code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : Optional[int] = '''dpr'''
def __init__( self , lowerCAmelCase__=3_0_5_2_2 , lowerCAmelCase__=7_6_8 , lowerCAmelCase__=1_2 , lowerCAmelCase__=1_2 , lowerCAmelCase__=3_0_7_2 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=5_1_2 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=0 , lowerCAmelCase__="absolute" , lowerCAmelCase__ = 0 , **lowerCAmelCase__ , ):
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = projection_dim
__SCREAMING_SNAKE_CASE = position_embedding_type
| 100 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : Dict = '''informer'''
__lowercase : Union[str, Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "student_t" , lowerCAmelCase__ = "nll" , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = "mean" , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 6_4 , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = True , lowerCAmelCase__ = "gelu" , lowerCAmelCase__ = 0.05 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 1_0_0 , lowerCAmelCase__ = 0.02 , lowerCAmelCase__=True , lowerCAmelCase__ = "prob" , lowerCAmelCase__ = 5 , lowerCAmelCase__ = True , **lowerCAmelCase__ , ):
# time series specific configuration
__SCREAMING_SNAKE_CASE = prediction_length
__SCREAMING_SNAKE_CASE = context_length or prediction_length
__SCREAMING_SNAKE_CASE = distribution_output
__SCREAMING_SNAKE_CASE = loss
__SCREAMING_SNAKE_CASE = input_size
__SCREAMING_SNAKE_CASE = num_time_features
__SCREAMING_SNAKE_CASE = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
__SCREAMING_SNAKE_CASE = scaling
__SCREAMING_SNAKE_CASE = num_dynamic_real_features
__SCREAMING_SNAKE_CASE = num_static_real_features
__SCREAMING_SNAKE_CASE = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""")
__SCREAMING_SNAKE_CASE = cardinality
else:
__SCREAMING_SNAKE_CASE = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""")
__SCREAMING_SNAKE_CASE = embedding_dimension
else:
__SCREAMING_SNAKE_CASE = [min(5_0 , (cat + 1) // 2) for cat in self.cardinality]
__SCREAMING_SNAKE_CASE = num_parallel_samples
# Transformer architecture configuration
__SCREAMING_SNAKE_CASE = input_size * len(self.lags_sequence) + self._number_of_features
__SCREAMING_SNAKE_CASE = d_model
__SCREAMING_SNAKE_CASE = encoder_attention_heads
__SCREAMING_SNAKE_CASE = decoder_attention_heads
__SCREAMING_SNAKE_CASE = encoder_ffn_dim
__SCREAMING_SNAKE_CASE = decoder_ffn_dim
__SCREAMING_SNAKE_CASE = encoder_layers
__SCREAMING_SNAKE_CASE = decoder_layers
__SCREAMING_SNAKE_CASE = dropout
__SCREAMING_SNAKE_CASE = attention_dropout
__SCREAMING_SNAKE_CASE = activation_dropout
__SCREAMING_SNAKE_CASE = encoder_layerdrop
__SCREAMING_SNAKE_CASE = decoder_layerdrop
__SCREAMING_SNAKE_CASE = activation_function
__SCREAMING_SNAKE_CASE = init_std
__SCREAMING_SNAKE_CASE = use_cache
# Informer
__SCREAMING_SNAKE_CASE = attention_type
__SCREAMING_SNAKE_CASE = sampling_factor
__SCREAMING_SNAKE_CASE = distil
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__)
@property
def snake_case_ ( self):
return (
sum(self.embedding_dimension)
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 100 | 1 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : int = argparse.ArgumentParser()
parser.add_argument('''--model_ckpt''', type=__lowerCamelCase, default='''microsoft/unixcoder-base-nine''' )
parser.add_argument('''--num_epochs''', type=__lowerCamelCase, default=5 )
parser.add_argument('''--batch_size''', type=__lowerCamelCase, default=6 )
parser.add_argument('''--gradient_accumulation_steps''', type=__lowerCamelCase, default=1 )
parser.add_argument('''--freeze''', type=__lowerCamelCase, default=__lowerCamelCase )
parser.add_argument('''--learning_rate''', type=__lowerCamelCase, default=5E-4 )
parser.add_argument('''--seed''', type=__lowerCamelCase, default=0 )
parser.add_argument('''--lr_scheduler_type''', type=__lowerCamelCase, default='''cosine''' )
parser.add_argument('''--num_warmup_steps''', type=__lowerCamelCase, default=10 )
parser.add_argument('''--weight_decay''', type=__lowerCamelCase, default=0.01 )
parser.add_argument('''--output_dir''', type=__lowerCamelCase, default='''./results''' )
return parser.parse_args()
lowerCAmelCase__ = load('''accuracy''')
def snake_case_ ( A_ : Optional[Any] ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : Tuple = eval_pred
_lowerCamelCase : Any = np.argmax(__lowerCamelCase, axis=1 )
return metric.compute(predictions=__lowerCamelCase, references=__lowerCamelCase )
class __snake_case ( A_):
def __init__( self : Optional[int] , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : Any = trainer
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[str] , **__lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if control.should_evaluate:
_lowerCamelCase : str = deepcopy(_lowerCamelCase )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix='''train''' )
return control_copy
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = get_args()
set_seed(args.seed )
_lowerCamelCase : Union[str, Any] = load_dataset('''codeparrot/codecomplex''', split='''train''' )
_lowerCamelCase : Any = dataset.train_test_split(test_size=0.2 )
_lowerCamelCase : Dict = train_test['''test'''].train_test_split(test_size=0.5 )
_lowerCamelCase : str = DatasetDict(
{
'''train''': train_test['''train'''],
'''test''': test_validation['''train'''],
'''valid''': test_validation['''test'''],
} )
print('''Loading tokenizer and model''' )
_lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(args.model_ckpt )
_lowerCamelCase : List[Any] = tokenizer.eos_token
_lowerCamelCase : List[Any] = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt, num_labels=7 )
_lowerCamelCase : List[Any] = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Optional[Any] = ClassLabel(num_classes=7, names=list(set(train_test_validation['''train''']['''complexity'''] ) ) )
def tokenize(A_ : List[str] ):
_lowerCamelCase : Union[str, Any] = tokenizer(example['''src'''], truncation=__lowerCamelCase, max_length=10_24 )
_lowerCamelCase : Union[str, Any] = labels.straint(example['''complexity'''] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
_lowerCamelCase : Optional[int] = train_test_validation.map(
__lowerCamelCase, batched=__lowerCamelCase, remove_columns=train_test_validation['''train'''].column_names, )
_lowerCamelCase : Dict = DataCollatorWithPadding(tokenizer=__lowerCamelCase )
_lowerCamelCase : Any = TrainingArguments(
output_dir=args.output_dir, learning_rate=args.learning_rate, lr_scheduler_type=args.lr_scheduler_type, evaluation_strategy='''epoch''', save_strategy='''epoch''', logging_strategy='''epoch''', per_device_train_batch_size=args.batch_size, per_device_eval_batch_size=args.batch_size, num_train_epochs=args.num_epochs, gradient_accumulation_steps=args.gradient_accumulation_steps, weight_decay=0.01, metric_for_best_model='''accuracy''', run_name='''complexity-java''', report_to='''wandb''', )
_lowerCamelCase : Dict = Trainer(
model=__lowerCamelCase, args=__lowerCamelCase, train_dataset=tokenized_datasets['''train'''], eval_dataset=tokenized_datasets['''valid'''], tokenizer=__lowerCamelCase, data_collator=__lowerCamelCase, compute_metrics=__lowerCamelCase, )
print('''Training...''' )
trainer.add_callback(CustomCallback(__lowerCamelCase ) )
trainer.train()
if __name__ == "__main__":
main()
| 368 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowerCAmelCase__ = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def snake_case_ ( A_ : int, A_ : Dict ):
'''simple docstring'''
_lowerCamelCase : List[Any] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
_lowerCamelCase : Union[str, Any] = int(re.match(R'''.*layer_(\d*).*''', A_ )[1] )
layer_number -= 3
return F'''h.{layer_number}.''' + key
def snake_case_ ( A_ : List[Any] ):
'''simple docstring'''
if dtype == torch.bool:
return 1 / 8
_lowerCamelCase : List[str] = re.search(R'''[^\d](\d+)$''', str(A_ ) )
if bit_search is None:
raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.''' )
_lowerCamelCase : Optional[Any] = int(bit_search.groups()[0] )
return bit_size // 8
def snake_case_ ( A_ : str, A_ : Any, A_ : int, A_ : List[str], A_ : Any ):
'''simple docstring'''
if bloom_config_file == "":
_lowerCamelCase : Dict = BloomConfig()
else:
_lowerCamelCase : Any = BloomConfig.from_json_file(A_ )
if shard_model:
_lowerCamelCase : Optional[int] = os.listdir(A_ )
_lowerCamelCase : List[str] = sorted(filter(lambda A_ : s.startswith('''layer''' ) and "model_00" in s, A_ ) )
_lowerCamelCase : str = {'''weight_map''': {}, '''metadata''': {}}
_lowerCamelCase : List[str] = 0
_lowerCamelCase : str = None
_lowerCamelCase : str = BloomConfig()
for j, file in enumerate(A_ ):
print('''Processing file: {}'''.format(A_ ) )
_lowerCamelCase : List[Any] = None
for i in range(A_ ):
# load all TP files
_lowerCamelCase : Any = file.replace('''model_00''', F'''model_0{i}''' )
_lowerCamelCase : Any = torch.load(os.path.join(A_, A_ ), map_location='''cpu''' )
# Rename keys in the transformers names
_lowerCamelCase : Optional[Any] = list(temp.keys() )
for key in keys:
_lowerCamelCase : List[Any] = temp.pop(A_ )
if tensors is None:
_lowerCamelCase : Any = temp
else:
for key in tensors.keys():
if any(key.endswith(A_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_lowerCamelCase : List[Any] = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_lowerCamelCase : Optional[Any] = torch.cat([tensors[key], temp[key]], dim=A_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(A_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_lowerCamelCase : Optional[Any] = tensors[key] / pretraining_tp
torch.save(
A_, os.path.join(
A_, '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ), str(len(A_ ) ).zfill(5 ) ), ), )
for key in tensors.keys():
_lowerCamelCase : str = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
_lowerCamelCase : Tuple = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ), str(len(A_ ) ).zfill(5 ) )
_lowerCamelCase : List[Any] = BloomConfig()
_lowerCamelCase : Tuple = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
_lowerCamelCase : Union[str, Any] = total_size
with open(A_, '''w''', encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(A_, WEIGHTS_NAME + '''.index.json''' ), '''w''', encoding='''utf-8''' ) as f:
_lowerCamelCase : Any = json.dumps(A_, indent=2, sort_keys=A_ ) + '''\n'''
f.write(A_ )
else:
_lowerCamelCase : Tuple = BloomModel(A_ )
_lowerCamelCase : Optional[int] = os.listdir(A_ )
_lowerCamelCase : Union[str, Any] = sorted(filter(lambda A_ : s.startswith('''layer''' ) and "model_00" in s, A_ ) )
_lowerCamelCase : int = None
for i, file in enumerate(A_ ):
_lowerCamelCase : Optional[int] = None
for i in range(A_ ):
# load all TP files
_lowerCamelCase : str = file.replace('''model_00''', F'''model_0{i}''' )
_lowerCamelCase : List[Any] = torch.load(os.path.join(A_, A_ ), map_location='''cpu''' )
# Rename keys in the transformers names
_lowerCamelCase : List[Any] = list(temp.keys() )
for key in keys:
_lowerCamelCase : Dict = temp.pop(A_ )
if tensors is None:
_lowerCamelCase : int = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(A_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_lowerCamelCase : Dict = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_lowerCamelCase : Optional[Any] = torch.cat([tensors[key], temp[key]], dim=A_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(A_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_lowerCamelCase : List[Any] = tensors[key] / pretraining_tp
_lowerCamelCase : List[str] = model.load_state_dict(A_, strict=A_ )
assert not other_keys.unexpected_keys, F'''The keys {other_keys.unexpected_keys} are unexpected'''
if missing_keys is None:
_lowerCamelCase : Optional[Any] = set(other_keys.missing_keys )
else:
_lowerCamelCase : Union[str, Any] = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F'''The keys {missing_keys} are missing'''
# Save pytorch-model
os.makedirs(A_, exist_ok=A_ )
_lowerCamelCase : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
_lowerCamelCase : Tuple = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}''' )
if config.torch_dtype is not None:
_lowerCamelCase : Dict = model.to(config.torch_dtype )
torch.save(model.state_dict(), A_ )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(A_, '''w''', encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM 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(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowerCAmelCase__ = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 175 | 0 |
__snake_case = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
assert type(__lowerCAmelCase ) in (int, float) and decimal == int(__lowerCAmelCase )
UpperCAmelCase : List[Any] =int(__lowerCAmelCase )
UpperCAmelCase : Dict =""
UpperCAmelCase : Any =False
if decimal < 0:
UpperCAmelCase : Optional[Any] =True
decimal *= -1
while decimal > 0:
UpperCAmelCase : List[Any] =divmod(__lowerCAmelCase , 16 )
UpperCAmelCase : List[Any] =values[remainder] + hexadecimal
UpperCAmelCase : Optional[int] ="0x" + hexadecimal
if negative:
UpperCAmelCase : Optional[int] ="-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = start
SCREAMING_SNAKE_CASE_ : List[str] = end
SCREAMING_SNAKE_CASE_ : Tuple = val
SCREAMING_SNAKE_CASE_ : List[str] = (start + end) // 2
SCREAMING_SNAKE_CASE_ : Optional[int] = left
SCREAMING_SNAKE_CASE_ : str = right
def __repr__( self : Tuple ):
"""simple docstring"""
return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'
class a__ :
def __init__( self : Any,_A : Sequence,_A : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = collection
SCREAMING_SNAKE_CASE_ : Optional[int] = function
if self.collection:
SCREAMING_SNAKE_CASE_ : List[str] = self._build_tree(0,len(_A ) - 1 )
def __UpperCamelCase ( self : int,_A : Any,_A : List[Any] ):
"""simple docstring"""
self._update_tree(self.root,_A,_A )
def __UpperCamelCase ( self : str,_A : Any,_A : List[Any] ):
"""simple docstring"""
return self._query_range(self.root,_A,_A )
def __UpperCamelCase ( self : Any,_A : Optional[int],_A : int ):
"""simple docstring"""
if start == end:
return SegmentTreeNode(_A,_A,self.collection[start] )
SCREAMING_SNAKE_CASE_ : List[Any] = (start + end) // 2
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._build_tree(_A,_A )
SCREAMING_SNAKE_CASE_ : Optional[int] = self._build_tree(mid + 1,_A )
return SegmentTreeNode(_A,_A,self.fn(left.val,right.val ),_A,_A )
def __UpperCamelCase ( self : int,_A : int,_A : Tuple,_A : Dict ):
"""simple docstring"""
if node.start == i and node.end == i:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = val
return
if i <= node.mid:
self._update_tree(node.left,_A,_A )
else:
self._update_tree(node.right,_A,_A )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.fn(node.left.val,node.right.val )
def __UpperCamelCase ( self : str,_A : List[str],_A : Optional[int],_A : Optional[Any] ):
"""simple docstring"""
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left,_A,_A )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left,_A,node.mid ),self._query_range(node.right,node.mid + 1,_A ),)
else:
# range in right child tree
return self._query_range(node.right,_A,_A )
def __UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
if self.root is not None:
SCREAMING_SNAKE_CASE_ : int = Queue()
queue.put(self.root )
while not queue.empty():
SCREAMING_SNAKE_CASE_ : Tuple = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 50)
__lowerCamelCase : int = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 18 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : Dict = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
"""
class _UpperCamelCase ( _A ):
'''simple docstring'''
@add_start_docstrings(snake_case_ )
def __call__( self : Union[str, Any] , snake_case_ : torch.LongTensor , snake_case_ : torch.FloatTensor , **snake_case_ : Union[str, Any] ):
raise NotImplementedError("""StoppingCriteria needs to be subclassed""" )
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : Optional[int] , snake_case_ : int , snake_case_ : Optional[int] = None ):
UpperCamelCase_: Optional[Any] = max_length
UpperCamelCase_: Any = max_position_embeddings
@add_start_docstrings(snake_case_ )
def __call__( self : int , snake_case_ : torch.LongTensor , snake_case_ : torch.FloatTensor , **snake_case_ : List[str] ):
UpperCamelCase_: Optional[int] = input_ids.shape[-1]
UpperCamelCase_: Any = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
"""This is a friendly reminder - the current text generation call will exceed the model's predefined """
f'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
"""exceptions, performance degradation, or nothing at all.""" )
return is_done
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : Optional[int] , snake_case_ : int , snake_case_ : int ):
warnings.warn(
"""The class `MaxNewTokensCriteria` is deprecated. """
f'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
"""with `max_length = start_length + max_new_tokens` instead.""" , snake_case_ , )
UpperCamelCase_: str = start_length
UpperCamelCase_: Optional[Any] = max_new_tokens
UpperCamelCase_: Optional[Any] = start_length + max_new_tokens
@add_start_docstrings(snake_case_ )
def __call__( self : List[Any] , snake_case_ : torch.LongTensor , snake_case_ : torch.FloatTensor , **snake_case_ : Union[str, Any] ):
return input_ids.shape[-1] >= self.max_length
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : Any , snake_case_ : float , snake_case_ : Optional[float] = None ):
UpperCamelCase_: Optional[Any] = max_time
UpperCamelCase_: Dict = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(snake_case_ )
def __call__( self : Optional[Any] , snake_case_ : torch.LongTensor , snake_case_ : torch.FloatTensor , **snake_case_ : Tuple ):
return time.time() - self.initial_timestamp > self.max_time
class _UpperCamelCase ( _A ):
'''simple docstring'''
@add_start_docstrings(snake_case_ )
def __call__( self : List[str] , snake_case_ : torch.LongTensor , snake_case_ : torch.FloatTensor , **snake_case_ : List[str] ):
return any(criteria(snake_case_ , snake_case_ ) for criteria in self )
@property
def lowerCAmelCase__ ( self : Any ):
for stopping_criterium in self:
if isinstance(snake_case_ , snake_case_ ):
return stopping_criterium.max_length
elif isinstance(snake_case_ , snake_case_ ):
return stopping_criterium.max_length
return None
def A__ ( lowerCamelCase , lowerCamelCase ) -> StoppingCriteriaList:
UpperCamelCase_: Tuple = stopping_criteria.max_length
UpperCamelCase_: Optional[int] = deepcopy(lowerCamelCase )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter""" , lowerCamelCase )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCamelCase ) )
return new_stopping_criteria
| 364 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(lowerCamelCase ) * abs(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 223 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Union[str, Any] ={
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : int =[
"NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
"NezhaForNextSentencePrediction",
"NezhaForMaskedLM",
"NezhaForPreTraining",
"NezhaForMultipleChoice",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 170 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = """realm"""
def __init__( self :str , lowerCamelCase :List[Any]=3_0522 , lowerCamelCase :Optional[int]=768 , lowerCamelCase :Any=128 , lowerCamelCase :Tuple=12 , lowerCamelCase :str=12 , lowerCamelCase :List[str]=8 , lowerCamelCase :List[str]=3072 , lowerCamelCase :List[str]="gelu_new" , lowerCamelCase :int=0.1 , lowerCamelCase :Optional[Any]=0.1 , lowerCamelCase :int=512 , lowerCamelCase :Union[str, Any]=2 , lowerCamelCase :str=0.02 , lowerCamelCase :Tuple=1e-12 , lowerCamelCase :Dict=256 , lowerCamelCase :int=10 , lowerCamelCase :List[str]=1e-3 , lowerCamelCase :str=5 , lowerCamelCase :Optional[int]=320 , lowerCamelCase :Union[str, Any]=1335_3718 , lowerCamelCase :str=5000 , lowerCamelCase :str=1 , lowerCamelCase :List[Any]=0 , lowerCamelCase :Tuple=2 , **lowerCamelCase :Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
# Common config
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = retriever_proj_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = num_candidates
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = layer_norm_eps
# Reader config
UpperCAmelCase__ = span_hidden_size
UpperCAmelCase__ = max_span_width
UpperCAmelCase__ = reader_layer_norm_eps
UpperCAmelCase__ = reader_beam_size
UpperCAmelCase__ = reader_seq_len
# Retrieval config
UpperCAmelCase__ = num_block_records
UpperCAmelCase__ = searcher_beam_size
| 169 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
a_ = PhobertTokenizer
a_ = False
def lowercase ( self : Any ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowerCAmelCase = ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
__lowerCAmelCase = dict(zip(lowerCAmelCase_ , range(len(lowerCAmelCase_ ) ) ) )
__lowerCAmelCase = ['#version: 0.2', 'l à</w>']
__lowerCAmelCase = {'unk_token': '<unk>'}
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(f"""{token} {vocab_tokens[token]}\n""" )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowerCAmelCase_ ) )
def lowercase ( self : str , **lowerCAmelCase_ : List[str] ) -> Dict:
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase_ )
def lowercase ( self : Dict , lowerCAmelCase_ : List[Any] ) -> Tuple:
__lowerCAmelCase = 'Tôi là VinAI Research'
__lowerCAmelCase = 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
return input_text, output_text
def lowercase ( self : int ) -> Union[str, Any]:
__lowerCAmelCase = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__lowerCAmelCase = 'Tôi là VinAI Research'
__lowerCAmelCase = 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
__lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase_ )
print(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
__lowerCAmelCase = tokens + [tokenizer.unk_token]
__lowerCAmelCase = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , lowerCAmelCase_ )
| 363 |
from collections.abc import Sequence
def a_ ( lowerCAmelCase_ : Sequence[float], lowerCAmelCase_ : bool = False ):
if not arr:
return 0
__lowerCAmelCase = 0 if allow_empty_subarrays else float('-inf' )
__lowerCAmelCase = 0.0
for num in arr:
__lowerCAmelCase = max(0 if allow_empty_subarrays else num, curr_sum + num )
__lowerCAmelCase = max(lowerCAmelCase_, lowerCAmelCase_ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
_snake_case : Optional[Any] = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"""{max_subarray_sum(nums) = }""")
| 207 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Any = ProphetNetTokenizer
UpperCamelCase : Any = False
def __A ( self ) -> str:
'''simple docstring'''
super().setUp()
lowerCamelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def __A ( self , A ) -> Tuple:
'''simple docstring'''
lowerCamelCase = """UNwant\u00E9d,running"""
lowerCamelCase = """unwanted, running"""
return input_text, output_text
def __A ( self ) -> int:
'''simple docstring'''
lowerCamelCase = self.tokenizer_class(self.vocab_file )
lowerCamelCase = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , [9, 6, 7, 12, 10, 11] )
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def __A ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def __A ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A , strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A , strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def __A ( self ) -> int:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A , strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A , strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = BasicTokenizer(do_lower_case=A , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def __A ( self ) -> int:
'''simple docstring'''
lowerCamelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
lowerCamelCase = {}
for i, token in enumerate(A ):
lowerCamelCase = i
lowerCamelCase = WordpieceTokenizer(vocab=A , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
@require_torch
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
lowerCamelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowerCamelCase = [10_37, 21_46, 2_04_23, 20_05, 76_80, 78_49, 39_89, 10_12, 1_02]
lowerCamelCase = tokenizer(A , padding=A , return_tensors="""pt""" )
self.assertIsInstance(A , A )
lowerCamelCase = list(batch.input_ids.numpy()[0] )
self.assertListEqual(A , A )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def __A ( self ) -> int:
'''simple docstring'''
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def __A ( self ) -> List[str]:
'''simple docstring'''
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
@slow
def __A ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
lowerCamelCase = tokenizer.encode("""sequence builders""" , add_special_tokens=A )
lowerCamelCase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A )
lowerCamelCase = tokenizer.build_inputs_with_special_tokens(A )
lowerCamelCase = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == text + [1_02]
assert encoded_pair == text + [1_02] + text_a + [1_02]
| 252 |
def __lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ : List[str]=0 ):
'''simple docstring'''
return sorted(lowerCamelCase__ , key=lambda lowerCamelCase__ : x[column] )
def __lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCase__ : str , lowerCamelCase__ : List[str]=float("""inf""" ) ):
'''simple docstring'''
for i in range(points_counts - 1 ):
for j in range(i + 1 , lowerCamelCase__ ):
lowerCamelCase = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCamelCase = current_dis
return min_dis
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Optional[Any]=float("""inf""" ) ):
'''simple docstring'''
for i in range(min(6 , points_counts - 1 ) , lowerCamelCase__ ):
for j in range(max(0 , i - 6 ) , lowerCamelCase__ ):
lowerCamelCase = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCamelCase = current_dis
return min_dis
def __lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : List[str] ):
'''simple docstring'''
if points_counts <= 3:
return dis_between_closest_pair(lowerCamelCase__ , lowerCamelCase__ )
# recursion
lowerCamelCase = points_counts // 2
lowerCamelCase = closest_pair_of_points_sqr(
lowerCamelCase__ , points_sorted_on_y[:mid] , lowerCamelCase__ )
lowerCamelCase = closest_pair_of_points_sqr(
lowerCamelCase__ , points_sorted_on_y[mid:] , points_counts - mid )
lowerCamelCase = min(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(lowerCamelCase__ )
lowerCamelCase = dis_between_closest_in_strip(
lowerCamelCase__ , len(lowerCamelCase__ ) , lowerCamelCase__ )
return min(lowerCamelCase__ , lowerCamelCase__ )
def __lowerCamelCase ( lowerCamelCase__ : Optional[int] , lowerCamelCase__ : str ):
'''simple docstring'''
lowerCamelCase = column_based_sort(lowerCamelCase__ , column=0 )
lowerCamelCase = column_based_sort(lowerCamelCase__ , column=1 )
return (
closest_pair_of_points_sqr(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
) ** 0.5
if __name__ == "__main__":
UpperCAmelCase : Dict = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("Distance:", closest_pair_of_points(points, len(points)))
| 252 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_lowerCamelCase : str = u
for i in range(1 , _lowerCamelCase ):
_lowerCamelCase : List[Any] = temp * (u - i)
return temp
def lowerCamelCase_( ) -> None:
'''simple docstring'''
_lowerCamelCase : List[Any] = int(input("enter the numbers of values: " ) )
_lowerCamelCase : list[list[float]] = []
for _ in range(_lowerCamelCase ):
y.append([] )
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
y[i].append(_lowerCamelCase )
_lowerCamelCase : List[str] = 0
print("enter the values of parameters in a list: " )
_lowerCamelCase : Union[str, Any] = list(map(_lowerCamelCase , input().split() ) )
print("enter the values of corresponding parameters: " )
for i in range(_lowerCamelCase ):
_lowerCamelCase : Union[str, Any] = float(input() )
_lowerCamelCase : Optional[Any] = int(input("enter the value to interpolate: " ) )
_lowerCamelCase : Tuple = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , _lowerCamelCase ):
for j in range(n - i ):
_lowerCamelCase : Any = y[j + 1][i - 1] - y[j][i - 1]
_lowerCamelCase : Optional[int] = y[0][0]
for i in range(1 , _lowerCamelCase ):
summ += (ucal(_lowerCamelCase , _lowerCamelCase ) * y[0][i]) / math.factorial(_lowerCamelCase )
print(F"""the value at {value} is {summ}""" )
if __name__ == "__main__":
main() | 356 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowerCAmelCase__ = 'char'
lowerCAmelCase__ = 'bpe'
lowerCAmelCase__ = 'wp'
_lowerCAmelCase : List[str] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class A_ ( _a ):
lowerCAmelCase__ = ['image_processor', 'char_tokenizer']
lowerCAmelCase__ = 'ViTImageProcessor'
lowerCAmelCase__ = 'MgpstrTokenizer'
def __init__( self: List[Any] ,__lowerCAmelCase: int=None ,__lowerCAmelCase: Optional[int]=None ,**__lowerCAmelCase: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." ,__lowerCAmelCase ,)
_lowerCamelCase : List[Any] = kwargs.pop("feature_extractor" )
_lowerCamelCase : str = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
_lowerCamelCase : List[str] = tokenizer
_lowerCamelCase : str = AutoTokenizer.from_pretrained("gpt2" )
_lowerCamelCase : List[str] = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(__lowerCAmelCase ,__lowerCAmelCase )
def __call__( self: Optional[int] ,__lowerCAmelCase: List[Any]=None ,__lowerCAmelCase: Union[str, Any]=None ,__lowerCAmelCase: Optional[Any]=None ,**__lowerCAmelCase: Tuple ):
'''simple docstring'''
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
_lowerCamelCase : Optional[int] = self.image_processor(__lowerCAmelCase ,return_tensors=__lowerCAmelCase ,**__lowerCAmelCase )
if text is not None:
_lowerCamelCase : int = self.char_tokenizer(__lowerCAmelCase ,return_tensors=__lowerCAmelCase ,**__lowerCAmelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
_lowerCamelCase : Tuple = encodings["input_ids"]
return inputs
def _lowercase ( self: int ,__lowerCAmelCase: Dict ):
'''simple docstring'''
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Optional[int] = sequences
_lowerCamelCase : Dict = char_preds.size(0 )
_lowerCamelCase, _lowerCamelCase : Optional[Any] = self._decode_helper(__lowerCAmelCase ,"char" )
_lowerCamelCase, _lowerCamelCase : Union[str, Any] = self._decode_helper(__lowerCAmelCase ,"bpe" )
_lowerCamelCase, _lowerCamelCase : Tuple = self._decode_helper(__lowerCAmelCase ,"wp" )
_lowerCamelCase : List[str] = []
_lowerCamelCase : str = []
for i in range(__lowerCAmelCase ):
_lowerCamelCase : str = [char_scores[i], bpe_scores[i], wp_scores[i]]
_lowerCamelCase : List[Any] = [char_strs[i], bpe_strs[i], wp_strs[i]]
_lowerCamelCase : Optional[Any] = scores.index(max(__lowerCAmelCase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_lowerCamelCase : Tuple = {}
_lowerCamelCase : Tuple = final_strs
_lowerCamelCase : int = final_scores
_lowerCamelCase : str = char_strs
_lowerCamelCase : Dict = bpe_strs
_lowerCamelCase : int = wp_strs
return out
def _lowercase ( self: List[str] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: List[Any] ):
'''simple docstring'''
if format == DecodeType.CHARACTER:
_lowerCamelCase : int = self.char_decode
_lowerCamelCase : List[str] = 1
_lowerCamelCase : Optional[int] = "[s]"
elif format == DecodeType.BPE:
_lowerCamelCase : Dict = self.bpe_decode
_lowerCamelCase : str = 2
_lowerCamelCase : Union[str, Any] = "#"
elif format == DecodeType.WORDPIECE:
_lowerCamelCase : int = self.wp_decode
_lowerCamelCase : List[str] = 102
_lowerCamelCase : List[Any] = "[SEP]"
else:
raise ValueError(F"""Format {format} is not supported.""" )
_lowerCamelCase, _lowerCamelCase : Union[str, Any] = [], []
_lowerCamelCase : Any = pred_logits.size(0 )
_lowerCamelCase : int = pred_logits.size(1 )
_lowerCamelCase, _lowerCamelCase : List[Any] = pred_logits.topk(1 ,dim=-1 ,largest=__lowerCAmelCase ,sorted=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = preds_index.view(-1 ,__lowerCAmelCase )[:, 1:]
_lowerCamelCase : List[str] = decoder(__lowerCAmelCase )
_lowerCamelCase, _lowerCamelCase : str = torch.nn.functional.softmax(__lowerCAmelCase ,dim=2 ).max(dim=2 )
_lowerCamelCase : Any = preds_max_prob[:, 1:]
for index in range(__lowerCAmelCase ):
