(X, y, test_size=0.2, random_state=0)
A_ : Optional[Any] = PolynomialFeatures(degree=4)
A_ : Tuple = poly_reg.fit_transform(X)
A_ : str = LinearRegression()
pol_reg.fit(X_poly, y)
def A ( ):
'''simple docstring'''
plt.scatter(snake_case__ , snake_case__ , color="""red""" )
plt.plot(snake_case__ , pol_reg.predict(poly_reg.fit_transform(snake_case__ ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 196 | 0 |
from __future__ import annotations
import numpy as np
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_ : List[str] = np.shape(_lowercase )
if rows != columns:
UpperCAmelCase_ : Dict = (
'''\'table\' has to be of square shaped array but got a '''
f'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(_lowercase )
UpperCAmelCase_ : str = np.zeros((rows, columns) )
UpperCAmelCase_ : int = np.zeros((rows, columns) )
for i in range(_lowercase ):
for j in range(_lowercase ):
UpperCAmelCase_ : Any = sum(lower[i][k] * upper[k][j] for k in range(_lowercase ) )
if upper[j][j] == 0:
raise ArithmeticError('''No LU decomposition exists''' )
UpperCAmelCase_ : List[str] = (table[i][j] - total) / upper[j][j]
UpperCAmelCase_ : str = 1
for j in range(_lowercase , _lowercase ):
UpperCAmelCase_ : Optional[int] = sum(lower[i][k] * upper[k][j] for k in range(_lowercase ) )
UpperCAmelCase_ : Tuple = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod() | 300 |
import os
import string
import sys
__a = 1 << 8
__a = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY_FLAG,
'mod_int': 91,
'undefined': sys.maxsize,
'interrupt': 3,
'insert': 50,
'delete': 51,
'pg_up': 53,
'pg_down': 54,
}
__a = KEYMAP['up']
__a = KEYMAP['left']
if sys.platform == "win32":
__a = []
__a = {
B'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
B'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
B'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
B'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
B'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
B'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
B'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
B'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
}
for i in range(10):
__a = ord(str(i))
def lowerCamelCase__ ( ):
'''simple docstring'''
if os.name == "nt":
import msvcrt
UpperCAmelCase_ : Union[str, Any] = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_lowercase ) == 0:
# Read the keystroke
UpperCAmelCase_ : str = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
UpperCAmelCase_ : Union[str, Any] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
UpperCAmelCase_ : List[Any] = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(_lowercase )
if ord(_lowercase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
UpperCAmelCase_ : Tuple = chr(KEYMAP['''esc'''] )
except KeyError:
UpperCAmelCase_ : Dict = cha[1]
else:
UpperCAmelCase_ : int = ch.decode(_lowercase )
else:
UpperCAmelCase_ : Optional[Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
UpperCAmelCase_ : str = sys.stdin.fileno()
UpperCAmelCase_ : Optional[Any] = termios.tcgetattr(_lowercase )
try:
tty.setraw(_lowercase )
UpperCAmelCase_ : Dict = sys.stdin.read(1 )
finally:
termios.tcsetattr(_lowercase , termios.TCSADRAIN , _lowercase )
return ch
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = get_raw_chars()
if ord(_lowercase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_lowercase ) == KEYMAP["esc"]:
UpperCAmelCase_ : Union[str, Any] = get_raw_chars()
if ord(_lowercase ) == KEYMAP["mod_int"]:
UpperCAmelCase_ : Optional[int] = get_raw_chars()
if ord(_lowercase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_lowercase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_lowercase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"] | 300 | 1 |
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