_lowerCamelCase : List[Any] = preds_str[index].find(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = preds_str[index][:pred_eos]
_lowerCamelCase : Optional[Any] = preds_index[index].cpu().tolist()
_lowerCamelCase : List[str] = pred_index.index(__lowerCAmelCase ) if eos_token in pred_index else -1
_lowerCamelCase : str = preds_max_prob[index][: pred_eos_index + 1]
_lowerCamelCase : Union[str, Any] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__lowerCAmelCase )
conf_scores.append(__lowerCAmelCase )
return dec_strs, conf_scores
def _lowercase ( self: Tuple ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : str = [seq.replace(" " ,"" ) for seq in self.char_tokenizer.batch_decode(__lowerCAmelCase )]
return decode_strs
def _lowercase ( self: List[str] ,__lowerCAmelCase: List[str] ):
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(__lowerCAmelCase )
def _lowercase ( self: Tuple ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = [seq.replace(" " ,"" ) for seq in self.wp_tokenizer.batch_decode(__lowerCAmelCase )]
return decode_strs | 340 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(__lowercase , 2 ) - pow(__lowercase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__lowercase , 2 ) - pow(__lowercase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__lowercase , 2 ) + pow(__lowercase , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = TransfoXLTokenizer
UpperCamelCase__ = False
UpperCamelCase__ = False
def UpperCamelCase ( self ):
"""simple docstring"""
super().setUp()
_UpperCAmelCase = [
'<unk>',
'[CLS]',
'[SEP]',
'want',
'unwanted',
'wa',
'un',
'running',
',',
'low',
'l',
]
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def UpperCamelCase ( self , **UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = '<unk> UNwanted , running'
_UpperCAmelCase = '<unk> unwanted, running'
return input_text, output_text
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=UpperCAmelCase )
_UpperCAmelCase = tokenizer.tokenize('<unk> UNwanted , running' )
self.assertListEqual(UpperCAmelCase , ['<unk>', 'unwanted', ',', 'running'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase ) , [0, 4, 8, 7] )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = TransfoXLTokenizer(lower_case=UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = TransfoXLTokenizer(lower_case=UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = TransfoXLTokenizer(lower_case=UpperCAmelCase )
_UpperCAmelCase = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
_UpperCAmelCase = [
'Hello',
'(',
'bracket',
')',
'and',
'side',
'@-@',
'scrolled',
'[',
'and',
']',
'Henry',
'\'s',
'$',
'5',
'@,@',
'000',
'with',
'3',
'@.@',
'34',
'm',
'.',
'What',
'\'s',
'up',
'!',
'?',
]
self.assertListEqual(tokenizer.tokenize(UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase ) , UpperCAmelCase )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = len(UpperCAmelCase )
tokenizer.add_tokens(['new1', 'new2'] )
tokenizer.move_added_token('new1' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(UpperCAmelCase ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('new1' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , 'new1' )
| 39 | 0 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _a ( _lowercase : Union[str, Any] , _lowercase : Any ):
'''simple docstring'''
__UpperCAmelCase : Dict = checkpoint
__UpperCAmelCase : Optional[Any] = {}
__UpperCAmelCase : str = vae_state_dict['''encoder.conv_in.weight''']
__UpperCAmelCase : str = vae_state_dict['''encoder.conv_in.bias''']
__UpperCAmelCase : Union[str, Any] = vae_state_dict['''encoder.conv_out.weight''']
__UpperCAmelCase : Optional[int] = vae_state_dict['''encoder.conv_out.bias''']
__UpperCAmelCase : List[str] = vae_state_dict['''encoder.norm_out.weight''']
__UpperCAmelCase : List[Any] = vae_state_dict['''encoder.norm_out.bias''']
__UpperCAmelCase : int = vae_state_dict['''decoder.conv_in.weight''']
__UpperCAmelCase : int = vae_state_dict['''decoder.conv_in.bias''']
__UpperCAmelCase : List[str] = vae_state_dict['''decoder.conv_out.weight''']
__UpperCAmelCase : Any = vae_state_dict['''decoder.conv_out.bias''']
__UpperCAmelCase : Any = vae_state_dict['''decoder.norm_out.weight''']
__UpperCAmelCase : List[str] = vae_state_dict['''decoder.norm_out.bias''']
__UpperCAmelCase : Optional[int] = vae_state_dict['''quant_conv.weight''']
__UpperCAmelCase : Union[str, Any] = vae_state_dict['''quant_conv.bias''']
__UpperCAmelCase : Dict = vae_state_dict['''post_quant_conv.weight''']
__UpperCAmelCase : str = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
__UpperCAmelCase : Optional[Any] = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
__UpperCAmelCase : Any = {
layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(_lowercase )
}
# Retrieves the keys for the decoder up blocks only
__UpperCAmelCase : Dict = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
__UpperCAmelCase : Optional[int] = {
layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(_lowercase )
}
for i in range(_lowercase ):
__UpperCAmelCase : Tuple = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]
if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
__UpperCAmelCase : Dict = vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.weight' )
__UpperCAmelCase : Optional[Any] = vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.bias' )
__UpperCAmelCase : Optional[int] = renew_vae_resnet_paths(_lowercase )
__UpperCAmelCase : List[Any] = {'''old''': F'down.{i}.block', '''new''': F'down_blocks.{i}.resnets'}
assign_to_checkpoint(_lowercase , _lowercase , _lowercase , additional_replacements=[meta_path] , config=_lowercase )
__UpperCAmelCase : int = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
__UpperCAmelCase : str = 2
for i in range(1 , num_mid_res_blocks + 1 ):
__UpperCAmelCase : int = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]
__UpperCAmelCase : List[str] = renew_vae_resnet_paths(_lowercase )
__UpperCAmelCase : int = {'''old''': F'mid.block_{i}', '''new''': F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(_lowercase , _lowercase , _lowercase , additional_replacements=[meta_path] , config=_lowercase )
__UpperCAmelCase : Tuple = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
__UpperCAmelCase : Dict = renew_vae_attention_paths(_lowercase )
__UpperCAmelCase : Optional[Any] = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(_lowercase , _lowercase , _lowercase , additional_replacements=[meta_path] , config=_lowercase )
conv_attn_to_linear(_lowercase )
for i in range(_lowercase ):
__UpperCAmelCase : List[str] = num_up_blocks - 1 - i
__UpperCAmelCase : Optional[int] = [
key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
]
if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
__UpperCAmelCase : Any = vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.weight'
]
__UpperCAmelCase : Any = vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.bias'
]
__UpperCAmelCase : str = renew_vae_resnet_paths(_lowercase )
__UpperCAmelCase : Dict = {'''old''': F'up.{block_id}.block', '''new''': F'up_blocks.{i}.resnets'}
assign_to_checkpoint(_lowercase , _lowercase , _lowercase , additional_replacements=[meta_path] , config=_lowercase )
__UpperCAmelCase : Tuple = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
__UpperCAmelCase : Union[str, Any] = 2
for i in range(1 , num_mid_res_blocks + 1 ):
__UpperCAmelCase : Union[str, Any] = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]
__UpperCAmelCase : Optional[int] = renew_vae_resnet_paths(_lowercase )
__UpperCAmelCase : Tuple = {'''old''': F'mid.block_{i}', '''new''': F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(_lowercase , _lowercase , _lowercase , additional_replacements=[meta_path] , config=_lowercase )
__UpperCAmelCase : Optional[int] = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
__UpperCAmelCase : Dict = renew_vae_attention_paths(_lowercase )
__UpperCAmelCase : Any = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(_lowercase , _lowercase , _lowercase , additional_replacements=[meta_path] , config=_lowercase )
conv_attn_to_linear(_lowercase )
return new_checkpoint
def _a ( _lowercase : str , _lowercase : str , ):
'''simple docstring'''
__UpperCAmelCase : str = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
__UpperCAmelCase : Optional[int] = io.BytesIO(r.content )
__UpperCAmelCase : int = OmegaConf.load(_lowercase )
__UpperCAmelCase : Optional[Any] = 512
__UpperCAmelCase : Tuple = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
__UpperCAmelCase : Union[str, Any] = {}
with safe_open(_lowercase , framework='''pt''' , device='''cpu''' ) as f:
for key in f.keys():
__UpperCAmelCase : Tuple = f.get_tensor(_lowercase )
else:
__UpperCAmelCase : List[Any] = torch.load(_lowercase , map_location=_lowercase )['''state_dict''']
# Convert the VAE model.
__UpperCAmelCase : int = create_vae_diffusers_config(_lowercase , image_size=_lowercase )
__UpperCAmelCase : str = custom_convert_ldm_vae_checkpoint(_lowercase , _lowercase )
__UpperCAmelCase : Tuple = AutoencoderKL(**_lowercase )
vae.load_state_dict(_lowercase )
vae.save_pretrained(_lowercase )
if __name__ == "__main__":
__UpperCAmelCase :Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
__UpperCAmelCase :Optional[Any] = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path) | 240 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase :Tuple = "Muhammad Umer Farooq"
__UpperCAmelCase :Tuple = "MIT"
__UpperCAmelCase :Union[str, Any] = "1.0.0"
__UpperCAmelCase :Optional[int] = "Muhammad Umer Farooq"
__UpperCAmelCase :Optional[Any] = "contact@muhammadumerfarooq.me"
__UpperCAmelCase :Any = "Alpha"
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class a ( _a ):
"""simple docstring"""
def __init__( self : Tuple , snake_case : str ) -> None:
super().__init__()
__UpperCAmelCase : list[str] = []
__UpperCAmelCase : Optional[int] = domain
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : str , snake_case : list[tuple[str, str | None]] ) -> None:
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__UpperCAmelCase : Optional[Any] = parse.urljoin(self.domain , snake_case )
self.urls.append(snake_case )
def _a ( _lowercase : str ):
'''simple docstring'''
return ".".join(get_sub_domain_name(_lowercase ).split('''.''' )[-2:] )
def _a ( _lowercase : str ):
'''simple docstring'''
return parse.urlparse(_lowercase ).netloc
def _a ( _lowercase : str = "https://github.com" ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = get_domain_name(_lowercase )
# Initialize the parser
__UpperCAmelCase : Dict = Parser(_lowercase )
try:
# Open URL
__UpperCAmelCase : Dict = requests.get(_lowercase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__UpperCAmelCase : str = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__UpperCAmelCase : Tuple = requests.get(_lowercase )
# Get the valid email.
__UpperCAmelCase : Dict = re.findall('''[a-zA-Z0-9]+@''' + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_lowercase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_lowercase )
if __name__ == "__main__":
__UpperCAmelCase :List[str] = emails_from_url("https://github.com")
print(f"""{len(emails)} emails found:""")
print("\n".join(sorted(emails))) | 240 | 1 |
"""simple docstring"""
from math import ceil, sqrt
def lowerCAmelCase__ ( UpperCamelCase__ = 1_0_0_0_0_0_0 ):
'''simple docstring'''
_a : Any = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_a : Optional[Any] = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_a : Optional[Any] = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 294 |
"""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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
_snake_case = torch.device('cpu')
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_a : Dict = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
return im
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02] )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Any = dct.pop(UpperCamelCase__ )
_a : Dict = val
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Tuple = []
for k in state_dict.keys():
_a : Any = k
if ".pwconv" in k:
_a : int = k_new.replace(""".pwconv""" , """.point_wise_conv""" )
if ".dwconv" in k:
_a : List[str] = k_new.replace(""".dwconv""" , """.depth_wise_conv""" )
if ".Proj." in k:
_a : Optional[int] = k_new.replace(""".Proj.""" , """.proj.""" )
if "patch_embed" in k_new:
_a : Tuple = k_new.replace("""patch_embed""" , """swiftformer.patch_embed.patch_embedding""" )
if "network" in k_new:
_a : int = k_new.split(""".""" )
if ls[2].isdigit():
_a : Union[str, Any] = """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:] )
else:
_a : Tuple = k_new.replace("""network""" , """swiftformer.encoder.network""" )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Tuple = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
_a : Optional[int] = 1_0_0_0
_a : Optional[Any] = """huggingface/label-files"""
_a : Optional[Any] = """imagenet-1k-id2label.json"""
_a : List[str] = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Optional[Any] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : Dict = idalabel
_a : Optional[int] = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
_a : Any = [3, 3, 6, 4]
_a : int = [4_8, 5_6, 1_1_2, 2_2_0]
elif swiftformer_name == "swiftformer_s":
_a : Any = [3, 3, 9, 6]
_a : List[str] = [4_8, 6_4, 1_6_8, 2_2_4]
elif swiftformer_name == "swiftformer_l1":
_a : List[Any] = [4, 3, 1_0, 5]
_a : Optional[int] = [4_8, 9_6, 1_9_2, 3_8_4]
elif swiftformer_name == "swiftformer_l3":
_a : List[Any] = [4, 4, 1_2, 6]
_a : Optional[Any] = [6_4, 1_2_8, 3_2_0, 5_1_2]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("""https""" ):
_a : Tuple = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location="""cpu""" , check_hash=UpperCamelCase__ )
else:
_a : Dict = torch.load(UpperCamelCase__ , map_location="""cpu""" )
_a : int = checkpoint
_a : Optional[Any] = create_rename_keys(UpperCamelCase__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# load HuggingFace model
_a : Any = SwiftFormerForImageClassification(UpperCamelCase__ ).eval()
hf_model.load_state_dict(UpperCamelCase__ )
# prepare test inputs
_a : Any = prepare_img()
_a : Union[str, Any] = ViTImageProcessor.from_pretrained("""preprocessor_config""" )
_a : Optional[int] = processor(images=UpperCamelCase__ , return_tensors="""pt""" )
# compare outputs from both models
_a : Dict = get_expected_output(UpperCamelCase__ )
_a : int = hf_model(inputs["""pixel_values"""] ).logits
assert hf_logits.shape == torch.Size([1, 1_0_0_0] )
assert torch.allclose(hf_logits[0, 0:5] , UpperCamelCase__ , atol=1e-3 )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swiftformer_name',
default='swiftformer_xs',
choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
type=str,
help='Name of the SwiftFormer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='./converted_outputs/',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')
_snake_case = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 294 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_lowerCamelCase):
_a = ['''torch''', '''scipy''']
def __init__( self: Dict , *_lowerCAmelCase: Any , **_lowerCAmelCase: int ):
requires_backends(self , ["torch", "scipy"] )
@classmethod
def SCREAMING_SNAKE_CASE ( cls: Any , *_lowerCAmelCase: str , **_lowerCAmelCase: Dict ):
requires_backends(cls , ["torch", "scipy"] )
@classmethod
def SCREAMING_SNAKE_CASE ( cls: Optional[int] , *_lowerCAmelCase: int , **_lowerCAmelCase: Optional[Any] ):
requires_backends(cls , ["torch", "scipy"] )
| 356 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
_UpperCAmelCase : Optional[Any] = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(42)
_UpperCAmelCase : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
_UpperCAmelCase : Any = "sshleifer/tiny-mbart"
@require_torch
class __lowerCAmelCase ( lowerCAmelCase):
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: int=False , _lowerCAmelCase: str=None , _lowerCAmelCase: Dict=True , _lowerCAmelCase: Dict=True , _lowerCAmelCase: Optional[int]=True , _lowerCAmelCase: Union[str, Any]=True , ):
lowercase :Any = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=_lowerCAmelCase , num_train_epochs=1 , distributed=_lowerCAmelCase , extra_args_str=_lowerCAmelCase , predict_with_generate=_lowerCAmelCase , do_train=_lowerCAmelCase , do_eval=_lowerCAmelCase , do_predict=_lowerCAmelCase , )
lowercase :List[Any] = TrainerState.load_from_json(os.path.join(_lowerCAmelCase , "trainer_state.json" ) ).log_history
if not do_eval:
return
lowercase :Union[str, Any] = [log for log in logs if "eval_loss" in log.keys()]
lowercase :Any = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
lowercase :Optional[Any] = eval_metrics[-1]
assert isinstance(last_step_stats["eval_bleu"] , _lowerCAmelCase )
assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def SCREAMING_SNAKE_CASE ( self: str ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase )
@require_torch_multi_gpu
def SCREAMING_SNAKE_CASE ( self: Tuple ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp simple" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp simple --fp16" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Dict ):
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp zero_dp_2" , predict_with_generate=_lowerCAmelCase )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
self.run_seqaseq_quick(
distributed=_lowerCAmelCase , extra_args_str="--sharded_ddp zero_dp_2 --fp16" , predict_with_generate=_lowerCAmelCase )
@require_apex
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--fp16 --fp16_backend=apex" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=_lowerCAmelCase , extra_args_str="--fp16 --fp16_backend=apex" )
@parameterized.expand(["base", "low", "high", "mixed"] )
@require_torch_multi_gpu
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _lowerCAmelCase: Any ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
lowercase :List[Any] = {
# test with the default log_level - should be info and thus log info once
"base": {"extra_args_str": "", "n_matches": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
}
lowercase :str = experiments[experiment_id]
lowercase :Dict = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
lowercase :List[str] = "Running training"
with CaptureStderr() as cl:
self.run_seqaseq_quick(**_lowerCAmelCase , extra_args_str=data["extra_args_str"] )
lowercase :Dict = len(re.findall(_lowerCAmelCase , cl.err ) )
self.assertEqual(_lowerCAmelCase , data["n_matches"] )
@slow
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Dict = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=_lowerCAmelCase , learning_rate=3e-4 , num_train_epochs=10 , distributed=_lowerCAmelCase , )
# Check metrics
lowercase :List[str] = TrainerState.load_from_json(os.path.join(_lowerCAmelCase , "trainer_state.json" ) ).log_history
lowercase :Dict = [log for log in logs if "eval_loss" in log.keys()]
lowercase :str = eval_metrics[0]
lowercase :Optional[int] = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["eval_bleu"] , _lowerCAmelCase )
# test if do_predict saves generations and metrics
lowercase :Optional[Any] = os.listdir(_lowerCAmelCase )
lowercase :List[str] = {os.path.basename(_lowerCAmelCase ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def SCREAMING_SNAKE_CASE ( self: Tuple ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(_lowerCAmelCase: str ) -> Tuple[int, float]:
lowercase :Tuple = "--skip_memory_metrics 0"
lowercase :List[str] = self.run_trainer(
max_len=1_28 , model_name=_lowerCAmelCase , learning_rate=3e-4 , num_train_epochs=1 , optim=_lowerCAmelCase , distributed=_lowerCAmelCase , extra_args_str=_lowerCAmelCase , do_eval=_lowerCAmelCase , do_predict=_lowerCAmelCase , n_gpus_to_use=1 , )
# Check metrics
lowercase :List[str] = TrainerState.load_from_json(Path(_lowerCAmelCase , "trainer_state.json" ) ).log_history
lowercase :Dict = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
lowercase :Any = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )
lowercase :List[str] = logs[0]["train_loss"]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
lowercase , lowercase , lowercase :Optional[Any] = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
lowercase , lowercase , lowercase :List[str] = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
lowercase :List[Any] = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
lowercase :List[str] = gpu_peak_mem_orig + gpu_alloc_mem_orig
lowercase :List[str] = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
lowercase :Tuple = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
lowercase :Union[str, Any] = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
_lowerCAmelCase , _lowerCAmelCase , "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
_lowerCAmelCase , _lowerCAmelCase , "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
_lowerCAmelCase , _lowerCAmelCase , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def SCREAMING_SNAKE_CASE ( self: Dict , _lowerCAmelCase: int , _lowerCAmelCase: str , _lowerCAmelCase: int , _lowerCAmelCase: float = 3e-3 , _lowerCAmelCase: str = "adafactor" , _lowerCAmelCase: bool = False , _lowerCAmelCase: str = None , _lowerCAmelCase: int = 0 , _lowerCAmelCase: bool = True , _lowerCAmelCase: bool = True , _lowerCAmelCase: bool = True , _lowerCAmelCase: bool = True , _lowerCAmelCase: int = None , ):
lowercase :Optional[int] = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
lowercase :Optional[Any] = self.get_auto_remove_tmp_dir()
lowercase :Tuple = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(_lowerCAmelCase )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(_lowerCAmelCase )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
lowercase :Union[str, Any] = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(_lowerCAmelCase )}\n ".split()
lowercase :str = "\n --do_predict\n ".split()
lowercase :Union[str, Any] = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
lowercase :Optional[int] = get_gpu_count()
lowercase :str = get_torch_dist_unique_port()
lowercase :Union[str, Any] = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
lowercase :Optional[int] = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
else:
lowercase :Tuple = ["run_translation.py"] + args
with patch.object(_lowerCAmelCase , "argv" , _lowerCAmelCase ):
main()
return output_dir
| 158 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
__magic_name__: int = "vivit"
def __init__( self : int , _A : List[str]=224 , _A : int=32 , _A : int=[2, 16, 16] , _A : List[Any]=3 , _A : List[Any]=768 , _A : int=12 , _A : Optional[int]=12 , _A : List[Any]=3072 , _A : Dict="gelu_fast" , _A : Optional[int]=0.0 , _A : List[str]=0.0 , _A : Dict=0.0_2 , _A : Optional[Any]=1E-06 , _A : Optional[int]=True , **_A : Dict , ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[int] = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : int = hidden_dropout_prob
snake_case_ : Union[str, Any] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : Optional[int] = image_size
snake_case_ : List[str] = num_frames
snake_case_ : Optional[Any] = tubelet_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : List[Any] = qkv_bias
super().__init__(**_A )
| 327 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def SCREAMING_SNAKE_CASE__ ( __a , **__a ):
snake_case_ : int = dataset.map(**__a )
@get_duration
def SCREAMING_SNAKE_CASE__ ( __a , **__a ):
snake_case_ : Dict = dataset.filter(**__a )
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ : Tuple = {'num examples': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : Dict = datasets.Features({'text': datasets.Value('string' ), 'numbers': datasets.Value('float32' )} )
snake_case_ : List[Any] = generate_example_dataset(
os.path.join(__a , 'dataset.arrow' ) , __a , num_examples=__a )
snake_case_ : str = transformers.AutoTokenizer.from_pretrained('bert-base-cased' , use_fast=__a )
def tokenize(__a ):
return tokenizer(examples['text'] )
snake_case_ : Any = map(__a )
snake_case_ : Tuple = map(__a , batched=__a )
snake_case_ : str = map(__a , function=lambda __a : None , batched=__a )
with dataset.formatted_as(type='numpy' ):
snake_case_ : Optional[int] = map(__a , function=lambda __a : None , batched=__a )
with dataset.formatted_as(type='pandas' ):
snake_case_ : str = map(__a , function=lambda __a : None , batched=__a )
with dataset.formatted_as(type='torch' , columns='numbers' ):
snake_case_ : int = map(__a , function=lambda __a : None , batched=__a )
with dataset.formatted_as(type='tensorflow' , columns='numbers' ):
snake_case_ : List[Any] = map(__a , function=lambda __a : None , batched=__a )
snake_case_ : int = map(__a , function=__a , batched=__a )
snake_case_ : Optional[Any] = filter(__a )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(__a , 'wb' ) as f:
f.write(json.dumps(__a ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 327 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE :int = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Any = ['''ConvNextFeatureExtractor''']
__SCREAMING_SNAKE_CASE :Any = ['''ConvNextImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
'''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvNextForImageClassification''',
'''ConvNextModel''',
'''ConvNextPreTrainedModel''',
'''ConvNextBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
'''TFConvNextForImageClassification''',
'''TFConvNextModel''',
'''TFConvNextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__SCREAMING_SNAKE_CASE :Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 358 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = {'''vocab_file''': '''spiece.model'''}
__SCREAMING_SNAKE_CASE :Any = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__SCREAMING_SNAKE_CASE :int = {
'''AI-Sweden/gpt-sw3-126m''': 2048,
'''AI-Sweden/gpt-sw3-350m''': 2048,
'''AI-Sweden/gpt-sw3-1.6b''': 2048,
'''AI-Sweden/gpt-sw3-6.7b''': 2048,
'''AI-Sweden/gpt-sw3-20b''': 2048,
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = VOCAB_FILES_NAMES
_lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , snake_case_ : Any , snake_case_ : Optional[Any]=False , snake_case_ : int=False , snake_case_ : Any=False , snake_case_ : Optional[Any]=None , snake_case_ : List[Any]=None , snake_case_ : Tuple=None , snake_case_ : Any=None , snake_case_ : Optional[Dict[str, Any]] = None , **snake_case_ : Any , ):
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
_UpperCAmelCase = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
_UpperCAmelCase = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
_UpperCAmelCase = "<|endoftext|>" if eos_token is None else eos_token
_UpperCAmelCase = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
_UpperCAmelCase = unk_token if pad_token is None else pad_token
_UpperCAmelCase = eos_token if bos_token is None else bos_token
else:
_UpperCAmelCase = "<pad>" if pad_token is None else pad_token
_UpperCAmelCase = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# Used for whitespace normalization in input texts
# fmt : off
_UpperCAmelCase = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
_UpperCAmelCase = re.compile(
f'[{"".join(map(snake_case_ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]' )
def __getstate__( self : Optional[Any] ):
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self : Any , snake_case_ : Union[str, Any] ):
_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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowercase ( self : Dict ):
return len(self.sp_model )
def lowercase ( self : Optional[Any] , snake_case_ : str ):
_UpperCAmelCase = self.non_printing_characters_re.sub("" , snake_case_ )
# Normalize whitespaces
_UpperCAmelCase = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
_UpperCAmelCase = unicodedata.normalize("NFC" , snake_case_ )
return text
def lowercase ( self : List[str] , snake_case_ : str , **snake_case_ : List[str] ):
_UpperCAmelCase = self.preprocess_text(snake_case_ )
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def lowercase ( self : str , snake_case_ : str ):
return self.sp_model.PieceToId(snake_case_ )
def lowercase ( self : int , snake_case_ : int ):
return self.sp_model.IdToPiece(snake_case_ )
@staticmethod
def lowercase ( snake_case_ : str ):
return out_string
def lowercase ( self : Any , snake_case_ : List[str] ):
_UpperCAmelCase = []
_UpperCAmelCase = ""
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case_ ) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(snake_case_ )
_UpperCAmelCase = False
out_string += self.sp_model.decode(snake_case_ )
return out_string
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase ( self : Optional[int] , snake_case_ : str , snake_case_ : Optional[str] = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = 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:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
def lowercase ( self : Any , snake_case_ : Union[str, List[str]] , snake_case_ : Union[str, bool] = False ):
if isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = self.preprocess_text(snake_case_ )
_UpperCAmelCase = self.sp_model.encode(snake_case_ )
else:
_UpperCAmelCase = [self.preprocess_text(snake_case_ ) for t in text]
_UpperCAmelCase = self.sp_model.encode(snake_case_ )
if return_tensors is True or return_tensors == "pt":
_UpperCAmelCase = torch.tensor(snake_case_ )
return token_ids
def lowercase ( self : Optional[Any] , snake_case_ : Union[int, List[int]] ):
return self.sp_model.decode(snake_case_ )
def lowercase ( self : List[str] , snake_case_ : "Conversation" ):
_UpperCAmelCase = [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
_UpperCAmelCase = (
f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(snake_case_ ) + f'{self.bos_token}Bot:'
)
return self.encode(text=snake_case_ )
| 156 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def lowercase__( __SCREAMING_SNAKE_CASE : Callable ):
@wraps(__SCREAMING_SNAKE_CASE )
def _inner_fn(*__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : List[Any] ):
warnings.warn(
(F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''') , __SCREAMING_SNAKE_CASE , )
return fn(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
return _inner_fn
| 213 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def lowercase__( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] ):
for attribute in key.split('.' ):
lowercase_ : Tuple = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if weight_type is not None:
lowercase_ : List[Any] = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).shape
else:
lowercase_ : str = 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":
lowercase_ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase_ : Optional[Any] = value
elif weight_type == "weight_v":
lowercase_ : Optional[Any] = value
elif weight_type == "bias":
lowercase_ : Union[str, Any] = value
else:
lowercase_ : Any = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowercase__( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
lowercase_ : Optional[int] = []
lowercase_ : Optional[int] = fairseq_model.state_dict()
lowercase_ : List[str] = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
lowercase_ : List[str] = False
if "conv_layers" in name:
load_conv_layer(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == 'group' , )
lowercase_ : str = True
else:
for key, mapped_key in MAPPING.items():
lowercase_ : int = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
lowercase_ : str = True
if "*" in mapped_key:
lowercase_ : int = name.split(__SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
lowercase_ : Optional[Any] = mapped_key.replace('*' , __SCREAMING_SNAKE_CASE )
if "weight_g" in name:
lowercase_ : Any = 'weight_g'
elif "weight_v" in name:
lowercase_ : Tuple = 'weight_v'
elif "weight" in name:
lowercase_ : int = 'weight'
elif "bias" in name:
lowercase_ : List[Any] = 'bias'
else:
lowercase_ : Optional[Any] = None
set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(__SCREAMING_SNAKE_CASE )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowercase__( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : str ):
lowercase_ : Dict = full_name.split('conv_layers.' )[-1]
lowercase_ : int = name.split('.' )
lowercase_ : Any = int(items[0] )
lowercase_ : Tuple = 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.'''
)
lowercase_ : List[str] = 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.'''
)
lowercase_ : List[Any] = 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."
)
lowercase_ : Optional[Any] = 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.'''
)
lowercase_ : List[str] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__SCREAMING_SNAKE_CASE )
def lowercase__( __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : int ):
lowercase_ : str = SEWConfig()
if is_finetuned:
lowercase_ : List[Any] = model.wav_encoder.wav_model.cfg
else:
lowercase_ : Tuple = model.cfg
lowercase_ : Any = fs_config.conv_bias
lowercase_ : Optional[Any] = eval(fs_config.conv_feature_layers )
lowercase_ : int = [x[0] for x in conv_layers]
lowercase_ : Any = [x[1] for x in conv_layers]
lowercase_ : Optional[Any] = [x[2] for x in conv_layers]
lowercase_ : Tuple = 'gelu'
lowercase_ : str = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
lowercase_ : int = 0.0
lowercase_ : Any = fs_config.activation_fn.name
lowercase_ : Tuple = fs_config.encoder_embed_dim
lowercase_ : int = 0.02
lowercase_ : Union[str, Any] = fs_config.encoder_ffn_embed_dim
lowercase_ : Tuple = 1E-5
lowercase_ : Union[str, Any] = fs_config.encoder_layerdrop
lowercase_ : Tuple = fs_config.encoder_attention_heads
lowercase_ : List[str] = fs_config.conv_pos_groups
lowercase_ : Union[str, Any] = fs_config.conv_pos
lowercase_ : str = len(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = fs_config.encoder_layers
lowercase_ : str = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
lowercase_ : Dict = model.cfg
lowercase_ : Dict = fs_config.final_dropout
lowercase_ : Dict = fs_config.layerdrop
lowercase_ : Optional[int] = fs_config.activation_dropout
lowercase_ : Any = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
lowercase_ : List[Any] = fs_config.attention_dropout
lowercase_ : Tuple = fs_config.dropout_input
lowercase_ : List[Any] = fs_config.dropout
lowercase_ : Any = fs_config.mask_channel_length
lowercase_ : str = fs_config.mask_channel_prob
lowercase_ : Optional[Any] = fs_config.mask_length
lowercase_ : Tuple = fs_config.mask_prob
lowercase_ : List[Any] = 'Wav2Vec2FeatureExtractor'
lowercase_ : Union[str, Any] = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def lowercase__( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Tuple=True ):
if is_finetuned:
lowercase_ , lowercase_ , lowercase_ : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
lowercase_ : List[str] = SEWConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
else:
lowercase_ : Tuple = convert_config(model[0] , __SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = model[0].eval()
lowercase_ : List[Any] = True if config.feat_extract_norm == 'layer' else False
lowercase_ : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )
if is_finetuned:
if dict_path:
lowercase_ : Dict = Dictionary.load(__SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase_ : str = target_dict.pad_index
lowercase_ : Union[str, Any] = target_dict.bos_index
lowercase_ : Tuple = target_dict.pad_index
lowercase_ : List[Any] = target_dict.bos_index
lowercase_ : Any = target_dict.eos_index
lowercase_ : str = len(target_dict.symbols )
lowercase_ : Optional[Any] = os.path.join(__SCREAMING_SNAKE_CASE , 'vocab.json' )
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE ) )
return
os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices , __SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = WavaVecaCTCTokenizer(
__SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=__SCREAMING_SNAKE_CASE , )
lowercase_ : Tuple = WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
lowercase_ : List[str] = SEWForCTC(__SCREAMING_SNAKE_CASE )
else:
lowercase_ : Any = SEWModel(__SCREAMING_SNAKE_CASE )
feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE )
recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
hf_model.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE =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(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__SCREAMING_SNAKE_CASE =parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 213 | 1 |
'''simple docstring'''
import os
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
lowercase =logging.get_logger(__name__)
lowercase ='▁'
lowercase ={'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}
lowercase ={
'vocab_file': {
'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
},
'monolingual_vocab_file': {
'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
},
}
lowercase ={'vinai/bartpho-syllable': 1024}
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase =VOCAB_FILES_NAMES
UpperCAmelCase =PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase =["input_ids", "attention_mask"]
def __init__( self , snake_case , snake_case , snake_case="<s>" , snake_case="</s>" , snake_case="</s>" , snake_case="<s>" , snake_case="<unk>" , snake_case="<pad>" , snake_case="<mask>" , snake_case = None , **snake_case , ) -> None:
'''simple docstring'''
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase : str =AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case) if isinstance(snake_case , snake_case) else mask_token
_UpperCAmelCase : Union[str, Any] ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=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 , )
_UpperCAmelCase : Tuple =vocab_file
_UpperCAmelCase : Optional[int] =monolingual_vocab_file
_UpperCAmelCase : str =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(snake_case))
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_UpperCAmelCase : int ={}
_UpperCAmelCase : Optional[Any] =0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(snake_case) not in self.fairseq_tokens_to_ids:
_UpperCAmelCase : List[str] =cnt
cnt += 1
with open(snake_case , 'r' , encoding='utf-8') as f:
for line in f.readlines():
_UpperCAmelCase : Union[str, Any] =line.strip().split()[0]
_UpperCAmelCase : List[Any] =len(self.fairseq_tokens_to_ids)
if str(snake_case) not in self.fairseq_tokens_to_ids:
_UpperCAmelCase : Tuple =len(self.fairseq_tokens_to_ids)
_UpperCAmelCase : Dict ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self) -> Dict:
'''simple docstring'''
_UpperCAmelCase : int =self.__dict__.copy()
_UpperCAmelCase : Tuple =None
_UpperCAmelCase : Any =self.sp_model.serialized_model_proto()
return state
def __setstate__( self , snake_case) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Dict =d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
_UpperCAmelCase : List[str] ={}
_UpperCAmelCase : Optional[Any] =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
def lowerCAmelCase ( self , snake_case , snake_case = None) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase : int =[self.cls_token_id]
_UpperCAmelCase : Any =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase ( self , snake_case , snake_case = None , snake_case = False) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case , token_ids_a=snake_case , already_has_special_tokens=snake_case)
if token_ids_a is None:
return [1] + ([0] * len(snake_case)) + [1]
return [1] + ([0] * len(snake_case)) + [1, 1] + ([0] * len(snake_case)) + [1]
def lowerCAmelCase ( self , snake_case , snake_case = None) -> List[int]:
'''simple docstring'''
_UpperCAmelCase : Any =[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 + sep + token_ids_a + sep) * [0]
@property
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
return len(self.fairseq_ids_to_tokens)
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCAmelCase : str ={self.convert_ids_to_tokens(snake_case): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def lowerCAmelCase ( self , snake_case) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(snake_case , out_type=snake_case)
def lowerCAmelCase ( self , snake_case) -> List[str]:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def lowerCAmelCase ( self , snake_case) -> Dict:
'''simple docstring'''
return self.fairseq_ids_to_tokens[index]
def lowerCAmelCase ( self , snake_case) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict =''.join(snake_case).replace(snake_case , ' ').strip()
return out_string
def lowerCAmelCase ( self , snake_case , snake_case = None) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
_UpperCAmelCase : str =os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
_UpperCAmelCase : Union[str, Any] =os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
_UpperCAmelCase : int =self.sp_model.serialized_model_proto()
fi.write(snake_case)
if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
snake_case) and os.path.isfile(self.monolingual_vocab_file):
copyfile(self.monolingual_vocab_file , snake_case)
elif not os.path.isfile(self.monolingual_vocab_file):
with open(snake_case , 'w' , encoding='utf-8') as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f"{str(snake_case)} \n")
return out_vocab_file, out_monolingual_vocab_file
| 242 |
'''simple docstring'''
lowercase =[0, 2, 4, 6, 8]
lowercase =[1, 3, 5, 7, 9]
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[int] , __lowerCamelCase : int ):
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 1_0
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_UpperCAmelCase : Union[str, Any] =0
for digit in range(1_0 ):
_UpperCAmelCase : str =digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 1_0 , __lowerCamelCase , __lowerCamelCase )
return result
_UpperCAmelCase : Optional[Any] =0
for digita in range(1_0 ):
_UpperCAmelCase : Any =digita
if (remainder + digita) % 2 == 0:
_UpperCAmelCase : Optional[int] =ODD_DIGITS
else:
_UpperCAmelCase : Union[str, Any] =EVEN_DIGITS
for digita in other_parity_digits:
_UpperCAmelCase : int =digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 1_0 , __lowerCamelCase , __lowerCamelCase , )
return result
def lowerCamelCase__ ( __lowerCamelCase : int = 9 ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] =0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(__lowerCamelCase , 0 , [0] * length , __lowerCamelCase )
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 242 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
'''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
'''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
'''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
'''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
'''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
'''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
'''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
'''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Optional[int] = '''xmod'''
def __init__( self ,__UpperCAmelCase=3_0522 ,__UpperCAmelCase=768 ,__UpperCAmelCase=12 ,__UpperCAmelCase=12 ,__UpperCAmelCase=3072 ,__UpperCAmelCase="gelu" ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=512 ,__UpperCAmelCase=2 ,__UpperCAmelCase=0.0_2 ,__UpperCAmelCase=1E-12 ,__UpperCAmelCase=1 ,__UpperCAmelCase=0 ,__UpperCAmelCase=2 ,__UpperCAmelCase="absolute" ,__UpperCAmelCase=True ,__UpperCAmelCase=None ,__UpperCAmelCase=False ,__UpperCAmelCase=2 ,__UpperCAmelCase=False ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=("en_XX",) ,__UpperCAmelCase=None ,**__UpperCAmelCase ,) -> List[Any]:
super().__init__(pad_token_id=__UpperCAmelCase ,bos_token_id=__UpperCAmelCase ,eos_token_id=__UpperCAmelCase ,**__UpperCAmelCase )
lowerCAmelCase__ : Dict = vocab_size
lowerCAmelCase__ : Optional[int] = hidden_size
lowerCAmelCase__ : Union[str, Any] = num_hidden_layers
lowerCAmelCase__ : List[Any] = num_attention_heads
lowerCAmelCase__ : int = hidden_act
lowerCAmelCase__ : str = intermediate_size
lowerCAmelCase__ : List[Any] = hidden_dropout_prob
lowerCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
lowerCAmelCase__ : Optional[Any] = max_position_embeddings
lowerCAmelCase__ : Tuple = type_vocab_size
lowerCAmelCase__ : Optional[int] = initializer_range
lowerCAmelCase__ : Dict = layer_norm_eps
lowerCAmelCase__ : List[Any] = position_embedding_type
lowerCAmelCase__ : Dict = use_cache
lowerCAmelCase__ : Tuple = classifier_dropout
lowerCAmelCase__ : Union[str, Any] = pre_norm
lowerCAmelCase__ : Union[str, Any] = adapter_reduction_factor
lowerCAmelCase__ : List[str] = adapter_layer_norm
lowerCAmelCase__ : Optional[Any] = adapter_reuse_layer_norm
lowerCAmelCase__ : int = ln_before_adapter
lowerCAmelCase__ : Any = list(__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = default_language
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase__ : List[str] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase__ : Optional[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 37 | import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class _lowercase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
UpperCamelCase_ : Optional[Any] = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=snake_case , multi_process=snake_case , )
UpperCamelCase_ : Tuple = TensorFlowBenchmark(snake_case )
UpperCamelCase_ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Dict = 'sgugger/tiny-distilbert-classification'
UpperCamelCase_ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case , only_pretrain_model=snake_case , )
UpperCamelCase_ : int = TensorFlowBenchmark(snake_case )
UpperCamelCase_ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : str = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case , )
UpperCamelCase_ : List[str] = TensorFlowBenchmark(snake_case )
UpperCamelCase_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : List[str] = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : str = AutoConfig.from_pretrained(snake_case )
UpperCamelCase_ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=snake_case , multi_process=snake_case , )
UpperCamelCase_ : int = TensorFlowBenchmark(snake_case , [config] )
UpperCamelCase_ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : List[str] = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : int = AutoConfig.from_pretrained(snake_case )
UpperCamelCase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case , )
UpperCamelCase_ : Tuple = TensorFlowBenchmark(snake_case , [config] )
UpperCamelCase_ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case , )
UpperCamelCase_ : Optional[int] = TensorFlowBenchmark(snake_case )
UpperCamelCase_ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
"""simple docstring"""
UpperCamelCase_ : Tuple = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : List[str] = AutoConfig.from_pretrained(snake_case )
UpperCamelCase_ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case , )
UpperCamelCase_ : List[Any] = TensorFlowBenchmark(snake_case , [config] )
UpperCamelCase_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Tuple = 'patrickvonplaten/t5-tiny-random'
UpperCamelCase_ : List[str] = AutoConfig.from_pretrained(snake_case )
UpperCamelCase_ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case , )
UpperCamelCase_ : int = TensorFlowBenchmark(snake_case , configs=[config] )
UpperCamelCase_ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : int = 'sshleifer/tiny-gpt2'
UpperCamelCase_ : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , use_xla=snake_case , multi_process=snake_case , )
UpperCamelCase_ : Union[str, Any] = TensorFlowBenchmark(snake_case )
UpperCamelCase_ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase_ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=snake_case , save_to_csv=snake_case , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(snake_case , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(snake_case , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(snake_case , 'env.csv' ) , multi_process=snake_case , )
UpperCamelCase_ : List[str] = TensorFlowBenchmark(snake_case )
benchmark.run()
self.assertTrue(Path(os.path.join(snake_case , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case , 'env.csv' ) ).exists() )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(snake_case : Union[str, Any] ):
self.assertTrue(hasattr(snake_case , 'sequential' ) )
self.assertTrue(hasattr(snake_case , 'cumulative' ) )
self.assertTrue(hasattr(snake_case , 'current' ) )
self.assertTrue(hasattr(snake_case , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase_ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=snake_case , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(snake_case , 'log.txt' ) , log_print=snake_case , trace_memory_line_by_line=snake_case , eager_mode=snake_case , multi_process=snake_case , )
UpperCamelCase_ : Tuple = TensorFlowBenchmark(snake_case )
UpperCamelCase_ : Union[str, Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(snake_case , 'log.txt' ) ).exists() )
| 175 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: Any = logging.get_logger(__name__)
a__: Optional[int] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class SCREAMING_SNAKE_CASE__ ( __lowercase ):
__SCREAMING_SNAKE_CASE = '''trocr'''
__SCREAMING_SNAKE_CASE = ['''past_key_values''']
__SCREAMING_SNAKE_CASE = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self,__lowerCamelCase=5_0265,__lowerCamelCase=1024,__lowerCamelCase=12,__lowerCamelCase=16,__lowerCamelCase=4096,__lowerCamelCase="gelu",__lowerCamelCase=512,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=2,__lowerCamelCase=0.02,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=1,__lowerCamelCase=0,__lowerCamelCase=2,**__lowerCamelCase,):
A__ = vocab_size
A__ = d_model
A__ = decoder_layers
A__ = decoder_attention_heads
A__ = decoder_ffn_dim
A__ = activation_function
A__ = max_position_embeddings
A__ = dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = init_std
A__ = decoder_layerdrop
A__ = use_cache
A__ = scale_embedding
A__ = use_learned_position_embeddings
A__ = layernorm_embedding
super().__init__(
pad_token_id=_a,bos_token_id=_a,eos_token_id=_a,decoder_start_token_id=_a,**_a,)
| 350 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: Union[str, Any] = False
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ):
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = '''A painting of a squirrel eating a burger '''
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__lowerCamelCase )
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = generator.manual_seed(0 )
A__ = pipe(
prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def UpperCamelCase ( self ):
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained(
'''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = '''A painting of a squirrel eating a burger '''
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images
A__ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
A__ = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 39 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {'''vocab_file''': '''vocab.txt'''}
_snake_case = {
'''vocab_file''': {
'''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
'''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
},
}
_snake_case = {
'''facebook/esm2_t6_8M_UR50D''': 1024,
'''facebook/esm2_t12_35M_UR50D''': 1024,
}
def A ( _lowerCamelCase ):
'''simple docstring'''
with open(__lowerCamelCase , "r" ) as f:
_lowerCAmelCase : List[Any] = f.read().splitlines()
return [l.strip() for l in lines]
class UpperCAmelCase_ ( _lowerCAmelCase):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self, __a, __a="<unk>", __a="<cls>", __a="<pad>", __a="<mask>", __a="<eos>", **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Union[str, Any] = load_vocab_file(__a)
_lowerCAmelCase : Optional[int] = dict(enumerate(self.all_tokens))
_lowerCAmelCase : Union[str, Any] = {tok: ind for ind, tok in enumerate(self.all_tokens)}
_lowerCAmelCase : Union[str, Any] = unk_token
_lowerCAmelCase : Optional[int] = cls_token
_lowerCAmelCase : Union[str, Any] = pad_token
_lowerCAmelCase : Optional[Any] = mask_token
_lowerCAmelCase : str = eos_token
_lowerCAmelCase : List[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens)
def snake_case__ ( self, __a):
'''simple docstring'''
return self._id_to_token.get(__a, self.unk_token)
def snake_case__ ( self, __a):
'''simple docstring'''
return self._token_to_id.get(__a, self._token_to_id.get(self.unk_token))
def snake_case__ ( self, __a, **__a):
'''simple docstring'''
return text.split()
def snake_case__ ( self, __a=False):
'''simple docstring'''
return len(self._id_to_token)
def snake_case__ ( self):
'''simple docstring'''
return {token: i for i, token in enumerate(self.all_tokens)}
def snake_case__ ( self, __a):
'''simple docstring'''
return self._token_to_id.get(__a, self._token_to_id.get(self.unk_token))
def snake_case__ ( self, __a):
'''simple docstring'''
return self._id_to_token.get(__a, self.unk_token)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Any = [self.cls_token_id]
_lowerCAmelCase : Tuple = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!")
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def snake_case__ ( self, __a, __a = None, __a = False):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model.")
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
_lowerCAmelCase : str = [1] + ([0] * len(__a)) + [1]
if token_ids_a is not None:
mask += [0] * len(__a) + [1]
return mask
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : int = os.path.join(__a, (filename_prefix + "-" if filename_prefix else "") + "vocab.txt")
with open(__a, "w") as f:
f.write("\n".join(self.all_tokens))
return (vocab_file,)
@property
def snake_case__ ( self):
'''simple docstring'''
return self.get_vocab_size(with_added_tokens=__a)
def snake_case__ ( self, __a, __a = False):
'''simple docstring'''
return super()._add_tokens(__a, special_tokens=__a)
| 36 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class a_ ( unittest.TestCase ):
def __init__( self : List[str] , lowercase : str , lowercase : Union[str, Any]=13 , lowercase : int=7 , lowercase : List[str]=True , lowercase : int=True , lowercase : str=True , lowercase : Any=True , lowercase : List[str]=99 , lowercase : Union[str, Any]=32 , lowercase : Optional[Any]=5 , lowercase : Dict=4 , lowercase : Dict=37 , lowercase : Dict="gelu" , lowercase : Optional[int]=0.1 , lowercase : str=0.1 , lowercase : List[Any]=512 , lowercase : str=16 , lowercase : Dict=2 , lowercase : Any=0.02 , lowercase : Any=4 , ):
"""simple docstring"""
lowercase_ :List[str] = parent
lowercase_ :Any = batch_size
lowercase_ :Dict = seq_length
lowercase_ :Union[str, Any] = is_training
lowercase_ :Optional[int] = use_attention_mask
lowercase_ :Any = use_token_type_ids
lowercase_ :Union[str, Any] = use_labels
lowercase_ :Dict = vocab_size
lowercase_ :Tuple = hidden_size
lowercase_ :Tuple = num_hidden_layers
lowercase_ :Optional[int] = num_attention_heads
lowercase_ :Optional[Any] = intermediate_size
lowercase_ :str = hidden_act
lowercase_ :Tuple = hidden_dropout_prob
lowercase_ :Optional[Any] = attention_probs_dropout_prob
lowercase_ :Tuple = max_position_embeddings
lowercase_ :Any = type_vocab_size
lowercase_ :int = type_sequence_label_size
lowercase_ :Tuple = initializer_range
lowercase_ :Optional[Any] = num_choices
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
lowercase_ :int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ :Union[str, Any] = None
if self.use_attention_mask:
lowercase_ :Dict = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ :List[str] = None
if self.use_token_type_ids:
lowercase_ :str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ :Optional[Any] = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase__ ( self : Union[str, Any] ):
"""simple docstring"""
lowercase_ :int = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ , lowercase_ :Tuple = config_and_inputs
lowercase_ :Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
lowercase_ :Any = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ , lowercase_ :Union[str, Any] = config_and_inputs
lowercase_ :Dict = True
lowercase_ :Optional[int] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase_ :str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class a_ ( _lowerCAmelCase , unittest.TestCase ):
__A = True
__A = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase__ ( self : Any ):
"""simple docstring"""
lowercase_ :Optional[Any] = FlaxBertModelTester(self )
@slow
def lowercase__ ( self : List[str] ):
"""simple docstring"""
lowercase_ :List[str] = FlaxBertModel.from_pretrained("bert-base-cased" )
lowercase_ :str = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowercase )
| 223 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class UpperCAmelCase__ ( unittest.TestCase):
def __lowerCamelCase ( self ) -> Optional[int]:
__UpperCamelCase = inspect.getfile(accelerate.test_utils )
__UpperCamelCase = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps""", """test_metrics.py"""] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
__UpperCamelCase = test_metrics
@require_cpu
def __lowerCamelCase ( self ) -> Optional[int]:
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def __lowerCamelCase ( self ) -> List[Any]:
debug_launcher(self.test_metrics.main )
@require_single_gpu
def __lowerCamelCase ( self ) -> Union[str, Any]:
self.test_metrics.main()
@require_multi_gpu
def __lowerCamelCase ( self ) -> Union[str, Any]:
print(f"Found {torch.cuda.device_count()} devices." )
__UpperCamelCase = ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase , env=os.environ.copy() )
| 243 |
'''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 UpperCAmelCase__ ( UpperCAmelCase_):
def __init__( self , lowercase=0.01 , lowercase=1_0_0_0 ) -> List[Any]:
__UpperCamelCase = p_stop
__UpperCamelCase = max_length
def __iter__( self ) -> Dict:
__UpperCamelCase = 0
__UpperCamelCase = False
while not stop and count < self.max_length:
yield count
count += 1
__UpperCamelCase = random.random() < self.p_stop
class UpperCAmelCase__ ( unittest.TestCase):
def __lowerCamelCase ( self , lowercase , lowercase , lowercase=False , lowercase=True ) -> List[str]:
__UpperCamelCase = [
BatchSamplerShard(lowercase , 2 , lowercase , split_batches=lowercase , even_batches=lowercase )
for i in range(2 )
]
__UpperCamelCase = [list(lowercase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(lowercase ) for shard in batch_sampler_shards] , [len(lowercase ) for e in expected] )
self.assertListEqual(lowercase , lowercase )
def __lowerCamelCase ( self ) -> Optional[Any]:
# Check the shards when the dataset is a round multiple of total batch size.
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=3 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__UpperCamelCase = BatchSampler(range(2_0 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
__UpperCamelCase = BatchSampler(range(2_0 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is very small.
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(lowercase , lowercase )
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase )
def __lowerCamelCase ( self ) -> Dict:
# Check the shards when the dataset is a round multiple of batch size.
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=4 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size.
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
# Check the shards when the dataset is very small.
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
def __lowerCamelCase ( self ) -> Optional[Any]:
# Check the shards when the dataset is a round multiple of total batch size.
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=3 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__UpperCamelCase = BatchSampler(range(2_0 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_0 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
[[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is very small.
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
__UpperCamelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
def __lowerCamelCase ( self ) -> str:
# Check the shards when the dataset is a round multiple of batch size.
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_4 ) , batch_size=4 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size.
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2_1 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [
[[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
[[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
# Check the shards when the dataset is very small.
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
__UpperCamelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
__UpperCamelCase = [BatchSamplerShard(lowercase , 2 , lowercase , even_batches=lowercase ) 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], [1_2, 1_3]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 1_0, 1_1]] )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase=False , lowercase=2 , lowercase=False ) -> List[str]:
random.seed(lowercase )
__UpperCamelCase = list(lowercase )
__UpperCamelCase = [
IterableDatasetShard(
lowercase , batch_size=lowercase , drop_last=lowercase , num_processes=lowercase , process_index=lowercase , split_batches=lowercase , )
for i in range(lowercase )
]
__UpperCamelCase = []
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(lowercase )
iterable_dataset_lists.append(list(lowercase ) )
__UpperCamelCase = 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
__UpperCamelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(lowercase ) , len(lowercase ) )
self.assertTrue(len(lowercase ) % shard_batch_size == 0 )
__UpperCamelCase = []
for idx in range(0 , len(lowercase ) , lowercase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(lowercase ) < len(lowercase ):
reference += reference
self.assertListEqual(lowercase , reference[: len(lowercase )] )
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = 4_2
__UpperCamelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
# Edge case with a very small dataset
__UpperCamelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = BatchSampler(range(1_6 ) , batch_size=4 , drop_last=lowercase )
__UpperCamelCase = SkipBatchSampler(lowercase , 2 )
self.assertListEqual(list(lowercase ) , [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]] )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = SkipDataLoader(list(range(1_6 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]] )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = DataLoader(list(range(1_6 ) ) , batch_size=4 )
__UpperCamelCase = skip_first_batches(lowercase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]] )
def __lowerCamelCase ( self ) -> Tuple:
__UpperCamelCase = DataLoaderShard(list(range(1_6 ) ) , batch_size=4 )
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def __lowerCamelCase ( self ) -> Tuple:
Accelerator()
__UpperCamelCase = DataLoaderDispatcher(range(1_6 ) , batch_size=4 )
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 243 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :str = logging.get_logger(__name__)
_lowerCAmelCase :Any = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class _UpperCAmelCase ( A__ ):
'''simple docstring'''
a__ ='''roc_bert'''
def __init__( self , A=3_0_5_2_2 , A=7_6_8 , A=1_2 , A=1_2 , A=3_0_7_2 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=2 , A=0.02 , A=1E-12 , A=True , A=0 , A="absolute" , A=None , A=True , A=True , A=7_6_8 , A=9_1_0 , A=5_1_2 , A=2_4_8_5_8 , A=True , **A , ) -> Any:
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : int = max_position_embeddings
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : Tuple = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : Optional[int] = layer_norm_eps
_UpperCAmelCase : str = use_cache
_UpperCAmelCase : Tuple = enable_pronunciation
_UpperCAmelCase : Dict = enable_shape
_UpperCAmelCase : List[Any] = pronunciation_embed_dim
_UpperCAmelCase : Union[str, Any] = pronunciation_vocab_size
_UpperCAmelCase : List[str] = shape_embed_dim
_UpperCAmelCase : Optional[int] = shape_vocab_size
_UpperCAmelCase : List[Any] = concat_input
_UpperCAmelCase : List[Any] = position_embedding_type
_UpperCAmelCase : Union[str, Any] = classifier_dropout
super().__init__(pad_token_id=A , **A )
| 263 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ = """"""
lowercase__ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self : List[str], lowerCamelCase : Optional[DatasetInfo] = None, lowerCamelCase : Optional[str] = None, **lowerCamelCase : str, ):
'''simple docstring'''
super().__init__(self, **lowerCamelCase )
lowercase__ = repo_info
lowercase__ = token
lowercase__ = None
def lowercase__ ( self : Optional[Any] ):
'''simple docstring'''
if self.dir_cache is None:
lowercase__ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
lowercase__ = {
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(lowerCamelCase ): {'''name''': str(lowerCamelCase ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowercase__ ( self : Any, lowerCamelCase : str, lowerCamelCase : str = "rb", **lowerCamelCase : Any, ):
'''simple docstring'''
if not isinstance(self.repo_info, lowerCamelCase ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
lowercase__ = hf_hub_url(self.repo_info.id, lowerCamelCase, revision=self.repo_info.sha )
return fsspec.open(
lowerCamelCase, mode=lowerCamelCase, headers=get_authentication_headers_for_url(lowerCamelCase, use_auth_token=self.token ), client_kwargs={'''trust_env''': True}, ).open()
def lowercase__ ( self : Dict, lowerCamelCase : Any, **lowerCamelCase : int ):
'''simple docstring'''
self._get_dirs()
lowercase__ = self._strip_protocol(lowerCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(lowerCamelCase )
def lowercase__ ( self : Optional[Any], lowerCamelCase : List[str], lowerCamelCase : Optional[int]=False, **lowerCamelCase : str ):
'''simple docstring'''
self._get_dirs()
lowercase__ = PurePosixPath(path.strip('''/''' ) )
lowercase__ = {}
for p, f in self.dir_cache.items():
lowercase__ = PurePosixPath(p.strip('''/''' ) )
lowercase__ = p.parent
if root == path:
lowercase__ = f
lowercase__ = list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 207 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
__lowercase = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def lowerCAmelCase (__UpperCamelCase : Tuple=True ):
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_UpperCamelCase ) )
class _lowercase ( _UpperCamelCase ):
"""simple docstring"""
lowercase__ = None
lowercase__ = None
def UpperCAmelCase_ ( self : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str ) -> Optional[int]:
'''simple docstring'''
with TemporaryDirectory() as tmp_dir:
__UpperCamelCase =dataset_module_factory(_SCREAMING_SNAKE_CASE , cache_dir=_SCREAMING_SNAKE_CASE )
__UpperCamelCase =import_main_class(dataset_module.module_path , dataset=_SCREAMING_SNAKE_CASE )
__UpperCamelCase =builder_cls(
cache_dir=_SCREAMING_SNAKE_CASE , config_name=_SCREAMING_SNAKE_CASE , hash=dataset_module.hash , )
__UpperCamelCase ='''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=_SCREAMING_SNAKE_CASE ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
__UpperCamelCase =cached_path(_SCREAMING_SNAKE_CASE , cache_dir=_SCREAMING_SNAKE_CASE )
self.assertTrue(os.path.exists(_SCREAMING_SNAKE_CASE ) )
@pytest.mark.integration
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
__UpperCamelCase =dataset_module_factory('''wikipedia''' , cache_dir=_UpperCamelCase )
__UpperCamelCase =import_main_class(dataset_module.module_path )
__UpperCamelCase =builder_cls(
cache_dir=_UpperCamelCase , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
__UpperCamelCase =None
builder_instance.download_and_prepare()
__UpperCamelCase =builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowerCAmelCase (__UpperCamelCase : Optional[int] ):
"""simple docstring"""
__UpperCamelCase =dataset_module_factory('''wikipedia''' , cache_dir=_UpperCamelCase )
__UpperCamelCase =import_main_class(dataset_module.module_path , dataset=_UpperCamelCase )
__UpperCamelCase =builder_cls(
cache_dir=_UpperCamelCase , config_name='''20220301.frr''' , hash=dataset_module.hash , )
__UpperCamelCase =builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_UpperCamelCase , _UpperCamelCase )
assert "train" in ds
assert isinstance(ds['''train'''] , _UpperCamelCase )
assert next(iter(ds['''train'''] ) )
| 368 | """simple docstring"""
def lowerCAmelCase (__UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
__UpperCamelCase =[0 for i in range(r + 1 )]
# nc0 = 1
__UpperCamelCase =1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
__UpperCamelCase =min(__UpperCamelCase , __UpperCamelCase )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 85 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 279 |
from collections import defaultdict
from math import gcd
def _a ( UpperCamelCase_ : int = 1_500_000 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = defaultdict(UpperCamelCase_ )
lowerCAmelCase__ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , UpperCamelCase_ , 2 ):
if gcd(UpperCamelCase_ , UpperCamelCase_ ) > 1:
continue
lowerCAmelCase__ = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(UpperCamelCase_ , limit + 1 , UpperCamelCase_ ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F"{solution() = }")
| 340 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _a :
pass
| 362 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
'''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class _a ( _lowercase):
_a : Optional[int] = '''xlm-roberta-xl'''
def __init__( self : Any , _SCREAMING_SNAKE_CASE : str=25_0880 , _SCREAMING_SNAKE_CASE : Optional[Any]=2560 , _SCREAMING_SNAKE_CASE : int=36 , _SCREAMING_SNAKE_CASE : Optional[int]=32 , _SCREAMING_SNAKE_CASE : Any=1_0240 , _SCREAMING_SNAKE_CASE : List[str]="gelu" , _SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , _SCREAMING_SNAKE_CASE : Dict=0.1 , _SCREAMING_SNAKE_CASE : Optional[int]=514 , _SCREAMING_SNAKE_CASE : Optional[int]=1 , _SCREAMING_SNAKE_CASE : Tuple=0.02 , _SCREAMING_SNAKE_CASE : Dict=1E-05 , _SCREAMING_SNAKE_CASE : Tuple=1 , _SCREAMING_SNAKE_CASE : Optional[int]=0 , _SCREAMING_SNAKE_CASE : Tuple=2 , _SCREAMING_SNAKE_CASE : Union[str, Any]="absolute" , _SCREAMING_SNAKE_CASE : Any=True , _SCREAMING_SNAKE_CASE : Any=None , **_SCREAMING_SNAKE_CASE : Tuple , )-> str:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = vocab_size
lowerCAmelCase__ : List[Any] = hidden_size
lowerCAmelCase__ : int = num_hidden_layers
lowerCAmelCase__ : Dict = num_attention_heads
lowerCAmelCase__ : str = hidden_act
lowerCAmelCase__ : List[Any] = intermediate_size
lowerCAmelCase__ : Tuple = hidden_dropout_prob
lowerCAmelCase__ : str = attention_probs_dropout_prob
lowerCAmelCase__ : List[str] = max_position_embeddings
lowerCAmelCase__ : Any = type_vocab_size
lowerCAmelCase__ : Optional[Any] = initializer_range
lowerCAmelCase__ : Optional[int] = layer_norm_eps
lowerCAmelCase__ : Optional[int] = position_embedding_type
lowerCAmelCase__ : Any = use_cache
lowerCAmelCase__ : List[Any] = classifier_dropout
class _a ( _lowercase):
@property
def UpperCAmelCase__( self : Any )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase__ : int = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase__ : List[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 211 | 0 |
import math
def __lowercase ( __lowerCAmelCase : float , __lowerCAmelCase : float ):
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_6_0:
raise ValueError('In Malus Law, the angle is in the range 0-360 degrees' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__lowerCAmelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='''malus_law''')
| 240 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class snake_case_ (unittest.TestCase ):
def lowerCamelCase__( self :Any ,__snake_case :List[Any] ) -> Union[str, Any]:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] ,model_result['ss'] ):
a__ = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(__snake_case )
def lowerCamelCase__( self :List[str] ) -> Union[str, Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :List[Any] ) -> Union[str, Any]:
a__ = 'sgugger/tiny-distilbert-classification'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,only_pretrain_model=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Optional[Any] ) -> Optional[Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,torchscript=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == 'cpu' ,'Cant do half precision' )
def lowerCamelCase__( self :int ) -> str:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,fpaa=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Optional[int] ) -> Union[str, Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = AutoConfig.from_pretrained(__snake_case )
# set architectures equal to `None`
a__ = None
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Dict ) -> int:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == 'cpu' ,'Can\'t do half precision' )
def lowerCamelCase__( self :int ) -> List[str]:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,fpaa=__snake_case ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__( self :str ) -> Union[str, Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :List[Any] ) -> Any:
a__ = 'sshleifer/tinier_bart'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Tuple ) -> Dict:
a__ = 'sshleifer/tiny-gpt2'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__( self :Union[str, Any] ) -> Optional[int]:
a__ = 'sshleifer/tinier_bart'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__( self :Optional[int] ) -> List[Any]:
a__ = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,save_to_csv=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,inference_time_csv_file=os.path.join(__snake_case ,'inf_time.csv' ) ,train_memory_csv_file=os.path.join(__snake_case ,'train_mem.csv' ) ,inference_memory_csv_file=os.path.join(__snake_case ,'inf_mem.csv' ) ,train_time_csv_file=os.path.join(__snake_case ,'train_time.csv' ) ,env_info_csv_file=os.path.join(__snake_case ,'env.csv' ) ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
benchmark.run()
self.assertTrue(Path(os.path.join(__snake_case ,'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'train_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'train_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'env.csv' ) ).exists() )
def lowerCamelCase__( self :Optional[int] ) -> List[str]:
a__ = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(__snake_case :List[str] ):
self.assertTrue(hasattr(__snake_case ,'sequential' ) )
self.assertTrue(hasattr(__snake_case ,'cumulative' ) )
self.assertTrue(hasattr(__snake_case ,'current' ) )
self.assertTrue(hasattr(__snake_case ,'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,log_filename=os.path.join(__snake_case ,'log.txt' ) ,log_print=__snake_case ,trace_memory_line_by_line=__snake_case ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(__snake_case ,'log.txt' ) ).exists() )
| 240 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _lowerCAmelCase :
'''simple docstring'''
@staticmethod
def lowercase (*UpperCAmelCase , **UpperCAmelCase ) -> int:
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_snake_case = ObjectDetectionPipeline(model=_a , image_processor=_a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def lowercase (self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
_snake_case = object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png""" , threshold=0.0 )
self.assertGreater(len(_a ) , 0 )
for detected_object in outputs:
self.assertEqual(
_a , {
"""score""": ANY(_a ),
"""label""": ANY(_a ),
"""box""": {"""xmin""": ANY(_a ), """ymin""": ANY(_a ), """xmax""": ANY(_a ), """ymax""": ANY(_a )},
} , )
import datasets
_snake_case = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
_snake_case = [
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
]
_snake_case = object_detector(_a , threshold=0.0 )
self.assertEqual(len(_a ) , len(_a ) )
for outputs in batch_outputs:
self.assertGreater(len(_a ) , 0 )
for detected_object in outputs:
self.assertEqual(
_a , {
"""score""": ANY(_a ),
"""label""": ANY(_a ),
"""box""": {"""xmin""": ANY(_a ), """ymin""": ANY(_a ), """xmax""": ANY(_a ), """ymax""": ANY(_a )},
} , )
@require_tf
@unittest.skip("""Object detection not implemented in TF""" )
def lowercase (self ) -> Union[str, Any]:
pass
@require_torch
def lowercase (self ) -> int:
_snake_case = """hf-internal-testing/tiny-detr-mobilenetsv3"""
_snake_case = AutoModelForObjectDetection.from_pretrained(_a )
_snake_case = AutoFeatureExtractor.from_pretrained(_a )
_snake_case = ObjectDetectionPipeline(model=_a , feature_extractor=_a )
_snake_case = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=0.0 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
] , )
_snake_case = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
],
[
{"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
],
] , )
@require_torch
@slow
def lowercase (self ) -> Tuple:
_snake_case = """facebook/detr-resnet-50"""
_snake_case = AutoModelForObjectDetection.from_pretrained(_a )
_snake_case = AutoFeatureExtractor.from_pretrained(_a )
_snake_case = ObjectDetectionPipeline(model=_a , feature_extractor=_a )
_snake_case = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] , )
_snake_case = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
[
{"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
] , )
@require_torch
@slow
def lowercase (self ) -> List[str]:
_snake_case = """facebook/detr-resnet-50"""
_snake_case = pipeline("""object-detection""" , model=_a )
_snake_case = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] , )
_snake_case = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
[
{"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
] , )
@require_torch
@slow
def lowercase (self ) -> Dict:
_snake_case = 0.9985
_snake_case = """facebook/detr-resnet-50"""
_snake_case = pipeline("""object-detection""" , model=_a )
_snake_case = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=_a )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def lowercase (self ) -> Any:
_snake_case = """Narsil/layoutlmv3-finetuned-funsd"""
_snake_case = 0.9993
_snake_case = pipeline("""object-detection""" , model=_a , threshold=_a )
_snake_case = object_detector(
"""https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png""" )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
{"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
] , ) | 361 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(_SCREAMING_SNAKE_CASE , max_perimeter + 1 ):
_snake_case = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(_SCREAMING_SNAKE_CASE ):
_snake_case = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = 1000 ):
_snake_case = pythagorean_triple(_SCREAMING_SNAKE_CASE )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f'''Perimeter {solution()} has maximum solutions''') | 270 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class UpperCamelCase ( unittest.TestCase ):
def a_ ( self) -> Union[str, Any]:
snake_case_ = 'laion/clap-htsat-unfused'
snake_case_ = tempfile.mkdtemp()
def a_ ( self, **lowerCAmelCase__) -> Union[str, Any]:
return RobertaTokenizer.from_pretrained(self.checkpoint, **lowerCAmelCase__)
def a_ ( self, **lowerCAmelCase__) -> Union[str, Any]:
return ClapFeatureExtractor.from_pretrained(self.checkpoint, **lowerCAmelCase__)
def a_ ( self) -> int:
shutil.rmtree(self.tmpdirname)
def a_ ( self) -> Any:
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_feature_extractor()
snake_case_ = ClapProcessor(tokenizer=lowerCAmelCase__, feature_extractor=lowerCAmelCase__)
processor.save_pretrained(self.tmpdirname)
snake_case_ = ClapProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, lowerCAmelCase__)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, lowerCAmelCase__)
def a_ ( self) -> Tuple:
snake_case_ = ClapProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
snake_case_ = self.get_tokenizer(bos_token='(BOS)', eos_token='(EOS)')
snake_case_ = self.get_feature_extractor(do_normalize=lowerCAmelCase__, padding_value=1.0)
snake_case_ = ClapProcessor.from_pretrained(
self.tmpdirname, bos_token='(BOS)', eos_token='(EOS)', do_normalize=lowerCAmelCase__, padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, lowerCAmelCase__)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, lowerCAmelCase__)
def a_ ( self) -> str:
snake_case_ = self.get_feature_extractor()
snake_case_ = self.get_tokenizer()
snake_case_ = ClapProcessor(tokenizer=lowerCAmelCase__, feature_extractor=lowerCAmelCase__)
snake_case_ = floats_list((3, 1000))
snake_case_ = feature_extractor(lowerCAmelCase__, return_tensors='np')
snake_case_ = processor(audios=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) -> Any:
snake_case_ = self.get_feature_extractor()
snake_case_ = self.get_tokenizer()
snake_case_ = ClapProcessor(tokenizer=lowerCAmelCase__, feature_extractor=lowerCAmelCase__)
snake_case_ = 'This is a test string'
snake_case_ = processor(text=lowerCAmelCase__)
snake_case_ = tokenizer(lowerCAmelCase__)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def a_ ( self) -> List[str]:
snake_case_ = self.get_feature_extractor()
snake_case_ = self.get_tokenizer()
snake_case_ = ClapProcessor(tokenizer=lowerCAmelCase__, feature_extractor=lowerCAmelCase__)
snake_case_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ = processor.batch_decode(lowerCAmelCase__)
snake_case_ = tokenizer.batch_decode(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, lowerCAmelCase__)
def a_ ( self) -> List[Any]:
snake_case_ = self.get_feature_extractor()
snake_case_ = self.get_tokenizer()
snake_case_ = ClapProcessor(tokenizer=lowerCAmelCase__, feature_extractor=lowerCAmelCase__)
self.assertListEqual(
processor.model_input_names[2:], feature_extractor.model_input_names, msg='`processor` and `feature_extractor` model input names do not match', )
| 69 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
while a != 0:
_lowerCAmelCase , _lowerCAmelCase = b % a, a
return b
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) != 1:
_lowerCAmelCase = F'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(SCREAMING_SNAKE_CASE_ )
_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
| 158 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class _a ( _lowerCAmelCase ):
def snake_case ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCAmelCase__, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(lowerCAmelCase__, '''depth_multiplier''' ) )
class _a :
def __init__( self : Any, lowerCAmelCase__ : Union[str, Any], lowerCAmelCase__ : Optional[Any]=1_3, lowerCAmelCase__ : Dict=3, lowerCAmelCase__ : Optional[Any]=3_2, lowerCAmelCase__ : Dict=0.25, lowerCAmelCase__ : int=8, lowerCAmelCase__ : Optional[int]=True, lowerCAmelCase__ : Dict=1_0_2_4, lowerCAmelCase__ : int=3_2, lowerCAmelCase__ : Optional[int]="relu6", lowerCAmelCase__ : Dict=0.1, lowerCAmelCase__ : Optional[int]=0.02, lowerCAmelCase__ : str=True, lowerCAmelCase__ : List[str]=True, lowerCAmelCase__ : List[str]=1_0, lowerCAmelCase__ : Union[str, Any]=None, ) -> Dict:
'''simple docstring'''
_UpperCamelCase : List[Any] = parent
_UpperCamelCase : Any = batch_size
_UpperCamelCase : List[str] = num_channels
_UpperCamelCase : Tuple = image_size
_UpperCamelCase : Tuple = depth_multiplier
_UpperCamelCase : Optional[Any] = min_depth
_UpperCamelCase : int = tf_padding
_UpperCamelCase : Tuple = int(last_hidden_size * depth_multiplier )
_UpperCamelCase : Optional[int] = output_stride
_UpperCamelCase : int = hidden_act
_UpperCamelCase : str = classifier_dropout_prob
_UpperCamelCase : Dict = use_labels
_UpperCamelCase : Any = is_training
_UpperCamelCase : List[str] = num_labels
_UpperCamelCase : Dict = initializer_range
_UpperCamelCase : Union[str, Any] = scope
def snake_case ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCamelCase : Dict = None
_UpperCamelCase : List[Any] = None
if self.use_labels:
_UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size], self.num_labels )
_UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
_UpperCamelCase : Any = self.get_config()
return config, pixel_values, labels, pixel_labels
def snake_case ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, min_depth=self.min_depth, tf_padding=self.tf_padding, hidden_act=self.hidden_act, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def snake_case ( self : str, lowerCAmelCase__ : Tuple, lowerCAmelCase__ : List[Any], lowerCAmelCase__ : str, lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = MobileNetVaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
_UpperCamelCase : str = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape, (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def snake_case ( self : Dict, lowerCAmelCase__ : str, lowerCAmelCase__ : List[str], lowerCAmelCase__ : Any, lowerCAmelCase__ : List[Any] ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase : str = self.num_labels
_UpperCamelCase : Tuple = MobileNetVaForImageClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
_UpperCamelCase : str = model(lowerCAmelCase__, labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def snake_case ( self : Optional[Any] ) -> str:
'''simple docstring'''
_UpperCamelCase : int = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase : List[str] = config_and_inputs
_UpperCamelCase : Tuple = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _a ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
UpperCamelCase = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def snake_case ( self : Any ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Dict = MobileNetVaModelTester(self )
_UpperCamelCase : str = MobileNetVaConfigTester(self, config_class=lowerCAmelCase__, has_text_modality=lowerCAmelCase__ )
def snake_case ( self : Optional[Any] ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds''' )
def snake_case ( self : int ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings''' )
def snake_case ( self : str ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''MobileNetV1 does not output attentions''' )
def snake_case ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
pass
def snake_case ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : str = model_class(lowerCAmelCase__ )
_UpperCamelCase : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase : Optional[Any] = [*signature.parameters.keys()]
_UpperCamelCase : int = ['''pixel_values''']
self.assertListEqual(arg_names[:1], lowerCAmelCase__ )
def snake_case ( self : int ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def snake_case ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
def check_hidden_states_output(lowerCAmelCase__ : Any, lowerCAmelCase__ : str, lowerCAmelCase__ : List[Any] ):
_UpperCamelCase : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
_UpperCamelCase : Any = model(**self._prepare_for_class(lowerCAmelCase__, lowerCAmelCase__ ) )
_UpperCamelCase : Optional[int] = outputs.hidden_states
_UpperCamelCase : Tuple = 2_6
self.assertEqual(len(lowerCAmelCase__ ), lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : Optional[int] = True
check_hidden_states_output(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase : Dict = True
check_hidden_states_output(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__ )
def snake_case ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
@slow
def snake_case ( self : Dict ) -> List[str]:
'''simple docstring'''
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase : Union[str, Any] = MobileNetVaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def a_ ( ):
_UpperCamelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _a ( unittest.TestCase ):
@cached_property
def snake_case ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224''' ) if is_vision_available() else None
)
@slow
def snake_case ( self : Optional[int] ) -> Dict:
'''simple docstring'''
_UpperCamelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224''' ).to(lowerCAmelCase__ )
_UpperCamelCase : Any = self.default_image_processor
_UpperCamelCase : Optional[Any] = prepare_img()
_UpperCamelCase : Dict = image_processor(images=lowerCAmelCase__, return_tensors='''pt''' ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
_UpperCamelCase : str = model(**lowerCAmelCase__ )
# verify the logits
_UpperCamelCase : Dict = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, lowerCAmelCase__ )
_UpperCamelCase : int = torch.tensor([-4.1_739, -1.1_233, 3.1_205] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCAmelCase__, atol=1e-4 ) )
| 128 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _a ( _lowerCAmelCase , _lowerCAmelCase ):
@register_to_config
def __init__( self : Any, lowerCAmelCase__ : int = 1_2_8, lowerCAmelCase__ : int = 2_5_6, lowerCAmelCase__ : float = 2_000.0, lowerCAmelCase__ : int = 7_6_8, lowerCAmelCase__ : int = 1_2, lowerCAmelCase__ : int = 1_2, lowerCAmelCase__ : int = 6_4, lowerCAmelCase__ : int = 2_0_4_8, lowerCAmelCase__ : float = 0.1, ) -> Any:
'''simple docstring'''
super().__init__()
_UpperCamelCase : Any = nn.Sequential(
nn.Linear(lowerCAmelCase__, d_model * 4, bias=lowerCAmelCase__ ), nn.SiLU(), nn.Linear(d_model * 4, d_model * 4, bias=lowerCAmelCase__ ), nn.SiLU(), )
_UpperCamelCase : List[Any] = nn.Embedding(lowerCAmelCase__, lowerCAmelCase__ )
_UpperCamelCase : List[Any] = False
_UpperCamelCase : Optional[Any] = nn.Linear(lowerCAmelCase__, lowerCAmelCase__, bias=lowerCAmelCase__ )
_UpperCamelCase : Any = nn.Dropout(p=lowerCAmelCase__ )
_UpperCamelCase : List[str] = nn.ModuleList()
for lyr_num in range(lowerCAmelCase__ ):
# FiLM conditional T5 decoder
_UpperCamelCase : Any = DecoderLayer(d_model=lowerCAmelCase__, d_kv=lowerCAmelCase__, num_heads=lowerCAmelCase__, d_ff=lowerCAmelCase__, dropout_rate=lowerCAmelCase__ )
self.decoders.append(lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = TaLayerNorm(lowerCAmelCase__ )
_UpperCamelCase : Dict = nn.Dropout(p=lowerCAmelCase__ )
_UpperCamelCase : Dict = nn.Linear(lowerCAmelCase__, lowerCAmelCase__, bias=lowerCAmelCase__ )
def snake_case ( self : Optional[Any], lowerCAmelCase__ : Union[str, Any], lowerCAmelCase__ : Optional[int] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase : List[Any] = torch.mul(query_input.unsqueeze(-1 ), key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def snake_case ( self : str, lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Union[str, Any] = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_UpperCamelCase : Any = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time, embedding_dim=self.config.d_model, max_period=self.config.max_decoder_noise_time, ).to(dtype=self.dtype )
_UpperCamelCase : Union[str, Any] = self.conditioning_emb(lowerCAmelCase__ ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_UpperCamelCase : int = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_UpperCamelCase : int = torch.broadcast_to(
torch.arange(lowerCAmelCase__, device=decoder_input_tokens.device ), (batch, seq_length), )
_UpperCamelCase : Dict = self.position_encoding(lowerCAmelCase__ )
_UpperCamelCase : List[str] = self.continuous_inputs_projection(lowerCAmelCase__ )
inputs += position_encodings
_UpperCamelCase : Dict = self.dropout(lowerCAmelCase__ )
# decoder: No padding present.
_UpperCamelCase : Tuple = torch.ones(
decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_UpperCamelCase : Tuple = [(x, self.encoder_decoder_mask(lowerCAmelCase__, lowerCAmelCase__ )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_UpperCamelCase : int = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1 )
_UpperCamelCase : Dict = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1 )
for lyr in self.decoders:
_UpperCamelCase : List[Any] = lyr(
lowerCAmelCase__, conditioning_emb=lowerCAmelCase__, encoder_hidden_states=lowerCAmelCase__, encoder_attention_mask=lowerCAmelCase__, )[0]
_UpperCamelCase : Any = self.decoder_norm(lowerCAmelCase__ )
_UpperCamelCase : Tuple = self.post_dropout(lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = self.spec_out(lowerCAmelCase__ )
return spec_out
class _a ( nn.Module ):
def __init__( self : Union[str, Any], lowerCAmelCase__ : List[Any], lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : List[str], lowerCAmelCase__ : List[str], lowerCAmelCase__ : str, lowerCAmelCase__ : Union[str, Any]=1e-6 ) -> Optional[int]:
'''simple docstring'''
super().__init__()
_UpperCamelCase : Optional[int] = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=lowerCAmelCase__, d_kv=lowerCAmelCase__, num_heads=lowerCAmelCase__, dropout_rate=lowerCAmelCase__ ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=lowerCAmelCase__, d_kv=lowerCAmelCase__, num_heads=lowerCAmelCase__, dropout_rate=lowerCAmelCase__, layer_norm_epsilon=lowerCAmelCase__, ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=lowerCAmelCase__, d_ff=lowerCAmelCase__, dropout_rate=lowerCAmelCase__, layer_norm_epsilon=lowerCAmelCase__ ) )
def snake_case ( self : Union[str, Any], lowerCAmelCase__ : str, lowerCAmelCase__ : List[Any]=None, lowerCAmelCase__ : List[str]=None, lowerCAmelCase__ : List[Any]=None, lowerCAmelCase__ : Optional[Any]=None, lowerCAmelCase__ : Any=None, ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase : List[str] = self.layer[0](
lowerCAmelCase__, conditioning_emb=lowerCAmelCase__, attention_mask=lowerCAmelCase__, )
if encoder_hidden_states is not None:
_UpperCamelCase : Any = torch.where(encoder_attention_mask > 0, 0, -1e1_0 ).to(
encoder_hidden_states.dtype )
_UpperCamelCase : int = self.layer[1](
lowerCAmelCase__, key_value_states=lowerCAmelCase__, attention_mask=lowerCAmelCase__, )
# Apply Film Conditional Feed Forward layer
_UpperCamelCase : Optional[int] = self.layer[-1](lowerCAmelCase__, lowerCAmelCase__ )
return (hidden_states,)
class _a ( nn.Module ):
def __init__( self : Tuple, lowerCAmelCase__ : List[Any], lowerCAmelCase__ : Tuple, lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase : int = TaLayerNorm(lowerCAmelCase__ )
_UpperCamelCase : List[Any] = TaFiLMLayer(in_features=d_model * 4, out_features=lowerCAmelCase__ )
_UpperCamelCase : Any = Attention(query_dim=lowerCAmelCase__, heads=lowerCAmelCase__, dim_head=lowerCAmelCase__, out_bias=lowerCAmelCase__, scale_qk=lowerCAmelCase__ )
_UpperCamelCase : List[str] = nn.Dropout(lowerCAmelCase__ )
def snake_case ( self : Optional[int], lowerCAmelCase__ : Tuple, lowerCAmelCase__ : str=None, lowerCAmelCase__ : Union[str, Any]=None, ) -> List[str]:
'''simple docstring'''
_UpperCamelCase : Any = self.layer_norm(lowerCAmelCase__ )
if conditioning_emb is not None:
_UpperCamelCase : str = self.FiLMLayer(lowerCAmelCase__, lowerCAmelCase__ )
# Self-attention block
_UpperCamelCase : Tuple = self.attention(lowerCAmelCase__ )
_UpperCamelCase : Dict = hidden_states + self.dropout(lowerCAmelCase__ )
return hidden_states
class _a ( nn.Module ):
def __init__( self : Tuple, lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : List[str], lowerCAmelCase__ : str, lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
super().__init__()
_UpperCamelCase : Any = Attention(query_dim=lowerCAmelCase__, heads=lowerCAmelCase__, dim_head=lowerCAmelCase__, out_bias=lowerCAmelCase__, scale_qk=lowerCAmelCase__ )
_UpperCamelCase : Tuple = TaLayerNorm(lowerCAmelCase__, eps=lowerCAmelCase__ )
_UpperCamelCase : int = nn.Dropout(lowerCAmelCase__ )
def snake_case ( self : Optional[Any], lowerCAmelCase__ : List[Any], lowerCAmelCase__ : str=None, lowerCAmelCase__ : Union[str, Any]=None, ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.layer_norm(lowerCAmelCase__ )
_UpperCamelCase : Tuple = self.attention(
lowerCAmelCase__, encoder_hidden_states=lowerCAmelCase__, attention_mask=attention_mask.squeeze(1 ), )
_UpperCamelCase : str = hidden_states + self.dropout(lowerCAmelCase__ )
return layer_output
class _a ( nn.Module ):
def __init__( self : Tuple, lowerCAmelCase__ : int, lowerCAmelCase__ : Tuple, lowerCAmelCase__ : Any, lowerCAmelCase__ : str ) -> Tuple:
'''simple docstring'''
super().__init__()
_UpperCamelCase : Any = TaDenseGatedActDense(d_model=lowerCAmelCase__, d_ff=lowerCAmelCase__, dropout_rate=lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = TaFiLMLayer(in_features=d_model * 4, out_features=lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = TaLayerNorm(lowerCAmelCase__, eps=lowerCAmelCase__ )
_UpperCamelCase : List[Any] = nn.Dropout(lowerCAmelCase__ )
def snake_case ( self : Tuple, lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : List[str]=None ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : Any = self.layer_norm(lowerCAmelCase__ )
if conditioning_emb is not None:
_UpperCamelCase : Dict = self.film(lowerCAmelCase__, lowerCAmelCase__ )
_UpperCamelCase : str = self.DenseReluDense(lowerCAmelCase__ )
_UpperCamelCase : str = hidden_states + self.dropout(lowerCAmelCase__ )
return hidden_states
class _a ( nn.Module ):
def __init__( self : str, lowerCAmelCase__ : List[Any], lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : Dict ) -> Tuple:
'''simple docstring'''
super().__init__()
_UpperCamelCase : List[str] = nn.Linear(lowerCAmelCase__, lowerCAmelCase__, bias=lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = nn.Linear(lowerCAmelCase__, lowerCAmelCase__, bias=lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = nn.Linear(lowerCAmelCase__, lowerCAmelCase__, bias=lowerCAmelCase__ )
_UpperCamelCase : List[str] = nn.Dropout(lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = NewGELUActivation()
def snake_case ( self : str, lowerCAmelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.act(self.wi_a(lowerCAmelCase__ ) )
_UpperCamelCase : Dict = self.wi_a(lowerCAmelCase__ )
_UpperCamelCase : Any = hidden_gelu * hidden_linear
_UpperCamelCase : Any = self.dropout(lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = self.wo(lowerCAmelCase__ )
return hidden_states
class _a ( nn.Module ):
def __init__( self : Union[str, Any], lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[Any]=1e-6 ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase : Optional[Any] = nn.Parameter(torch.ones(lowerCAmelCase__ ) )
_UpperCamelCase : Tuple = eps
def snake_case ( self : Dict, lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = hidden_states.to(torch.floataa ).pow(2 ).mean(-1, keepdim=lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_UpperCamelCase : int = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class _a ( nn.Module ):
def snake_case ( self : Optional[Any], lowerCAmelCase__ : torch.Tensor ) -> torch.Tensor:
'''simple docstring'''
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(lowerCAmelCase__, 3.0 )) ))
class _a ( nn.Module ):
def __init__( self : List[str], lowerCAmelCase__ : Optional[Any], lowerCAmelCase__ : int ) -> List[Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase : Tuple = nn.Linear(lowerCAmelCase__, out_features * 2, bias=lowerCAmelCase__ )
def snake_case ( self : int, lowerCAmelCase__ : List[str], lowerCAmelCase__ : Any ) -> int:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.scale_bias(lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase : Union[str, Any] = torch.chunk(lowerCAmelCase__, 2, -1 )
_UpperCamelCase : Optional[int] = x * (1 + scale) + shift
return x
| 128 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = RoCBertTokenizer
A_ = None
A_ = False
A_ = True
A_ = filter_non_english
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
__a : List[Any] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
__a : Tuple = {}
__a : Tuple = {}
for i, value in enumerate(__a ):
__a : str = i
__a : int = i
__a : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__a : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
__a : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_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.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(__a , __a , ensure_ascii=__a )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(__a , __a , ensure_ascii=__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__a : List[str] = tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(__a , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__a ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__a ) , [5, 6, 2, 5, 7, 8] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = RoCBertBasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = RoCBertBasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = RoCBertBasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = RoCBertBasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = RoCBertBasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=__a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__a : Union[str, Any] = {}
for i, token in enumerate(__a ):
__a : Any = i
__a : Union[str, Any] = RoCBertWordpieceTokenizer(vocab=__a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__a ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
__a : str = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(__a ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__a : Optional[Any] = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
__a : Union[str, Any] = tokenizer_r.encode_plus(
__a , return_attention_mask=__a , return_token_type_ids=__a , return_offsets_mapping=__a , add_special_tokens=__a , )
__a : Optional[Any] = tokenizer_r.do_lower_case if hasattr(__a , 'do_lower_case' ) else False
__a : Any = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = ['的', '人', '有']
__a : Optional[int] = ''.join(__a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__a : Optional[int] = True
__a : Tuple = self.tokenizer_class.from_pretrained(__a , **__a )
__a : Optional[Any] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__a : Optional[int] = tokenizer_p.encode(__a , add_special_tokens=__a )
__a : Optional[int] = tokenizer_r.encode(__a , add_special_tokens=__a )
__a : int = tokenizer_r.convert_ids_to_tokens(__a )
__a : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
__a : List[str] = False
__a : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__a : Any = self.tokenizer_class.from_pretrained(__a , **__a )
__a : Any = tokenizer_r.encode(__a , add_special_tokens=__a )
__a : Optional[int] = tokenizer_p.encode(__a , add_special_tokens=__a )
__a : int = tokenizer_r.convert_ids_to_tokens(__a )
__a : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that only the first Chinese character is not preceded by "##".
__a : Optional[int] = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(__a )
]
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
@slow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__a : List[str] = tokenizer.encode('你好' , add_special_tokens=__a )
__a : Optional[int] = tokenizer.encode('你是谁' , add_special_tokens=__a )
__a : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__a : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[Any] = self.get_tokenizers(do_lower_case=__a )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
__a : Any = '你好，你是谁'
__a : List[Any] = tokenizer.tokenize(__a )
__a : List[Any] = tokenizer.convert_tokens_to_ids(__a )
__a : Tuple = tokenizer.convert_tokens_to_shape_ids(__a )
__a : Optional[Any] = tokenizer.convert_tokens_to_pronunciation_ids(__a )
__a : List[Any] = tokenizer.prepare_for_model(
__a , __a , __a , add_special_tokens=__a )
__a : str = tokenizer.encode_plus(__a , add_special_tokens=__a )
self.assertEqual(__a , __a )
| 27 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=lowerCAmelCase_ ):
"""simple docstring"""
A__ : Tuple = ['''speech''']
def __init__( self : List[Any] , *_snake_case : str , **_snake_case : List[Any] ):
requires_backends(self , ['''speech'''] )
class __lowerCAmelCase ( metaclass=lowerCAmelCase_ ):
"""simple docstring"""
A__ : List[Any] = ['''speech''']
def __init__( self : List[str] , *_snake_case : List[Any] , **_snake_case : Dict ):
requires_backends(self , ['''speech'''] )
| 156 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Tuple = logging.get_logger(__name__)
lowerCAmelCase_ : List[Any] = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}
class UpperCamelCase_ ( a_ ):
_A : int = 'mra'
def __init__( self , snake_case__=5_02_65 , snake_case__=7_68 , snake_case__=12 , snake_case__=12 , snake_case__=30_72 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=5_12 , snake_case__=1 , snake_case__=0.02 , snake_case__=1e-5 , snake_case__="absolute" , snake_case__=4 , snake_case__="full" , snake_case__=0 , snake_case__=0 , snake_case__=1 , snake_case__=0 , snake_case__=2 , **snake_case__ , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase = vocab_size
UpperCAmelCase = max_position_embeddings
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 = initializer_range
UpperCAmelCase = type_vocab_size
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = block_per_row
UpperCAmelCase = approx_mode
UpperCAmelCase = initial_prior_first_n_blocks
UpperCAmelCase = initial_prior_diagonal_n_blocks
| 248 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=a_ )
class UpperCamelCase_ ( a_ ):
_A : str = field(default='image-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
_A : ClassVar[Features] = Features({'image': Image()} )
_A : ClassVar[Features] = Features({'labels': ClassLabel} )
_A : str = "image"
_A : str = "labels"
def UpperCamelCase_ ( self , snake_case__ ) -> List[str]:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , snake_case__ ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
UpperCAmelCase = copy.deepcopy(self )
UpperCAmelCase = self.label_schema.copy()
UpperCAmelCase = features[self.label_column]
UpperCAmelCase = label_schema
return task_template
@property
def UpperCamelCase_ ( self ) -> Dict[str, str]:
"""simple docstring"""
return {
self.image_column: "image",
self.label_column: "labels",
}
| 248 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_A = logging.get_logger(__name__)
class _lowerCamelCase ( a_ ):
def __init__( self : int , *UpperCamelCase : int , **UpperCamelCase : Dict ) -> None:
"""simple docstring"""
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , UpperCamelCase , )
super().__init__(*UpperCamelCase , **UpperCamelCase )
| 242 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_A = logging.get_logger(__name__)
def lowercase_ ( __UpperCAmelCase ) -> List[List[ImageInput]]:
if isinstance(__UpperCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__UpperCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__UpperCAmelCase ):
return [[videos]]
raise ValueError(f"""Could not make batched video from {videos}""" )
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Optional[int] = ["pixel_values"]
def __init__( self : Dict , UpperCamelCase : bool = True , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase : bool = True , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : bool = True , UpperCamelCase : Union[int, float] = 1 / 2_55 , UpperCamelCase : bool = True , UpperCamelCase : bool = True , UpperCamelCase : Optional[Union[float, List[float]]] = None , UpperCamelCase : Optional[Union[float, List[float]]] = None , **UpperCamelCase : Dict , ) -> None:
"""simple docstring"""
super().__init__(**UpperCamelCase )
lowerCAmelCase__ : int = size if size is not None else {"""shortest_edge""": 2_56}
lowerCAmelCase__ : Union[str, Any] = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
lowerCAmelCase__ : List[str] = get_size_dict(UpperCamelCase , param_name="""crop_size""" )
lowerCAmelCase__ : Union[str, Any] = do_resize
lowerCAmelCase__ : str = size
lowerCAmelCase__ : str = do_center_crop
lowerCAmelCase__ : Tuple = crop_size
lowerCAmelCase__ : Union[str, Any] = resample
lowerCAmelCase__ : Any = do_rescale
lowerCAmelCase__ : Union[str, Any] = rescale_factor
lowerCAmelCase__ : Dict = offset
lowerCAmelCase__ : Optional[int] = do_normalize
lowerCAmelCase__ : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : np.ndarray , UpperCamelCase : Dict[str, int] , UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Optional[Any] , ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
if "shortest_edge" in size:
lowerCAmelCase__ : int = get_resize_output_image_size(UpperCamelCase , size["""shortest_edge"""] , default_to_square=UpperCamelCase )
elif "height" in size and "width" in size:
lowerCAmelCase__ : Union[str, Any] = (size["""height"""], size["""width"""])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : Tuple , UpperCamelCase : np.ndarray , UpperCamelCase : Dict[str, int] , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Optional[int] , ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase__ : int = get_size_dict(UpperCamelCase )
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(UpperCamelCase , size=(size["""height"""], size["""width"""]) , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : Dict , UpperCamelCase : np.ndarray , UpperCamelCase : Union[int, float] , UpperCamelCase : bool = True , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Union[str, Any] , ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = image.astype(np.floataa )
if offset:
lowerCAmelCase__ : Tuple = image - (scale / 2)
return rescale(UpperCamelCase , scale=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : Dict , UpperCamelCase : np.ndarray , UpperCamelCase : Union[float, List[float]] , UpperCamelCase : Union[float, List[float]] , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Optional[int] , ) -> np.ndarray:
"""simple docstring"""
return normalize(UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : ImageInput , UpperCamelCase : bool = None , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : PILImageResampling = None , UpperCamelCase : bool = None , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : bool = None , UpperCamelCase : float = None , UpperCamelCase : bool = None , UpperCamelCase : bool = None , UpperCamelCase : Optional[Union[float, List[float]]] = None , UpperCamelCase : Optional[Union[float, List[float]]] = None , UpperCamelCase : Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.ndarray:
"""simple docstring"""
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.
lowerCAmelCase__ : Optional[Any] = to_numpy_array(UpperCamelCase )
if do_resize:
lowerCAmelCase__ : List[str] = self.resize(image=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase )
if do_center_crop:
lowerCAmelCase__ : List[str] = self.center_crop(UpperCamelCase , size=UpperCamelCase )
if do_rescale:
lowerCAmelCase__ : Optional[int] = self.rescale(image=UpperCamelCase , scale=UpperCamelCase , offset=UpperCamelCase )
if do_normalize:
lowerCAmelCase__ : Tuple = self.normalize(image=UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase )
lowerCAmelCase__ : List[str] = to_channel_dimension_format(UpperCamelCase , UpperCamelCase )
return image
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : ImageInput , UpperCamelCase : bool = None , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : PILImageResampling = None , UpperCamelCase : bool = None , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : bool = None , UpperCamelCase : float = None , UpperCamelCase : bool = None , UpperCamelCase : bool = None , UpperCamelCase : Optional[Union[float, List[float]]] = None , UpperCamelCase : Optional[Union[float, List[float]]] = None , UpperCamelCase : Optional[Union[str, TensorType]] = None , UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase : Dict , ) -> PIL.Image.Image:
"""simple docstring"""
lowerCAmelCase__ : List[str] = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ : Union[str, Any] = resample if resample is not None else self.resample
lowerCAmelCase__ : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ : Dict = offset if offset is not None else self.offset
lowerCAmelCase__ : str = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ : List[Any] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ : Union[str, Any] = image_std if image_std is not None else self.image_std
lowerCAmelCase__ : List[Any] = size if size is not None else self.size
lowerCAmelCase__ : Tuple = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
lowerCAmelCase__ : Optional[int] = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ : Optional[Any] = get_size_dict(UpperCamelCase , param_name="""crop_size""" )
if not valid_images(UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowerCAmelCase__ : int = make_batched(UpperCamelCase )
lowerCAmelCase__ : str = [
[
self._preprocess_image(
image=UpperCamelCase , do_resize=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase , do_center_crop=UpperCamelCase , crop_size=UpperCamelCase , do_rescale=UpperCamelCase , rescale_factor=UpperCamelCase , offset=UpperCamelCase , do_normalize=UpperCamelCase , image_mean=UpperCamelCase , image_std=UpperCamelCase , data_format=UpperCamelCase , )
for img in video
]
for video in videos
]
lowerCAmelCase__ : Dict = {"""pixel_values""": videos}
return BatchFeature(data=UpperCamelCase , tensor_type=UpperCamelCase )
| 242 | 1 |
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed to the function""" )
lowercase__ = """"""
while len(__magic_name__ ) % 3 != 0:
lowercase__ = """0""" + bin_string
lowercase__ = [
bin_string[index : index + 3]
for index in range(len(__magic_name__ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
lowercase__ = 0
for index, val in enumerate(__magic_name__ ):
oct_val += int(2 ** (2 - index) * int(__magic_name__ ) )
oct_string += str(__magic_name__ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 359 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Tuple = {
'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 A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''convbert'''
def __init__(self : str , _UpperCAmelCase : Union[str, Any]=3_0522 , _UpperCAmelCase : Dict=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : str=3072 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : str=512 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Tuple=1E-1_2 , _UpperCAmelCase : Dict=1 , _UpperCAmelCase : Optional[Any]=0 , _UpperCAmelCase : List[str]=2 , _UpperCAmelCase : str=768 , _UpperCAmelCase : List[Any]=2 , _UpperCAmelCase : Optional[Any]=9 , _UpperCAmelCase : List[Any]=1 , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : List[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase , )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = embedding_size
lowercase__ = head_ratio
lowercase__ = conv_kernel_size
lowercase__ = num_groups
lowercase__ = classifier_dropout
class A ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def lowerCamelCase__ (self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase__ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 146 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase_( lowerCamelCase_ ) -> float:
return np.dot(lowerCamelCase_ , lowerCamelCase_ )
class _lowerCamelCase:
def __init__( self, *,
lowerCamelCase = np.inf, lowerCamelCase = "linear", lowerCamelCase = 0.0, ) -> None:
"""simple docstring"""
_lowercase : str = regularization
_lowercase : List[str] = gamma
if kernel == "linear":
_lowercase : int = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('rbf kernel requires gamma')
if not isinstance(self.gamma, (float, int)):
raise ValueError('gamma must be float or int')
if not self.gamma > 0:
raise ValueError('gamma must be > 0')
_lowercase : Tuple = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
_lowercase : Dict = F'''Unknown kernel: {kernel}'''
raise ValueError(lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> float:
"""simple docstring"""
return np.dot(lowerCamelCase, lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> float:
"""simple docstring"""
return np.exp(-(self.gamma * norm_squared(vectora - vectora)))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> None:
"""simple docstring"""
_lowercase : int = observations
_lowercase : Optional[Any] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((_lowercase) , ) : Tuple = np.shape(lowerCamelCase)
def to_minimize(lowerCamelCase) -> float:
_lowercase : str = 0
((_lowercase) , ) : Any = np.shape(lowerCamelCase)
for i in range(lowerCamelCase):
for j in range(lowerCamelCase):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i], observations[j])
)
return 1 / 2 * s - sum(lowerCamelCase)
_lowercase : Tuple = LinearConstraint(lowerCamelCase, 0, 0)
_lowercase : Dict = Bounds(0, self.regularization)
_lowercase : Optional[int] = minimize(
lowerCamelCase, np.ones(lowerCamelCase), bounds=lowerCamelCase, constraints=[ly_contraint]).x
_lowercase : Optional[Any] = l_star
# calculating mean offset of separation plane to points
_lowercase : Optional[Any] = 0
for i in range(lowerCamelCase):
for j in range(lowerCamelCase):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i], observations[j])
_lowercase : str = s / n
def UpperCamelCase ( self, lowerCamelCase) -> int:
"""simple docstring"""
_lowercase : Any = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n], lowerCamelCase)
for n in range(len(self.classes)))
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
_a = 2
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , *, # begin keyword-only arguments
UpperCAmelCase="<s>" , UpperCAmelCase="<pad>" , UpperCAmelCase="</s>" , UpperCAmelCase="<unk>" , UpperCAmelCase=None , ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = bos, unk, pad, eos
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = {}
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = self.add_symbol(UpperCAmelCase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(UpperCAmelCase )
_UpperCAmelCase = len(self.symbols )
def __eq__( self , UpperCAmelCase ):
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self , UpperCAmelCase ):
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self ):
"""simple docstring"""
return len(self.symbols )
def __contains__( self , UpperCAmelCase ):
"""simple docstring"""
return sym in self.indices
@classmethod
def UpperCamelCase ( cls , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = cls()
d.add_from_file(UpperCAmelCase )
return d
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=1 , UpperCAmelCase=False ):
"""simple docstring"""
if word in self.indices and not overwrite:
_UpperCAmelCase = self.indices[word]
_UpperCAmelCase = self.count[idx] + n
return idx
else:
_UpperCAmelCase = len(self.symbols )
_UpperCAmelCase = idx
self.symbols.append(UpperCAmelCase )
self.count.append(UpperCAmelCase )
return idx
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
return 0
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
if isinstance(UpperCAmelCase , UpperCAmelCase ):
try:
with open(UpperCAmelCase , 'r' , encoding='utf-8' ) as fd:
self.add_from_file(UpperCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(UpperCAmelCase ) )
return
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = self._load_meta(UpperCAmelCase )
for line in lines[indices_start_line:]:
try:
_UpperCAmelCase , _UpperCAmelCase = line.rstrip().rsplit(' ' , 1 )
if field == "#fairseq:overwrite":
_UpperCAmelCase = True
_UpperCAmelCase , _UpperCAmelCase = line.rsplit(' ' , 1 )
else:
_UpperCAmelCase = False
_UpperCAmelCase = int(UpperCAmelCase )
_UpperCAmelCase = line
if word in self and not overwrite:
raise RuntimeError(
'Duplicate word found when loading Dictionary: \'{}\'. '
'Duplicate words can overwrite earlier ones by adding the '
'#fairseq:overwrite flag at the end of the corresponding row '
'in the dictionary file. If using the Camembert model, please '
'download an updated copy of the model file.'.format(UpperCAmelCase ) )
self.add_symbol(UpperCAmelCase , n=UpperCAmelCase , overwrite=UpperCAmelCase )
except ValueError:
raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'' )
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
_UpperCAmelCase = dict((re.sub(R'@@$' , '' , __lowerCAmelCase ), v) if k.endswith('@@' ) else (re.sub(R'$' , '</w>' , __lowerCAmelCase ), v) for k, v in d.items() )
_UpperCAmelCase = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
_UpperCAmelCase = d[k] # restore
return da
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> str:
"""simple docstring"""
if not os.path.exists(__lowerCAmelCase ):
raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""" )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'checkpoint.pt' )
if not os.path.isfile(__lowerCAmelCase ):
raise ValueError(F"""path to the file {checkpoint_file} does not exist!""" )
_UpperCAmelCase = torch.load(__lowerCAmelCase , map_location='cpu' )
_UpperCAmelCase = chkpt['cfg']['model']
# dicts
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'dict.txt' )
if not os.path.isfile(__lowerCAmelCase ):
raise ValueError(F"""path to the file {dict_file} does not exist!""" )
_UpperCAmelCase = Dictionary.load(__lowerCAmelCase )
_UpperCAmelCase = rewrite_dict_keys(src_dict.indices )
_UpperCAmelCase = len(__lowerCAmelCase )
_UpperCAmelCase = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES['vocab_file'] )
print(F"""Generating {src_vocab_file} of {src_vocab_size} records""" )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'bpecodes' )
if not os.path.isfile(__lowerCAmelCase ):
raise ValueError(F"""path to the file {bpecodes_file} does not exist!""" )
_UpperCAmelCase = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES['merges_file'] )
shutil.copyfile(__lowerCAmelCase , __lowerCAmelCase )
# model config
_UpperCAmelCase = os.path.join(__lowerCAmelCase , 'config.json' )
_UpperCAmelCase = {
'activation_dropout': args['activation_dropout'],
'architectures': ['BioGptForCausalLM'],
'attention_probs_dropout_prob': args['attention_dropout'],
'bos_token_id': 0,
'eos_token_id': 2,
'hidden_act': args['activation_fn'],
'hidden_dropout_prob': args['dropout'],
'hidden_size': args['decoder_embed_dim'],
'initializer_range': 0.02,
'intermediate_size': args['decoder_ffn_embed_dim'],
'layer_norm_eps': 1E-12,
'layerdrop': args['decoder_layerdrop'],
'max_position_embeddings': args['max_target_positions'],
'model_type': 'biogpt',
'num_attention_heads': args['decoder_attention_heads'],
'num_hidden_layers': args['decoder_layers'],
'pad_token_id': 1,
'scale_embedding': not args['no_scale_embedding'],
'tie_word_embeddings': args['share_decoder_input_output_embed'],
'vocab_size': src_vocab_size,
}
# good hparam defaults to start with
print(F"""Generating {biogpt_model_config_file}""" )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
_UpperCAmelCase = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = {
'bos_token': '<s>',
'eos_token': '</s>',
'model_max_length': 1_024,
'pad_token': '<pad>',
'special_tokens_map_file': None,
'tokenizer_class': 'BioGptTokenizer',
'unk_token': '<unk>',
}
print(F"""Generating {biogpt_tokenizer_config_file}""" )
with open(__lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
_UpperCAmelCase = chkpt['model']
# remove unneeded keys
_UpperCAmelCase = [
'decoder.version',
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('output_projection.weight' ):
_UpperCAmelCase = model_state_dict.pop(__lowerCAmelCase )
else:
_UpperCAmelCase = model_state_dict.pop(__lowerCAmelCase )
_UpperCAmelCase = BioGptConfig.from_pretrained(__lowerCAmelCase )
_UpperCAmelCase = BioGptForCausalLM(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase )
# save
_UpperCAmelCase = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print('Conversion is done!' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--biogpt_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
''' bpecodes, etc.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_a = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 39 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = ""
snake_case = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
snake_case = None # compression type in fsspec. ex: "gzip"
snake_case = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self , _SCREAMING_SNAKE_CASE = "" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
super().__init__(self , **_SCREAMING_SNAKE_CASE )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
A_ : List[Any] = fsspec.open(
_SCREAMING_SNAKE_CASE , mode='''rb''' , protocol=_SCREAMING_SNAKE_CASE , compression=self.compression , client_kwargs={
'''requote_redirect_url''': False, # see https://github.com/huggingface/datasets/pull/5459
'''trust_env''': True, # Enable reading proxy env variables.
**(target_options or {}).pop('''client_kwargs''' , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
A_ : Tuple = os.path.basename(self.file.path.split('''::''' )[0] )
A_ : List[str] = (
self.compressed_name[: self.compressed_name.rindex('''.''' )]
if '''.''' in self.compressed_name
else self.compressed_name
)
A_ : List[str] = None
@classmethod
def _snake_case ( cls , _SCREAMING_SNAKE_CASE )->Tuple:
'''simple docstring'''
return super()._strip_protocol(_SCREAMING_SNAKE_CASE ).lstrip('''/''' )
def _snake_case ( self )->Optional[Any]:
'''simple docstring'''
if self.dir_cache is None:
A_ : str = {**self.file.fs.info(self.file.path ), '''name''': self.uncompressed_name}
A_ : Any = {f['''name''']: f}
def _snake_case ( self , _SCREAMING_SNAKE_CASE )->Tuple:
'''simple docstring'''
return self.file.open().read()
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "rb" , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , )->List[Any]:
'''simple docstring'''
A_ : Any = self._strip_protocol(_SCREAMING_SNAKE_CASE )
if mode != "rb":
raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''' )
return self.file.open()
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "bz2"
snake_case = "bz2"
snake_case = ".bz2"
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "gzip"
snake_case = "gzip"
snake_case = ".gz"
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "lz4"
snake_case = "lz4"
snake_case = ".lz4"
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "xz"
snake_case = "xz"
snake_case = ".xz"
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = "zstd"
snake_case = "zstd"
snake_case = ".zst"
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "rb" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE , **_SCREAMING_SNAKE_CASE , )->str:
'''simple docstring'''
super().__init__(
fo=_SCREAMING_SNAKE_CASE , mode=_SCREAMING_SNAKE_CASE , target_protocol=_SCREAMING_SNAKE_CASE , target_options=_SCREAMING_SNAKE_CASE , block_size=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
A_ : Tuple = self.file.__enter__
class _lowerCamelCase :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
A_ : Any = file_
def __enter__( self )->Any:
'''simple docstring'''
self._file.__enter__()
return self
def __exit__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->List[str]:
'''simple docstring'''
self._file.__exit__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __iter__( self )->Any:
'''simple docstring'''
return iter(self._file )
def _snake_case ( self )->Optional[Any]:
'''simple docstring'''
return next(self._file )
def __getattr__( self , _SCREAMING_SNAKE_CASE )->Any:
'''simple docstring'''
return getattr(self._file , _SCREAMING_SNAKE_CASE )
def fixed_enter(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return WrappedFile(_enter(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) )
A_ : Optional[Any] = fixed_enter
| 65 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->None:
'''simple docstring'''
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , _SCREAMING_SNAKE_CASE , )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 65 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class snake_case :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=3 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=2_24 , __UpperCAmelCase=10_00 , __UpperCAmelCase=[3, 3, 6, 4] , __UpperCAmelCase=[48, 56, 1_12, 2_20] , ) ->str:
a_ = parent
a_ = batch_size
a_ = num_channels
a_ = is_training
a_ = use_labels
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = num_labels
a_ = image_size
a_ = layer_depths
a_ = embed_dims
def UpperCAmelCase__ ( self) ->Dict:
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a_ = None
if self.use_labels:
a_ = ids_tensor([self.batch_size] , self.num_labels)
a_ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ ( self) ->Any:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="gelu" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=__UpperCAmelCase , layer_scale_init_value=1E-5 , )
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Optional[int]:
a_ = SwiftFormerModel(config=__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(__UpperCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Union[str, Any]:
a_ = self.num_labels
a_ = SwiftFormerForImageClassification(__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(__UpperCAmelCase , labels=__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
a_ = SwiftFormerForImageClassification(__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a_ = model(__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def UpperCAmelCase__ ( self) ->Tuple:
((a_) , (a_) , (a_)) = self.prepare_config_and_inputs()
a_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
a_ : int = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
a_ : List[Any] = (
{"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
a_ : Tuple = False
a_ : Optional[Any] = False
a_ : List[str] = False
a_ : Any = False
a_ : Optional[int] = False
def UpperCAmelCase__ ( self) ->Optional[int]:
a_ = SwiftFormerModelTester(self)
a_ = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def UpperCAmelCase__ ( self) ->Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason="SwiftFormer does not use inputs_embeds")
def UpperCAmelCase__ ( self) ->Optional[int]:
pass
def UpperCAmelCase__ ( self) ->Union[str, Any]:
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(__UpperCAmelCase)
a_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear))
def UpperCAmelCase__ ( self) ->Optional[Any]:
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(__UpperCAmelCase)
a_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a_ = [*signature.parameters.keys()]
a_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCAmelCase)
def UpperCAmelCase__ ( self) ->List[str]:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Optional[Any]:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase)
@slow
def UpperCAmelCase__ ( self) ->int:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = SwiftFormerModel.from_pretrained(__UpperCAmelCase)
self.assertIsNotNone(__UpperCAmelCase)
@unittest.skip(reason="SwiftFormer does not output attentions")
def UpperCAmelCase__ ( self) ->Union[str, Any]:
pass
def UpperCAmelCase__ ( self) ->Any:
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase):
a_ = model_class(__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
with torch.no_grad():
a_ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase))
a_ = outputs.hidden_states
a_ = 8
self.assertEqual(len(__UpperCAmelCase) , __UpperCAmelCase) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(__UpperCAmelCase)):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
]) , )
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Tuple:
def _config_zero_init(__UpperCAmelCase):
a_ = copy.deepcopy(__UpperCAmelCase)
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(__UpperCAmelCase , __UpperCAmelCase , 1E-10)
if isinstance(getattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) , __UpperCAmelCase):
a_ = _config_zero_init(getattr(__UpperCAmelCase , __UpperCAmelCase))
setattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
return configs_no_init
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
a_ = _config_zero_init(__UpperCAmelCase)
for model_class in self.all_model_classes:
a_ = model_class(config=__UpperCAmelCase)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def UpperCAmelCase__ ( self) ->str:
pass
def UpperCamelCase ( ) ->int:
"""simple docstring"""
a_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self) ->Optional[int]:
return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None
@slow
def UpperCAmelCase__ ( self) ->str:
a_ = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(__UpperCAmelCase)
a_ = self.default_image_processor
a_ = prepare_img()
a_ = image_processor(images=__UpperCAmelCase , return_tensors="pt").to(__UpperCAmelCase)
# forward pass
with torch.no_grad():
a_ = model(**__UpperCAmelCase)
# verify the logits
a_ = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , __UpperCAmelCase)
a_ = torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]).to(__UpperCAmelCase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4)) | 243 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(UpperCAmelCase ) == 26
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = [False] * 26
for char in input_str:
if char.islower():
a_ = True
elif char.isupper():
a_ = True
return all(UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def UpperCamelCase ( ) ->None:
"""simple docstring"""
from timeit import timeit
a_ = "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
print(timeit("is_pangram()" , setup=UpperCAmelCase ) )
print(timeit("is_pangram_faster()" , setup=UpperCAmelCase ) )
print(timeit("is_pangram_fastest()" , setup=UpperCAmelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark() | 243 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case__ (A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Any = LayoutLMTokenizer
__lowerCAmelCase :str = LayoutLMTokenizerFast
__lowerCAmelCase :Optional[int] = True
__lowerCAmelCase :str = True
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
super().setUp()
a__ : Any = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
a__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def SCREAMING_SNAKE_CASE__( self , **__lowercase ) -> Optional[int]:
"""simple docstring"""
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> Union[str, Any]:
"""simple docstring"""
a__ : List[Any] = """UNwant\u00E9d,running"""
a__ : str = """unwanted, running"""
return input_text, output_text
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
a__ : int = self.tokenizer_class(self.vocab_file )
a__ : Optional[Any] = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(__lowercase , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , [7, 4, 5, 1_0, 8, 9] )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
pass
| 367 |
from string import ascii_uppercase
_lowercase : str ={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase : Dict =dict(enumerate(ascii_uppercase))
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Any = len(_lowercase)
a__ : Optional[int] = 0
while True:
if x == i:
a__ : Optional[Any] = 0
if len(_lowercase) == len(_lowercase):
break
key += key[i]
i += 1
return key
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Tuple = """"""
a__ : str = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
a__ : List[str] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : int = """"""
a__ : int = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
a__ : Dict = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
a__ : List[Any] = """THE GERMAN ATTACK"""
a__ : List[Any] = """SECRET"""
a__ : Tuple = generate_key(_lowercase , _lowercase)
a__ : str = cipher_text(_lowercase , _lowercase)
print(F'''Encrypted Text = {s}''')
print(F'''Original Text = {original_text(_lowercase , _lowercase)}''')
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 266 | 0 |
"""simple docstring"""
lowerCAmelCase_ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase_ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase_ = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
assert len(str(__lowerCamelCase ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
lowercase__ : Tuple = year // 1_00
lowercase__ : Dict = (5 * (century % 4) + 2) % 7
lowercase__ : List[str] = year % 1_00
lowercase__ : int = centurian % 12
lowercase__ : str = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
lowercase__ : Optional[int] = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
lowercase__ : str = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , lowercase_ ):
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = load_tool("text-classification" )
self.tool.setup()
snake_case_ = load_tool("text-classification" , remote=a__ )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(a__ , "positive" )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(a__ , "positive" )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(a__ , "positive" )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(a__ , "positive" )
| 85 | 0 |
"""simple docstring"""
import copy
import re
class _UpperCAmelCase :
_lowerCAmelCase : int = """hp"""
_lowerCAmelCase : List[Any] = {}
_lowerCAmelCase : Any = None
@classmethod
def _snake_case ( cls : str , lowercase_ : List[str] , lowercase_ : List[Any] ):
snake_case_ : Union[str, Any] = prefix
snake_case_ : int = defaults
cls.build_naming_info()
@staticmethod
def _snake_case ( lowercase_ : Any , lowercase_ : List[str] ):
if len(lowercase_ ) == 0:
return ""
snake_case_ : Union[str, 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(lowercase_ ) + 1 ):
snake_case_ : Optional[Any] = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
snake_case_ : Union[str, Any] = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(lowercase_ : str ):
snake_case_ : Any = ''''''
while integer != 0:
snake_case_ : Optional[int] = chr(ord('''A''' ) + integer % 10 ) + s
integer //= 10
return s
snake_case_ : Optional[Any] = 0
while True:
snake_case_ : Tuple = word + '''#''' + int_to_alphabetic(lowercase_ )
if sword in info["reverse_short_word"]:
continue
else:
snake_case_ : Dict = sword
break
snake_case_ : Dict = short_word
snake_case_ : Dict = word
return short_word
@staticmethod
def _snake_case ( lowercase_ : str , lowercase_ : Optional[Any] ):
snake_case_ : Dict = param_name.split('''_''' )
snake_case_ : Any = [TrialShortNamer.shortname_for_word(lowercase_ , lowercase_ ) 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
snake_case_ : Optional[int] = ['''''', '''_''']
for separator in separators:
snake_case_ : str = separator.join(lowercase_ )
if shortname not in info["reverse_short_param"]:
snake_case_ : Any = shortname
snake_case_ : Tuple = param_name
return shortname
return param_name
@staticmethod
def _snake_case ( lowercase_ : Dict , lowercase_ : Union[str, Any] ):
snake_case_ : Any = TrialShortNamer.shortname_for_key(lowercase_ , lowercase_ )
snake_case_ : Tuple = short_name
snake_case_ : Optional[Any] = param_name
@classmethod
def _snake_case ( cls : Union[str, Any] ):
if cls.NAMING_INFO is not None:
return
snake_case_ : List[Any] = {
'''short_word''': {},
'''reverse_short_word''': {},
'''short_param''': {},
'''reverse_short_param''': {},
}
snake_case_ : List[Any] = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(lowercase_ , lowercase_ )
snake_case_ : List[str] = info
@classmethod
def _snake_case ( cls : str , lowercase_ : Optional[int] ):
cls.build_naming_info()
assert cls.PREFIX is not None
snake_case_ : Dict = [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
snake_case_ : Optional[Any] = cls.NAMING_INFO['''short_param'''][k]
if isinstance(lowercase_ , lowercase_ ):
snake_case_ : Any = 1 if v else 0
snake_case_ : Any = '''''' if isinstance(lowercase_ , (int, float) ) else '''-'''
snake_case_ : int = f"{key}{sep}{v}"
name.append(lowercase_ )
return "_".join(lowercase_ )
@classmethod
def _snake_case ( cls : Optional[int] , lowercase_ : str ):
snake_case_ : Union[str, Any] = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
snake_case_ : Union[str, Any] = []
else:
snake_case_ : Optional[int] = repr.split('''_''' )
snake_case_ : str = {}
for value in values:
if "-" in value:
snake_case_, snake_case_ : List[str] = value.split('''-''' )
else:
snake_case_ : Dict = re.sub('''[0-9.]''' , '''''' , lowercase_ )
snake_case_ : List[Any] = float(re.sub('''[^0-9.]''' , '''''' , lowercase_ ) )
snake_case_ : Optional[Any] = cls.NAMING_INFO['''reverse_short_param'''][p_k]
snake_case_ : str = p_v
for k in cls.DEFAULTS:
if k not in parameters:
snake_case_ : str = cls.DEFAULTS[k]
return parameters
| 155 |
"""simple docstring"""
from math import factorial
lowercase__ : Optional[int] = {str(d): factorial(d) for d in range(10)}
def __lowercase ( _a ):
return sum(DIGIT_FACTORIAL[d] for d in str(_a ) )
def __lowercase ( ):
snake_case_ : Optional[int] = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , _a ) if sum_of_digit_factorial(_a ) == i )
if __name__ == "__main__":
print(f'{solution() = }')
| 155 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case__ = 2_0_0_0_0_0_0 ) -> Optional[int]:
lowerCAmelCase = [0]
lowerCAmelCase = 4_2
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
lowerCAmelCase = 0
# the area corresponding to the grid that gives the product closest to target
lowerCAmelCase = 0
# an estimate of b, using the quadratic formula
lowerCAmelCase = 4_2
# the largest integer less than b_estimate
lowerCAmelCase = 4_2
# the largest integer less than b_estimate
lowerCAmelCase = 4_2
# the triangle number corresponding to b_floor
lowerCAmelCase = 4_2
# the triangle number corresponding to b_ceil
lowerCAmelCase = 4_2
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
lowerCAmelCase = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
lowerCAmelCase = floor(__A )
lowerCAmelCase = ceil(__A )
lowerCAmelCase = triangle_numbers[b_floor]
lowerCAmelCase = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
lowerCAmelCase = triangle_b_first_guess * triangle_a
lowerCAmelCase = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
lowerCAmelCase = triangle_b_second_guess * triangle_a
lowerCAmelCase = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f'{solution() = }')
| 338 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = 'glpn'
def __init__(self , A=3 , A=4 , A=[2, 2, 2, 2] , A=[8, 4, 2, 1] , A=[32, 64, 160, 256] , A=[7, 3, 3, 3] , A=[4, 2, 2, 2] , A=[1, 2, 5, 8] , A=[4, 4, 4, 4] , A="gelu" , A=0.0 , A=0.0 , A=0.02 , A=0.1 , A=1E-6 , A=64 , A=10 , A=-1 , **A , ) -> Any:
"""simple docstring"""
super().__init__(**A )
_a = num_channels
_a = num_encoder_blocks
_a = depths
_a = sr_ratios
_a = hidden_sizes
_a = patch_sizes
_a = strides
_a = mlp_ratios
_a = num_attention_heads
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = initializer_range
_a = drop_path_rate
_a = layer_norm_eps
_a = decoder_hidden_size
_a = max_depth
_a = head_in_index
| 211 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MraForMaskedLM',
'MraForMultipleChoice',
'MraForQuestionAnswering',
'MraForSequenceClassification',
'MraForTokenClassification',
'MraLayer',
'MraModel',
'MraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_UpperCamelCase : Optional[str] = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
_UpperCamelCase : Optional[int] = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Source language id for translation.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Target language id for translation.'''} )
_UpperCamelCase : Optional[int] = field(default=lowerCAmelCase_ , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
_UpperCamelCase : bool = field(
default=lowerCAmelCase_ , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
logger.info(F'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(F''' {key} = {metrics[key]}''' )
save_json(UpperCamelCase_ , os.path.join(UpperCamelCase_ , F'''{split}_results.json''' ) )
def lowerCAmelCase_ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
check_output_dir(UpperCamelCase_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , UpperCamelCase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
assert hasattr(UpperCamelCase_ , UpperCamelCase_ ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(UpperCamelCase_ , UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
UpperCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(UpperCamelCase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
UpperCamelCase_ = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
UpperCamelCase_ = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(UpperCamelCase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
UpperCamelCase_ = SeqaSeqDataset
# Get datasets
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
UpperCamelCase_ = (
build_compute_metrics_fn(data_args.task , UpperCamelCase_ ) if training_args.predict_with_generate else None
)
UpperCamelCase_ = SeqaSeqTrainer(
model=UpperCamelCase_ , args=UpperCamelCase_ , data_args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , data_collator=SeqaSeqDataCollator(
UpperCamelCase_ , UpperCamelCase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=UpperCamelCase_ , tokenizer=UpperCamelCase_ , )
UpperCamelCase_ = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
UpperCamelCase_ = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
UpperCamelCase_ = train_result.metrics
UpperCamelCase_ = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCamelCase_ = trainer.evaluate(metric_key_prefix="val" )
UpperCamelCase_ = data_args.n_val
UpperCamelCase_ = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.do_predict:
logger.info("*** Predict ***" )
UpperCamelCase_ = trainer.predict(test_dataset=UpperCamelCase_ , metric_key_prefix="test" )
UpperCamelCase_ = test_output.metrics
UpperCamelCase_ = data_args.n_test
if trainer.is_world_process_zero():
UpperCamelCase_ = round(metrics["test_loss"] , 4 )
handle_metrics("test" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.predict_with_generate:
UpperCamelCase_ = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ )
UpperCamelCase_ = lmap(str.strip , UpperCamelCase_ )
write_txt_file(UpperCamelCase_ , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(UpperCamelCase_ , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 328 | 0 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.getLogger(__name__)
def UpperCAmelCase_ ( __lowercase : str ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = git.Repo(search_parent_directories=__lowercase )
_UpperCAmelCase = {
"repo_id": str(__lowercase ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__lowercase , "git_log.json" ) , "w" ) as f:
json.dump(__lowercase , __lowercase , indent=4 )
def UpperCAmelCase_ ( __lowercase : Tuple ) -> Tuple:
'''simple docstring'''
if params.n_gpu <= 0:
_UpperCAmelCase = 0
_UpperCAmelCase = -1
_UpperCAmelCase = True
_UpperCAmelCase = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
_UpperCAmelCase = int(os.environ["WORLD_SIZE"] )
_UpperCAmelCase = int(os.environ["N_GPU_NODE"] )
_UpperCAmelCase = int(os.environ["RANK"] )
# number of nodes / node ID
_UpperCAmelCase = params.world_size // params.n_gpu_per_node
_UpperCAmelCase = params.global_rank // params.n_gpu_per_node
_UpperCAmelCase = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
_UpperCAmelCase = 1
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 1
_UpperCAmelCase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
_UpperCAmelCase = params.node_id == 0 and params.local_rank == 0
_UpperCAmelCase = params.n_nodes > 1
# summary
_UpperCAmelCase = f'--- Global rank: {params.global_rank} - '
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" , backend="nccl" , )
def UpperCAmelCase_ ( __lowercase : Union[str, Any] ) -> str:
'''simple docstring'''
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 22 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowerCAmelCase__ :
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> Dict:
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
__lowerCamelCase = img
__lowerCamelCase = img.shape[1]
__lowerCamelCase = img.shape[0]
__lowerCamelCase = dst_width
__lowerCamelCase = dst_height
__lowerCamelCase = self.src_w / self.dst_w
__lowerCamelCase = self.src_h / self.dst_h
__lowerCamelCase = __lowerCamelCase = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 2_55
)
def __A ( self : List[Any] ) -> str:
for i in range(self.dst_h ):
for j in range(self.dst_w ):
__lowerCamelCase = self.img[self.get_y(SCREAMING_SNAKE_CASE__ )][self.get_x(SCREAMING_SNAKE_CASE__ )]
def __A ( self : str , SCREAMING_SNAKE_CASE__ : int ) -> int:
return int(self.ratio_x * x )
def __A ( self : str , SCREAMING_SNAKE_CASE__ : int ) -> int:
return int(self.ratio_y * y )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = 800, 600
SCREAMING_SNAKE_CASE__ : int = imread("image_data/lena.jpg", 1)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
)
waitKey(0)
destroyAllWindows()
| 270 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : list , __lowerCAmelCase : int , __lowerCAmelCase : int = 0 , __lowerCAmelCase : int = 0 ) -> int:
snake_case = right or len(__lowerCAmelCase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(__lowerCAmelCase , __lowerCAmelCase , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
snake_case_ = "WhisperFeatureExtractor"
snake_case_ = "WhisperTokenizer"
def __init__( self : Dict , __snake_case : Any , __snake_case : int )-> List[Any]:
super().__init__(__snake_case , __snake_case )
snake_case = self.feature_extractor
snake_case = False
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : str=None , __snake_case : List[str]=None , __snake_case : int=True )-> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=__snake_case , language=__snake_case , no_timestamps=__snake_case )
def __call__( self : str , *__snake_case : Tuple , **__snake_case : Union[str, Any] )-> Any:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__snake_case , **__snake_case )
snake_case = kwargs.pop("""audio""" , __snake_case )
snake_case = kwargs.pop("""sampling_rate""" , __snake_case )
snake_case = kwargs.pop("""text""" , __snake_case )
if len(__snake_case ) > 0:
snake_case = args[0]
snake_case = 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:
snake_case = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case )
if text is not None:
snake_case = self.tokenizer(__snake_case , **__snake_case )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings["""input_ids"""]
return inputs
def lowerCAmelCase ( self : Union[str, Any] , *__snake_case : Union[str, Any] , **__snake_case : str )-> Optional[Any]:
return self.tokenizer.batch_decode(*__snake_case , **__snake_case )
def lowerCAmelCase ( self : Optional[int] , *__snake_case : Any , **__snake_case : Union[str, Any] )-> List[str]:
return self.tokenizer.decode(*__snake_case , **__snake_case )
def lowerCAmelCase ( self : Any , __snake_case : str , __snake_case : Dict="np" )-> Any:
return self.tokenizer.get_prompt_ids(__snake_case , return_tensors=__snake_case )
| 3 | 0 |
def _lowerCAmelCase (_lowerCAmelCase = 1_00):
UpperCamelCase_ = 0
UpperCamelCase_ = 0
for i in range(1 , n + 1):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 128 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
UpperCAmelCase : int ="""Hello, World!"""
UpperCAmelCase : int ="""en_XX"""
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
UpperCamelCase_ = Path("data_bin")
UpperCamelCase_ = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowerCAmelCase).parent) , checkpoint_file=Path(_lowerCAmelCase).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(_lowerCAmelCase) , bpe="sentencepiece" , sentencepiece_model=str(Path(_lowerCAmelCase).parent / "sentencepiece.bpe.model") , src_dict=str(data_dir / "dict.txt") , )
xmod.eval() # disable dropout
print(_lowerCAmelCase)
UpperCamelCase_ = xmod.model.encoder.sentence_encoder
UpperCamelCase_ = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , "bottleneck" , 2) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCamelCase_ = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" , _lowerCAmelCase)
UpperCamelCase_ = XmodForSequenceClassification(_lowerCAmelCase) if classification_head else XmodForMaskedLM(_lowerCAmelCase)
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCamelCase_ = xmod_sent_encoder.embed_tokens.weight
UpperCamelCase_ = xmod_sent_encoder.embed_positions.weight
UpperCamelCase_ = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight) # just zero them out b/c xmod doesn't use them.
UpperCamelCase_ = xmod_sent_encoder.layernorm_embedding.weight
UpperCamelCase_ = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
UpperCamelCase_ = model.roberta.encoder.layer[i]
UpperCamelCase_ = xmod_sent_encoder.layers[i]
# self attention
UpperCamelCase_ = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
):
raise AssertionError("Dimensions of self-attention weights do not match.")
UpperCamelCase_ = xmod_layer.self_attn.q_proj.weight
UpperCamelCase_ = xmod_layer.self_attn.q_proj.bias
UpperCamelCase_ = xmod_layer.self_attn.k_proj.weight
UpperCamelCase_ = xmod_layer.self_attn.k_proj.bias
UpperCamelCase_ = xmod_layer.self_attn.v_proj.weight
UpperCamelCase_ = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCamelCase_ = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match.")
UpperCamelCase_ = xmod_layer.self_attn.out_proj.weight
UpperCamelCase_ = xmod_layer.self_attn.out_proj.bias
UpperCamelCase_ = xmod_layer.self_attn_layer_norm.weight
UpperCamelCase_ = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCamelCase_ = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match.")
UpperCamelCase_ = xmod_layer.fca.weight
UpperCamelCase_ = xmod_layer.fca.bias
# output
UpperCamelCase_ = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match.")
UpperCamelCase_ = xmod_layer.fca.weight
UpperCamelCase_ = xmod_layer.fca.bias
UpperCamelCase_ = xmod_layer.final_layer_norm.weight
UpperCamelCase_ = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCamelCase_ = xmod_layer.adapter_layer_norm.weight
UpperCamelCase_ = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
raise AssertionError("Lists of language adapters do not match.")
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCamelCase_ = bert_output.adapter_modules[lang_code]
UpperCamelCase_ = xmod_layer.adapter_modules[lang_code]
UpperCamelCase_ = from_adapter.fca.weight
UpperCamelCase_ = from_adapter.fca.bias
UpperCamelCase_ = from_adapter.fca.weight
UpperCamelCase_ = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCamelCase_ = xmod_sent_encoder.layer_norm.weight
UpperCamelCase_ = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCamelCase_ = xmod.model.classification_heads["mnli"].dense.weight
UpperCamelCase_ = xmod.model.classification_heads["mnli"].dense.bias
UpperCamelCase_ = xmod.model.classification_heads["mnli"].out_proj.weight
UpperCamelCase_ = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
UpperCamelCase_ = xmod.model.encoder.lm_head.dense.weight
UpperCamelCase_ = xmod.model.encoder.lm_head.dense.bias
UpperCamelCase_ = xmod.model.encoder.lm_head.layer_norm.weight
UpperCamelCase_ = xmod.model.encoder.lm_head.layer_norm.bias
UpperCamelCase_ = xmod.model.encoder.lm_head.weight
UpperCamelCase_ = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCamelCase_ = xmod.encode(_lowerCAmelCase).unsqueeze(0) # batch of size 1
model.roberta.set_default_language(_lowerCAmelCase)
UpperCamelCase_ = model(_lowerCAmelCase)[0]
if classification_head:
UpperCamelCase_ = xmod.model.classification_heads["mnli"](xmod.extract_features(_lowerCAmelCase))
else:
UpperCamelCase_ = xmod.model(_lowerCAmelCase , lang_id=[SAMPLE_LANGUAGE])[0]
print(our_output.shape , their_output.shape)
UpperCamelCase_ = torch.max(torch.abs(our_output - their_output)).item()
print(f"""max_absolute_diff = {max_absolute_diff}""") # ~ 1e-7
UpperCamelCase_ = torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3)
print("Do both models output the same tensors?" , "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
Path(_lowerCAmelCase).mkdir(parents=_lowerCAmelCase , exist_ok=_lowerCAmelCase)
print(f"""Saving model to {pytorch_dump_folder_path}""")
model.save_pretrained(_lowerCAmelCase)
if __name__ == "__main__":
UpperCAmelCase : Optional[Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
UpperCAmelCase : Tuple =parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 128 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = ["ConditionalDetrFeatureExtractor"]
_lowerCamelCase : Optional[Any] = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Dict = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 360 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _UpperCAmelCase (UpperCamelCase_ : str , UpperCamelCase_ : Tuple=None ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = None
if token is not None:
_lowerCAmelCase : str = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
_lowerCAmelCase : Tuple = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
_lowerCAmelCase : str = requests.get(UpperCamelCase_ , headers=UpperCamelCase_ ).json()
_lowerCAmelCase : Optional[Any] = {}
try:
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
_lowerCAmelCase : List[str] = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(UpperCamelCase_ ):
_lowerCAmelCase : int = requests.get(url + F"&page={i + 2}" , headers=UpperCamelCase_ ).json()
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return job_links
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
def _UpperCAmelCase (UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[Any]=None ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = None
if token is not None:
_lowerCAmelCase : Optional[Any] = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
_lowerCAmelCase : Optional[int] = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
_lowerCAmelCase : Optional[int] = requests.get(UpperCamelCase_ , headers=UpperCamelCase_ ).json()
_lowerCAmelCase : List[str] = {}
try:
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
_lowerCAmelCase : List[str] = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(UpperCamelCase_ ):
_lowerCAmelCase : List[str] = requests.get(url + F"&page={i + 2}" , headers=UpperCamelCase_ ).json()
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
return artifacts
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
def _UpperCAmelCase (UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Any ):
'''simple docstring'''
_lowerCAmelCase : str = None
if token is not None:
_lowerCAmelCase : Any = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
_lowerCAmelCase : List[str] = requests.get(UpperCamelCase_ , headers=UpperCamelCase_ , allow_redirects=UpperCamelCase_ )
_lowerCAmelCase : List[str] = result.headers["""Location"""]
_lowerCAmelCase : List[Any] = requests.get(UpperCamelCase_ , allow_redirects=UpperCamelCase_ )
_lowerCAmelCase : int = os.path.join(UpperCamelCase_ , F"{artifact_name}.zip" )
with open(UpperCamelCase_ , """wb""" ) as fp:
fp.write(response.content )
def _UpperCAmelCase (UpperCamelCase_ : int , UpperCamelCase_ : Optional[int]=None ):
'''simple docstring'''
_lowerCAmelCase : Dict = []
_lowerCAmelCase : Any = []
_lowerCAmelCase : Union[str, Any] = None
with zipfile.ZipFile(UpperCamelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(UpperCamelCase_ ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(UpperCamelCase_ ) as f:
for line in f:
_lowerCAmelCase : List[str] = line.decode("""UTF-8""" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
_lowerCAmelCase : Union[str, Any] = line[: line.index(""": """ )]
_lowerCAmelCase : Union[str, Any] = line[line.index(""": """ ) + len(""": """ ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("""FAILED """ ):
# `test` is the test method that failed
_lowerCAmelCase : Tuple = line[len("""FAILED """ ) :]
failed_tests.append(UpperCamelCase_ )
elif filename == "job_name.txt":
_lowerCAmelCase : str = line
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError(
F"`errors` and `failed_tests` should have the same number of elements. Got {len(UpperCamelCase_ )} for `errors` "
F"and {len(UpperCamelCase_ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
""" problem.""" )
_lowerCAmelCase : int = None
if job_name and job_links:
_lowerCAmelCase : Optional[int] = job_links.get(UpperCamelCase_ , UpperCamelCase_ )
# A list with elements of the form (line of error, error, failed test)
_lowerCAmelCase : Tuple = [x + [y] + [job_link] for x, y in zip(UpperCamelCase_ , UpperCamelCase_ )]
return result
def _UpperCAmelCase (UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : List[Any] = [os.path.join(UpperCamelCase_ , UpperCamelCase_ ) for p in os.listdir(UpperCamelCase_ ) if p.endswith(""".zip""" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(UpperCamelCase_ , job_links=UpperCamelCase_ ) )
return errors
def _UpperCAmelCase (UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Dict=None ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = Counter()
counter.update([x[1] for x in logs] )
_lowerCAmelCase : Dict = counter.most_common()
_lowerCAmelCase : Dict = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
_lowerCAmelCase : Union[str, Any] = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}
_lowerCAmelCase : int = dict(sorted(r.items() , key=lambda UpperCamelCase_ : item[1]["count"] , reverse=UpperCamelCase_ ) )
return r
def _UpperCAmelCase (UpperCamelCase_ : Tuple ):
'''simple docstring'''
_lowerCAmelCase : List[str] = test.split("""::""" )[0]
if test.startswith("""tests/models/""" ):
_lowerCAmelCase : Optional[Any] = test.split("""/""" )[2]
else:
_lowerCAmelCase : Union[str, Any] = None
return test
def _UpperCAmelCase (UpperCamelCase_ : List[str] , UpperCamelCase_ : Dict=None ):
'''simple docstring'''
_lowerCAmelCase : List[str] = [(x[0], x[1], get_model(x[2] )) for x in logs]
_lowerCAmelCase : List[str] = [x for x in logs if x[2] is not None]
_lowerCAmelCase : int = {x[2] for x in logs}
_lowerCAmelCase : str = {}
for test in tests:
_lowerCAmelCase : Union[str, Any] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
_lowerCAmelCase : List[Any] = counter.most_common()
_lowerCAmelCase : Optional[int] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
_lowerCAmelCase : List[str] = sum(error_counts.values() )
if n_errors > 0:
_lowerCAmelCase : int = {"""count""": n_errors, """errors""": error_counts}
_lowerCAmelCase : Dict = dict(sorted(r.items() , key=lambda UpperCamelCase_ : item[1]["count"] , reverse=UpperCamelCase_ ) )
return r
def _UpperCAmelCase (UpperCamelCase_ : Union[str, Any] ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = """| no. | error | status |"""
_lowerCAmelCase : List[Any] = """|-:|:-|:-|"""
_lowerCAmelCase : str = [header, sep]
for error in reduced_by_error:
_lowerCAmelCase : Optional[Any] = reduced_by_error[error]["""count"""]
_lowerCAmelCase : int = F"| {count} | {error[:100]} | |"
lines.append(UpperCamelCase_ )
return "\n".join(UpperCamelCase_ )
def _UpperCAmelCase (UpperCamelCase_ : List[str] ):
'''simple docstring'''
_lowerCAmelCase : str = """| model | no. of errors | major error | count |"""
_lowerCAmelCase : Any = """|-:|-:|-:|-:|"""
_lowerCAmelCase : str = [header, sep]
for model in reduced_by_model:
_lowerCAmelCase : Union[str, Any] = reduced_by_model[model]["""count"""]
_lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = list(reduced_by_model[model]["""errors"""].items() )[0]
_lowerCAmelCase : str = F"| {model} | {count} | {error[:60]} | {_count} |"
lines.append(UpperCamelCase_ )
return "\n".join(UpperCamelCase_ )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
_lowerCamelCase : Tuple = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
_lowerCamelCase : Optional[int] = get_job_links(args.workflow_run_id, token=args.token)
_lowerCamelCase : int = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
_lowerCamelCase : Optional[Any] = k.find(" / ")
_lowerCamelCase : Tuple = k[index + len(" / ") :]
_lowerCamelCase : List[Any] = v
with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
_lowerCamelCase : Union[str, Any] = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
_lowerCamelCase : str = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
_lowerCamelCase : Dict = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
_lowerCamelCase : Union[str, Any] = counter.most_common(3_0)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
_lowerCamelCase : str = reduce_by_error(errors)
_lowerCamelCase : Tuple = reduce_by_model(errors)
_lowerCamelCase : List[str] = make_github_table(reduced_by_error)
_lowerCamelCase : Optional[Any] = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
| 159 | 0 |
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[Any] = len(a__)
for i in range(a__):
for j in range(i + 1 , a__):
if numbers[j] < numbers[i]:
a_ , a_ : int = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__snake_case : Tuple = input("""Enter numbers separated by a comma:\n""").strip()
__snake_case : Optional[int] = [int(item) for item in user_input.split(""",""")]
print(exchange_sort(unsorted))
| 248 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
__snake_case : Optional[int] = """sshleifer/mar_enro_6_3_student"""
class A__(a_ ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Tuple:
super().setUp()
a_ : Union[str, Any] = cached_path(
"""https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" , extract_compressed_file=_lowercase , )
a_ : Union[str, Any] = F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
MarianMTModel.from_pretrained(_lowercase )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> int:
a_ : Any = {
"""$MAX_LEN""": 64,
"""$BS""": 64,
"""$GAS""": 1,
"""$ENRO_DIR""": self.data_dir,
"""facebook/mbart-large-cc25""": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"""--learning_rate=3e-5""": """--learning_rate 3e-4""",
"""--num_train_epochs 6""": """--num_train_epochs 1""",
}
# Clean up bash script
a_ : List[str] = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
a_ : Dict = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
for k, v in env_vars_to_replace.items():
a_ : Optional[int] = bash_script.replace(_lowercase , str(_lowercase ) )
a_ : int = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
a_ : Dict = F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
a_ : Union[str, Any] = ["""finetune.py"""] + bash_script.split() + args
with patch.object(_lowercase , """argv""" , _lowercase ):
a_ : Optional[Any] = argparse.ArgumentParser()
a_ : Tuple = pl.Trainer.add_argparse_args(_lowercase )
a_ : Any = SummarizationModule.add_model_specific_args(_lowercase , os.getcwd() )
a_ : str = parser.parse_args()
a_ : Union[str, Any] = main(_lowercase )
# Check metrics
a_ : Any = load_json(model.metrics_save_path )
a_ : List[Any] = metrics["""val"""][0]
a_ : Union[str, Any] = metrics["""val"""][-1]
self.assertEqual(len(metrics["""val"""] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , _lowercase )
self.assertGreater(last_step_stats["""val_avg_gen_time"""] , 0.0_1 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["""val_avg_gen_time"""] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["""val_avg_bleu"""] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
a_ : Optional[Any] = os.listdir(_lowercase )
a_ : Dict = [x for x in contents if x.endswith(""".ckpt""" )][0]
a_ : str = os.path.join(args.output_dir , _lowercase )
a_ : Any = torch.load(_lowercase , map_location="""cpu""" )
a_ : Union[str, Any] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
a_ : List[Any] = {os.path.basename(_lowercase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
class A__(a_ ):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Optional[Any]:
a_ : Tuple = F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
a_ : str = {
"""--fp16_opt_level=O1""": """""",
"""$MAX_LEN""": 128,
"""$BS""": 16,
"""$GAS""": 1,
"""$ENRO_DIR""": data_dir,
"""$m""": """sshleifer/student_marian_en_ro_6_1""",
"""val_check_interval=0.25""": """val_check_interval=1.0""",
}
# Clean up bash script
a_ : Union[str, Any] = (
(self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
)
a_ : Union[str, Any] = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
a_ : Any = bash_script.replace("""--fp16 """ , """ """ )
for k, v in env_vars_to_replace.items():
a_ : Dict = bash_script.replace(_lowercase , str(_lowercase ) )
a_ : int = self.get_auto_remove_tmp_dir()
a_ : Optional[Any] = bash_script.replace("""--fp16""" , """""" )
a_ : List[str] = 6
a_ : str = (
["""distillation.py"""]
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
"""--gpus=1""",
"""--learning_rate=1e-3""",
F'''--num_train_epochs={epochs}''',
"""--warmup_steps=10""",
"""--val_check_interval=1.0""",
"""--do_predict""",
]
)
with patch.object(_lowercase , """argv""" , _lowercase ):
a_ : int = argparse.ArgumentParser()
a_ : Any = pl.Trainer.add_argparse_args(_lowercase )
a_ : str = SummarizationDistiller.add_model_specific_args(_lowercase , os.getcwd() )
a_ : Any = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
a_ : Dict = distill_main(_lowercase )
# Check metrics
a_ : Any = load_json(model.metrics_save_path )
a_ : int = metrics["""val"""][0]
a_ : Union[str, Any] = metrics["""val"""][-1]
assert len(metrics["""val"""] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.0_1
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , _lowercase )
# check lightning ckpt can be loaded and has a reasonable statedict
a_ : Dict = os.listdir(_lowercase )
a_ : List[Any] = [x for x in contents if x.endswith(""".ckpt""" )][0]
a_ : int = os.path.join(args.output_dir , _lowercase )
a_ : Union[str, Any] = torch.load(_lowercase , map_location="""cpu""" )
a_ : List[str] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
a_ : List[str] = {os.path.basename(_lowercase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
| 248 | 1 |
"""simple docstring"""
class __snake_case : # Public class to implement a graph
def __init__( self , lowercase , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = row
a__: List[str] = col
a__: Optional[int] = graph
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> bool:
'''simple docstring'''
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> None:
'''simple docstring'''
a__: str = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
a__: Tuple = [-1, 0, 1, -1, 1, -1, 0, 1]
a__: Optional[int] = True # Make those cells visited
for k in range(8):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , lowercase):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , lowercase)
def lowerCamelCase_ ( self) -> int: # And finally, count all islands.
'''simple docstring'''
a__: Dict = [[False for j in range(self.COL)] for i in range(self.ROW)]
a__: Union[str, Any] = 0
for i in range(self.ROW):
for j in range(self.COL):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(lowercase , lowercase , lowercase)
count += 1
return count
| 353 | """simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ = re.compile(r'\b(a|an|the)\b', re.UNICODE)
lowercase__ = None
def __a ( ) ->List[Any]:
a__: Dict = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.' )
parser.add_argument('data_file' , metavar='data.json' , help='Input data JSON file.' )
parser.add_argument('pred_file' , metavar='pred.json' , help='Model predictions.' )
parser.add_argument(
'--out-file' , '-o' , metavar='eval.json' , help='Write accuracy metrics to file (default is stdout).' )
parser.add_argument(
'--na-prob-file' , '-n' , metavar='na_prob.json' , help='Model estimates of probability of no answer.' )
parser.add_argument(
'--na-prob-thresh' , '-t' , type=_SCREAMING_SNAKE_CASE , default=1.0 , help='Predict "" if no-answer probability exceeds this (default = 1.0).' , )
parser.add_argument(
'--out-image-dir' , '-p' , metavar='out_images' , default=_SCREAMING_SNAKE_CASE , help='Save precision-recall curves to directory.' )
parser.add_argument('--verbose' , '-v' , action='store_true' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
a__: Optional[Any] = bool(qa['answers']['text'] )
return qid_to_has_ans
def __a ( _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
def remove_articles(_SCREAMING_SNAKE_CASE ):
return ARTICLES_REGEX.sub(' ' , _SCREAMING_SNAKE_CASE )
def white_space_fix(_SCREAMING_SNAKE_CASE ):
return " ".join(text.split() )
def remove_punc(_SCREAMING_SNAKE_CASE ):
a__: Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_SCREAMING_SNAKE_CASE ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE ) ) ) )
def __a ( _SCREAMING_SNAKE_CASE ) ->Optional[int]:
if not s:
return []
return normalize_answer(_SCREAMING_SNAKE_CASE ).split()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[Any]:
return int(normalize_answer(_SCREAMING_SNAKE_CASE ) == normalize_answer(_SCREAMING_SNAKE_CASE ) )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: Any = get_tokens(_SCREAMING_SNAKE_CASE )
a__: Optional[int] = get_tokens(_SCREAMING_SNAKE_CASE )
a__: Optional[int] = collections.Counter(_SCREAMING_SNAKE_CASE ) & collections.Counter(_SCREAMING_SNAKE_CASE )
a__: Tuple = sum(common.values() )
if len(_SCREAMING_SNAKE_CASE ) == 0 or len(_SCREAMING_SNAKE_CASE ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
a__: Any = 1.0 * num_same / len(_SCREAMING_SNAKE_CASE )
a__: Optional[int] = 1.0 * num_same / len(_SCREAMING_SNAKE_CASE )
a__: Dict = (2 * precision * recall) / (precision + recall)
return fa
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
a__: Union[str, Any] = {}
a__: Dict = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
a__: Optional[int] = qa['id']
a__: List[Any] = [t for t in qa['answers']['text'] if normalize_answer(_SCREAMING_SNAKE_CASE )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
a__: str = ['']
if qid not in preds:
print(F'Missing prediction for {qid}' )
continue
a__: Any = preds[qid]
# Take max over all gold answers
a__: List[str] = max(compute_exact(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for a in gold_answers )
a__: Optional[int] = max(compute_fa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for a in gold_answers )
return exact_scores, fa_scores
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
a__: List[str] = {}
for qid, s in scores.items():
a__: List[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
a__: Optional[int] = float(not qid_to_has_ans[qid] )
else:
a__: Optional[Any] = s
return new_scores
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Tuple:
if not qid_list:
a__: str = len(_SCREAMING_SNAKE_CASE )
return collections.OrderedDict(
[
('exact', 100.0 * sum(exact_scores.values() ) / total),
('f1', 100.0 * sum(fa_scores.values() ) / total),
('total', total),
] )
else:
a__: Optional[Any] = len(_SCREAMING_SNAKE_CASE )
return collections.OrderedDict(
[
('exact', 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
('f1', 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
('total', total),
] )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
for k in new_eval:
a__: List[Any] = new_eval[k]
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
plt.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , color='b' , alpha=0.2 , where='post' )
plt.fill_between(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , step='post' , alpha=0.2 , color='b' )
plt.xlabel('Recall' )
plt.ylabel('Precision' )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_SCREAMING_SNAKE_CASE )
plt.savefig(_SCREAMING_SNAKE_CASE )
plt.clf()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) ->List[str]:
a__: Optional[int] = sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : na_probs[k] )
a__: Dict = 0.0
a__: Optional[int] = 1.0
a__: Tuple = 0.0
a__: Tuple = [1.0]
a__: Optional[Any] = [0.0]
a__: Optional[Any] = 0.0
for i, qid in enumerate(_SCREAMING_SNAKE_CASE ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
a__: Optional[Any] = true_pos / float(i + 1 )
a__: int = true_pos / float(_SCREAMING_SNAKE_CASE )
if i == len(_SCREAMING_SNAKE_CASE ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_SCREAMING_SNAKE_CASE )
recalls.append(_SCREAMING_SNAKE_CASE )
if out_image:
plot_pr_curve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return {"ap": 100.0 * avg_prec}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if out_image_dir and not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
a__: Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
a__: Optional[Any] = make_precision_recall_eval(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , out_image=os.path.join(_SCREAMING_SNAKE_CASE , 'pr_exact.png' ) , title='Precision-Recall curve for Exact Match score' , )
a__: List[str] = make_precision_recall_eval(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , out_image=os.path.join(_SCREAMING_SNAKE_CASE , 'pr_f1.png' ) , title='Precision-Recall curve for F1 score' , )
a__: Optional[Any] = {k: float(_SCREAMING_SNAKE_CASE ) for k, v in qid_to_has_ans.items()}
a__: List[Any] = make_precision_recall_eval(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , out_image=os.path.join(_SCREAMING_SNAKE_CASE , 'pr_oracle.png' ) , title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)' , )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'pr_exact' )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'pr_f1' )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'pr_oracle' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
if not qid_list:
return
a__: Any = [na_probs[k] for k in qid_list]
a__: List[str] = np.ones_like(_SCREAMING_SNAKE_CASE ) / float(len(_SCREAMING_SNAKE_CASE ) )
plt.hist(_SCREAMING_SNAKE_CASE , weights=_SCREAMING_SNAKE_CASE , bins=20 , range=(0.0, 1.0) )
plt.xlabel('Model probability of no-answer' )
plt.ylabel('Proportion of dataset' )
plt.title(F'Histogram of no-answer probability: {name}' )
plt.savefig(os.path.join(_SCREAMING_SNAKE_CASE , F'na_prob_hist_{name}.png' ) )
plt.clf()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: str = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
a__: List[Any] = num_no_ans
a__: Union[str, Any] = cur_score
a__: Optional[Any] = 0.0
a__: str = sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : na_probs[k] )
for i, qid in enumerate(_SCREAMING_SNAKE_CASE ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
a__: Tuple = scores[qid]
else:
if preds[qid]:
a__: Optional[Any] = -1
else:
a__: Optional[int] = 0
cur_score += diff
if cur_score > best_score:
a__: Dict = cur_score
a__: Optional[int] = na_probs[qid]
return 100.0 * best_score / len(_SCREAMING_SNAKE_CASE ), best_thresh
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__ , a__: str = find_best_thresh(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__ , a__: Optional[int] = find_best_thresh(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: List[Any] = best_exact
a__: Dict = exact_thresh
a__: Optional[int] = best_fa
a__: str = fa_thresh
def __a ( ) ->int:
with open(OPTS.data_file ) as f:
a__: Tuple = json.load(_SCREAMING_SNAKE_CASE )
a__: Union[str, Any] = dataset_json['data']
with open(OPTS.pred_file ) as f:
a__: Dict = json.load(_SCREAMING_SNAKE_CASE )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
a__: Dict = json.load(_SCREAMING_SNAKE_CASE )
else:
a__: Optional[Any] = {k: 0.0 for k in preds}
a__: List[Any] = make_qid_to_has_ans(_SCREAMING_SNAKE_CASE ) # maps qid to True/False
a__: Optional[int] = [k for k, v in qid_to_has_ans.items() if v]
a__: Union[str, Any] = [k for k, v in qid_to_has_ans.items() if not v]
a__ , a__: Optional[Any] = get_raw_scores(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = apply_no_ans_threshold(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.na_prob_thresh )
a__: Dict = apply_no_ans_threshold(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.na_prob_thresh )
a__: str = make_eval_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if has_ans_qids:
a__: List[str] = make_eval_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , qid_list=_SCREAMING_SNAKE_CASE )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'HasAns' )
if no_ans_qids:
a__: Optional[Any] = make_eval_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , qid_list=_SCREAMING_SNAKE_CASE )
merge_eval(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'NoAns' )
if OPTS.na_prob_file:
find_all_best_thresh(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.out_image_dir )
histogram_na_prob(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.out_image_dir , 'hasAns' )
histogram_na_prob(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , OPTS.out_image_dir , 'noAns' )
if OPTS.out_file:
with open(OPTS.out_file , 'w' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
print(json.dumps(_SCREAMING_SNAKE_CASE , indent=2 ) )
if __name__ == "__main__":
lowercase__ = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main()
| 203 | 0 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
lowercase_ = {
"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"
)
},
}
lowercase_ = {
"facebook/blenderbot_small-90M": 5_1_2,
}
class __lowerCAmelCase ( __lowerCAmelCase ):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase="<|endoftext|>" , lowerCAmelCase="<|endoftext|>" , lowerCAmelCase="<|endoftext|>" , lowerCAmelCase=False , lowerCAmelCase=True , **lowerCAmelCase , ) -> int:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCamelCase__ , merges=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , trim_offsets=lowerCamelCase__ , ) , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , **lowerCamelCase__ , )
_lowercase =add_prefix_space
def A__ ( self , lowerCAmelCase , lowerCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
_lowercase =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A__ ( self , lowerCAmelCase , lowerCAmelCase = None ) -> List[int]:
'''simple docstring'''
_lowercase =[self.sep_token_id]
_lowercase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 205 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
UpperCamelCase__ : List[Any] = torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )
UpperCamelCase__ : str = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
UpperCamelCase__ : int = {}
for k, v in state_dict.items():
if "pred_layer" in k:
UpperCamelCase__ : Optional[int] = v
else:
UpperCamelCase__ : Tuple = v
UpperCamelCase__ : Union[str, Any] = chkpt['''params''']
UpperCamelCase__ : Optional[Any] = {n: v for n, v in config.items() if not isinstance(SCREAMING_SNAKE_CASE , (torch.FloatTensor, numpy.ndarray) )}
UpperCamelCase__ : Dict = chkpt['''dico_word2id''']
UpperCamelCase__ : Dict = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
UpperCamelCase__ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCamelCase__ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCamelCase__ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE , indent=2 ) + '''\n''' )
print(F"Save vocab file to {pytorch_config_dump_path}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE , indent=2 ) + '''\n''' )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 146 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def snake_case_(_UpperCamelCase = 1_000_000 ) -> int:
"""simple docstring"""
_snake_case = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_snake_case = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_snake_case = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f'''{solution() = }''')
| 357 |
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 lowercase_ ( __lowercase , unittest.TestCase ):
UpperCamelCase_ : Union[str, Any] = CLIPTokenizer
UpperCamelCase_ : Optional[int] = CLIPTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Union[str, Any] = {}
UpperCamelCase_ : Optional[Any] = False
def UpperCamelCase_ ( self : Union[str, Any] ) -> Dict:
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(A__ , range(len(A__ ) ) ) )
_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(A__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A__ ) )
def UpperCamelCase_ ( self : List[Any] , **A__ : int ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase_ ( self : Any , **A__ : Tuple ) -> Optional[Any]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase_ ( self : Optional[Any] , A__ : str ) -> str:
_snake_case = '''lower newer'''
_snake_case = '''lower newer'''
return input_text, output_text
def UpperCamelCase_ ( self : Union[str, Any] ) -> 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(A__ )
self.assertListEqual(A__ , A__ )
_snake_case = tokens + [tokenizer.unk_token]
_snake_case = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
@require_ftfy
def UpperCamelCase_ ( self : Any ) -> Optional[int]:
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(A__ , **A__ )
_snake_case = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_snake_case = '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
_snake_case = tokenizer_s.tokenize(A__ )
_snake_case = tokenizer_r.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# 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(A__ )
_snake_case = tokenizer_r.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# 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(A__ )
_snake_case = tokenizer_r.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# 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(A__ )
_snake_case = tokenizer_r.tokenize(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase_ ( self : List[Any] ) -> Optional[Any]:
# 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(
A__ , use_fast=A__ , )
_snake_case = tokenizer_r(A__ , return_offsets_mapping=A__ , add_special_tokens=A__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A__ ) + 1, len(A__ ) + 1 + len(A__ )) , )
_snake_case = f""" {text}"""
_snake_case = self.rust_tokenizer_class.from_pretrained(
A__ , use_fast=A__ , )
_snake_case = tokenizer_r(A__ , return_offsets_mapping=A__ , add_special_tokens=A__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(A__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A__ ) + 1, 1 + len(A__ ) + 1 + len(A__ )) , )
def UpperCamelCase_ ( self : Union[str, Any] ) -> 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(A__ ) 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 UpperCamelCase_ ( self : Dict ) -> Union[str, Any]:
super().test_tokenization_python_rust_equals()
def UpperCamelCase_ ( self : str ) -> Optional[int]:
# CLIP always lower cases letters
pass
| 278 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCAmelCase_ ( __A ) -> typing.Counter[int]:
'''simple docstring'''
UpperCAmelCase__ = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__A, max_perimeter + 1 ):
UpperCAmelCase__ = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(__A ):
UpperCAmelCase__ = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def lowerCAmelCase_ ( __A = 1_000 ) -> int:
'''simple docstring'''
UpperCAmelCase__ = pythagorean_triple(__A )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f'''Perimeter {solution()} has maximum solutions''')
| 65 | def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
def get_matched_characters(__A, __A ) -> str:
UpperCAmelCase__ = []
UpperCAmelCase__ = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCAmelCase__ = int(max(0, i - limit ) )
UpperCAmelCase__ = int(min(i + limit + 1, len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__A )
UpperCAmelCase__ = f"""{_stra[0:_stra.index(__A )]} {_stra[_stra.index(__A ) + 1:]}"""
return "".join(__A )
# matching characters
UpperCAmelCase__ = get_matched_characters(__A, __A )
UpperCAmelCase__ = get_matched_characters(__A, __A )
UpperCAmelCase__ = len(__A )
# transposition
UpperCAmelCase__ = (
len([(ca, ca) for ca, ca in zip(__A, __A ) if ca != ca] ) // 2
)
if not match_count:
UpperCAmelCase__ = 0.0
else:
UpperCAmelCase__ = (
1
/ 3
* (
match_count / len(__A )
+ match_count / len(__A )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCAmelCase__ = 0
for ca, ca in zip(stra[:4], stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 65 | 1 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class __lowerCamelCase ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = StableDiffusionControlNetImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"} )
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
torch.manual_seed(0 )
lowercase_ = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowercase_ = 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 )
lowercase_ = 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 , )
torch.manual_seed(0 )
lowercase_ = 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 , )
lowercase_ = CLIPTextModel(_A )
lowercase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowercase_ = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def A__ ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
if str(_A ).startswith("mps" ):
lowercase_ = torch.manual_seed(_A )
else:
lowercase_ = torch.Generator(device=_A ).manual_seed(_A )
lowercase_ = 2
lowercase_ = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_A , device=torch.device(_A ) , )
lowercase_ = floats_tensor(control_image.shape , rng=random.Random(_A ) ).to(_A )
lowercase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ = Image.fromarray(np.uinta(_A ) ).convert("RGB" ).resize((64, 64) )
lowercase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def A__ ( self ) -> List[Any]:
'''simple docstring'''
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def A__ ( self ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
class __lowerCamelCase ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = StableDiffusionControlNetImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def A__ ( self ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(UpperCAmelCase ):
if isinstance(_A , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowercase_ = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(_A )
torch.manual_seed(0 )
lowercase_ = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(_A )
torch.manual_seed(0 )
lowercase_ = 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 )
lowercase_ = 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 , )
torch.manual_seed(0 )
lowercase_ = 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 , )
lowercase_ = CLIPTextModel(_A )
lowercase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowercase_ = MultiControlNetModel([controlneta, controlneta] )
lowercase_ = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def A__ ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
if str(_A ).startswith("mps" ):
lowercase_ = torch.manual_seed(_A )
else:
lowercase_ = torch.Generator(device=_A ).manual_seed(_A )
lowercase_ = 2
lowercase_ = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_A , device=torch.device(_A ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_A , device=torch.device(_A ) , ),
]
lowercase_ = floats_tensor(control_image[0].shape , rng=random.Random(_A ) ).to(_A )
lowercase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase_ = Image.fromarray(np.uinta(_A ) ).convert("RGB" ).resize((64, 64) )
lowercase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = self.get_dummy_components()
lowercase_ = self.pipeline_class(**_A )
pipe.to(_A )
lowercase_ = 10.0
lowercase_ = 4
lowercase_ = self.get_dummy_inputs(_A )
lowercase_ = steps
lowercase_ = scale
lowercase_ = pipe(**_A )[0]
lowercase_ = self.get_dummy_inputs(_A )
lowercase_ = steps
lowercase_ = scale
lowercase_ = pipe(**_A , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowercase_ = self.get_dummy_inputs(_A )
lowercase_ = steps
lowercase_ = scale
lowercase_ = pipe(**_A , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowercase_ = self.get_dummy_inputs(_A )
lowercase_ = steps
lowercase_ = scale
lowercase_ = pipe(**_A , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
def A__ ( self ) -> int:
'''simple docstring'''
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def A__ ( self ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = self.get_dummy_components()
lowercase_ = self.pipeline_class(**_A )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(_A )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny" )
lowercase_ = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , safety_checker=_A , controlnet=_A )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_A )
lowercase_ = torch.Generator(device="cpu" ).manual_seed(0 )
lowercase_ = "evil space-punk bird"
lowercase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ).resize((512, 512) )
lowercase_ = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png" ).resize((512, 512) )
lowercase_ = pipe(
_A , _A , control_image=_A , generator=_A , output_type="np" , num_inference_steps=50 , strength=0.6 , )
lowercase_ = output.images[0]
assert image.shape == (512, 512, 3)
lowercase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy" )
assert np.abs(expected_image - image ).max() < 9e-2
| 364 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"""vocab_file""": """vocab.txt"""}
SCREAMING_SNAKE_CASE__ = {
"""vocab_file""": {
"""facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
"""facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
},
}
SCREAMING_SNAKE_CASE__ = {
"""facebook/esm2_t6_8M_UR50D""": 1_0_2_4,
"""facebook/esm2_t12_35M_UR50D""": 1_0_2_4,
}
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Any ):
'''simple docstring'''
with open(__lowerCamelCase , "r" ) as f:
lowercase_ = f.read().splitlines()
return [l.strip() for l in lines]
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["input_ids", "attention_mask"]
def __init__( self , UpperCAmelCase , UpperCAmelCase="<unk>" , UpperCAmelCase="<cls>" , UpperCAmelCase="<pad>" , UpperCAmelCase="<mask>" , UpperCAmelCase="<eos>" , **UpperCAmelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**UpperCAmelCase )
lowercase_ = load_vocab_file(UpperCAmelCase )
lowercase_ = dict(enumerate(self.all_tokens ) )
lowercase_ = {tok: ind for ind, tok in enumerate(self.all_tokens )}
lowercase_ = unk_token
lowercase_ = cls_token
lowercase_ = pad_token
lowercase_ = mask_token
lowercase_ = eos_token
lowercase_ = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def A__ ( self , UpperCAmelCase ) -> str:
'''simple docstring'''
return self._id_to_token.get(UpperCAmelCase , self.unk_token )
def A__ ( self , UpperCAmelCase ) -> int:
'''simple docstring'''
return self._token_to_id.get(UpperCAmelCase , self._token_to_id.get(self.unk_token ) )
def A__ ( self , UpperCAmelCase , **UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
return text.split()
def A__ ( self , UpperCAmelCase=False ) -> List[str]:
'''simple docstring'''
return len(self._id_to_token )
def A__ ( self ) -> Tuple:
'''simple docstring'''
return {token: i for i, token in enumerate(self.all_tokens )}
def A__ ( self , UpperCAmelCase ) -> int:
'''simple docstring'''
return self._token_to_id.get(UpperCAmelCase , self._token_to_id.get(self.unk_token ) )
def A__ ( self , UpperCAmelCase ) -> str:
'''simple docstring'''
return self._id_to_token.get(UpperCAmelCase , self.unk_token )
def A__ ( self , UpperCAmelCase , UpperCAmelCase = None ) -> List[int]:
'''simple docstring'''
lowercase_ = [self.cls_token_id]
lowercase_ = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!" )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def A__ ( self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
lowercase_ = [1] + ([0] * len(UpperCAmelCase )) + [1]
if token_ids_a is not None:
mask += [0] * len(UpperCAmelCase ) + [1]
return mask
def A__ ( self , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = os.path.join(UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + "vocab.txt" )
with open(UpperCAmelCase , "w" ) as f:
f.write("\n".join(self.all_tokens ) )
return (vocab_file,)
@property
def A__ ( self ) -> int:
'''simple docstring'''
return self.get_vocab_size(with_added_tokens=UpperCAmelCase )
def A__ ( self , UpperCAmelCase , UpperCAmelCase = False ) -> int:
'''simple docstring'''
return super()._add_tokens(UpperCAmelCase , special_tokens=UpperCAmelCase )
| 297 | 0 |
'''simple docstring'''
def a__ ( a__ = 1_00_00_00 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = set(range(3 , a__ , 2 ) )
primes.add(2 )
for p in range(3 , a__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , a__ , a__ ) ) )
__SCREAMING_SNAKE_CASE = [float(a__ ) for n in range(limit + 1 )]
for p in primes:
for n in range(a__ , limit + 1 , a__ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 267 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCAmelCase__ ( a ):
"""simple docstring"""
lowerCAmelCase__ = "linear"
lowerCAmelCase__ = "cosine"
lowerCAmelCase__ = "cosine_with_restarts"
lowerCAmelCase__ = "polynomial"
lowerCAmelCase__ = "constant"
lowerCAmelCase__ = "constant_with_warmup"
lowerCAmelCase__ = "piecewise_constant"
def a__ ( a__ , a__ = -1 ):
"""simple docstring"""
return LambdaLR(a__ , lambda a__ : 1 , last_epoch=a__ )
def a__ ( a__ , a__ , a__ = -1 ):
"""simple docstring"""
def lr_lambda(a__ ):
if current_step < num_warmup_steps:
return float(a__ ) / float(max(1.0 , a__ ) )
return 1.0
return LambdaLR(a__ , a__ , last_epoch=a__ )
def a__ ( a__ , a__ , a__ = -1 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = step_rules.split(""",""" )
for rule_str in rule_list[:-1]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = rule_str.split(""":""" )
__SCREAMING_SNAKE_CASE = int(a__ )
__SCREAMING_SNAKE_CASE = float(a__ )
__SCREAMING_SNAKE_CASE = value
__SCREAMING_SNAKE_CASE = float(rule_list[-1] )
def create_rules_function(a__ , a__ ):
def rule_func(a__ ) -> float:
__SCREAMING_SNAKE_CASE = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(a__ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
__SCREAMING_SNAKE_CASE = create_rules_function(a__ , a__ )
return LambdaLR(a__ , a__ , last_epoch=a__ )
def a__ ( a__ , a__ , a__ , a__=-1 ):
"""simple docstring"""
def lr_lambda(a__ ):
if current_step < num_warmup_steps:
return float(a__ ) / float(max(1 , a__ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(a__ , a__ , a__ )
def a__ ( a__ , a__ , a__ , a__ = 0.5 , a__ = -1 ):
"""simple docstring"""
def lr_lambda(a__ ):
if current_step < num_warmup_steps:
return float(a__ ) / float(max(1 , a__ ) )
__SCREAMING_SNAKE_CASE = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(a__ ) * 2.0 * progress )) )
return LambdaLR(a__ , a__ , a__ )
def a__ ( a__ , a__ , a__ , a__ = 1 , a__ = -1 ):
"""simple docstring"""
def lr_lambda(a__ ):
if current_step < num_warmup_steps:
return float(a__ ) / float(max(1 , a__ ) )
__SCREAMING_SNAKE_CASE = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(a__ ) * progress) % 1.0) )) )
return LambdaLR(a__ , a__ , a__ )
def a__ ( a__ , a__ , a__ , a__=1E-7 , a__=1.0 , a__=-1 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = optimizer.defaults["""lr"""]
if not (lr_init > lr_end):
raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})' )
def lr_lambda(a__ ):
if current_step < num_warmup_steps:
return float(a__ ) / float(max(1 , a__ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
__SCREAMING_SNAKE_CASE = lr_init - lr_end
__SCREAMING_SNAKE_CASE = num_training_steps - num_warmup_steps
__SCREAMING_SNAKE_CASE = 1 - (current_step - num_warmup_steps) / decay_steps
__SCREAMING_SNAKE_CASE = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(a__ , a__ , a__ )
UpperCAmelCase : Optional[Any] = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def a__ ( a__ , a__ , a__ = None , a__ = None , a__ = None , a__ = 1 , a__ = 1.0 , a__ = -1 , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = SchedulerType(a__ )
__SCREAMING_SNAKE_CASE = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(a__ , last_epoch=a__ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(a__ , step_rules=a__ , last_epoch=a__ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.' )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(a__ , num_warmup_steps=a__ , last_epoch=a__ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.' )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
a__ , num_warmup_steps=a__ , num_training_steps=a__ , num_cycles=a__ , last_epoch=a__ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
a__ , num_warmup_steps=a__ , num_training_steps=a__ , power=a__ , last_epoch=a__ , )
return schedule_func(
a__ , num_warmup_steps=a__ , num_training_steps=a__ , last_epoch=a__ )
| 267 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _lowerCAmelCase ( _UpperCamelCase : Any ) -> Dict:
"""simple docstring"""
if "cls_token" in name:
_SCREAMING_SNAKE_CASE =name.replace('cls_token' , 'vit.embeddings.cls_token' )
if "mask_token" in name:
_SCREAMING_SNAKE_CASE =name.replace('mask_token' , 'decoder.mask_token' )
if "decoder_pos_embed" in name:
_SCREAMING_SNAKE_CASE =name.replace('decoder_pos_embed' , 'decoder.decoder_pos_embed' )
if "pos_embed" in name and "decoder" not in name:
_SCREAMING_SNAKE_CASE =name.replace('pos_embed' , 'vit.embeddings.position_embeddings' )
if "patch_embed.proj" in name:
_SCREAMING_SNAKE_CASE =name.replace('patch_embed.proj' , 'vit.embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
_SCREAMING_SNAKE_CASE =name.replace('patch_embed.norm' , 'vit.embeddings.norm' )
if "decoder_blocks" in name:
_SCREAMING_SNAKE_CASE =name.replace('decoder_blocks' , 'decoder.decoder_layers' )
if "blocks" in name:
_SCREAMING_SNAKE_CASE =name.replace('blocks' , 'vit.encoder.layer' )
if "attn.proj" in name:
_SCREAMING_SNAKE_CASE =name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
_SCREAMING_SNAKE_CASE =name.replace('attn' , 'attention.self' )
if "norm1" in name:
_SCREAMING_SNAKE_CASE =name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
_SCREAMING_SNAKE_CASE =name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
_SCREAMING_SNAKE_CASE =name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
_SCREAMING_SNAKE_CASE =name.replace('mlp.fc2' , 'output.dense' )
if "decoder_embed" in name:
_SCREAMING_SNAKE_CASE =name.replace('decoder_embed' , 'decoder.decoder_embed' )
if "decoder_norm" in name:
_SCREAMING_SNAKE_CASE =name.replace('decoder_norm' , 'decoder.decoder_norm' )
if "decoder_pred" in name:
_SCREAMING_SNAKE_CASE =name.replace('decoder_pred' , 'decoder.decoder_pred' )
if "norm.weight" in name and "decoder" not in name:
_SCREAMING_SNAKE_CASE =name.replace('norm.weight' , 'vit.layernorm.weight' )
if "norm.bias" in name and "decoder" not in name:
_SCREAMING_SNAKE_CASE =name.replace('norm.bias' , 'vit.layernorm.bias' )
return name
def _lowerCAmelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : str ) -> List[str]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE =orig_state_dict.pop(_UpperCamelCase )
if "qkv" in key:
_SCREAMING_SNAKE_CASE =key.split('.' )
_SCREAMING_SNAKE_CASE =int(key_split[1] )
if "decoder_blocks" in key:
_SCREAMING_SNAKE_CASE =config.decoder_hidden_size
_SCREAMING_SNAKE_CASE ='decoder.decoder_layers.'
if "weight" in key:
_SCREAMING_SNAKE_CASE =val[:dim, :]
_SCREAMING_SNAKE_CASE =val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE =val[-dim:, :]
elif "bias" in key:
_SCREAMING_SNAKE_CASE =val[:dim]
_SCREAMING_SNAKE_CASE =val[dim : dim * 2]
_SCREAMING_SNAKE_CASE =val[-dim:]
else:
_SCREAMING_SNAKE_CASE =config.hidden_size
_SCREAMING_SNAKE_CASE ='vit.encoder.layer.'
if "weight" in key:
_SCREAMING_SNAKE_CASE =val[:dim, :]
_SCREAMING_SNAKE_CASE =val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE =val[-dim:, :]
elif "bias" in key:
_SCREAMING_SNAKE_CASE =val[:dim]
_SCREAMING_SNAKE_CASE =val[dim : dim * 2]
_SCREAMING_SNAKE_CASE =val[-dim:]
else:
_SCREAMING_SNAKE_CASE =val
return orig_state_dict
def _lowerCAmelCase ( _UpperCamelCase : Any , _UpperCamelCase : int ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =ViTMAEConfig()
if "large" in checkpoint_url:
_SCREAMING_SNAKE_CASE =10_24
_SCREAMING_SNAKE_CASE =40_96
_SCREAMING_SNAKE_CASE =24
_SCREAMING_SNAKE_CASE =16
elif "huge" in checkpoint_url:
_SCREAMING_SNAKE_CASE =14
_SCREAMING_SNAKE_CASE =12_80
_SCREAMING_SNAKE_CASE =51_20
_SCREAMING_SNAKE_CASE =32
_SCREAMING_SNAKE_CASE =16
_SCREAMING_SNAKE_CASE =ViTMAEForPreTraining(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(_UpperCamelCase , map_location='cpu' )['model']
_SCREAMING_SNAKE_CASE =ViTMAEImageProcessor(size=config.image_size )
_SCREAMING_SNAKE_CASE =convert_state_dict(_UpperCamelCase , _UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
model.eval()
_SCREAMING_SNAKE_CASE ='https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'
_SCREAMING_SNAKE_CASE =Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw )
_SCREAMING_SNAKE_CASE =ViTMAEImageProcessor(size=config.image_size )
_SCREAMING_SNAKE_CASE =image_processor(images=_UpperCamelCase , return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
_SCREAMING_SNAKE_CASE =model(**_UpperCamelCase )
_SCREAMING_SNAKE_CASE =outputs.logits
if "large" in checkpoint_url:
_SCREAMING_SNAKE_CASE =torch.tensor(
[[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]] )
elif "huge" in checkpoint_url:
_SCREAMING_SNAKE_CASE =torch.tensor(
[[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]] )
else:
_SCREAMING_SNAKE_CASE =torch.tensor(
[[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , _UpperCamelCase , atol=1E-4 )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCamelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowerCamelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
lowerCamelCase : Any = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 114 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =int(number**0.5 )
return number == sq * sq
def _lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> tuple[int, int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
_SCREAMING_SNAKE_CASE =x_den * y_den * z_den
_SCREAMING_SNAKE_CASE =gcd(_UpperCamelCase , _UpperCamelCase )
top //= hcf
bottom //= hcf
return top, bottom
def _lowerCAmelCase ( _UpperCamelCase : int = 35 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =set()
_SCREAMING_SNAKE_CASE =42
_SCREAMING_SNAKE_CASE =Fraction(0 )
_SCREAMING_SNAKE_CASE =42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
_SCREAMING_SNAKE_CASE =x_num * y_den + x_den * y_num
_SCREAMING_SNAKE_CASE =x_den * y_den
_SCREAMING_SNAKE_CASE =gcd(_UpperCamelCase , _UpperCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_SCREAMING_SNAKE_CASE =add_three(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
unique_s.add(_UpperCamelCase )
# n=2
_SCREAMING_SNAKE_CASE =(
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
_SCREAMING_SNAKE_CASE =x_den * x_den * y_den * y_den
if is_sq(_UpperCamelCase ) and is_sq(_UpperCamelCase ):
_SCREAMING_SNAKE_CASE =int(sqrt(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =int(sqrt(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =gcd(_UpperCamelCase , _UpperCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_SCREAMING_SNAKE_CASE =add_three(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
unique_s.add(_UpperCamelCase )
# n=-1
_SCREAMING_SNAKE_CASE =x_num * y_num
_SCREAMING_SNAKE_CASE =x_den * y_num + x_num * y_den
_SCREAMING_SNAKE_CASE =gcd(_UpperCamelCase , _UpperCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_SCREAMING_SNAKE_CASE =add_three(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
unique_s.add(_UpperCamelCase )
# n=2
_SCREAMING_SNAKE_CASE =x_num * x_num * y_num * y_num
_SCREAMING_SNAKE_CASE =(
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_UpperCamelCase ) and is_sq(_UpperCamelCase ):
_SCREAMING_SNAKE_CASE =int(sqrt(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =int(sqrt(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =gcd(_UpperCamelCase , _UpperCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_SCREAMING_SNAKE_CASE =add_three(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
unique_s.add(_UpperCamelCase )
for num, den in unique_s:
total += Fraction(_UpperCamelCase , _UpperCamelCase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 114 | 1 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
a = concatenate_datasets
a = DownloadConfig
a = DownloadManager
a = DownloadMode
a = DownloadConfig
a = DownloadMode
a = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 155 |
"""simple docstring"""
def lowercase (snake_case__ : list[int] , snake_case__ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case__ ) == len(snake_case__ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = equationa
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = equationa
# Calculate the determinants of the matrices
lowerCAmelCase = aa * ba - aa * ba
lowerCAmelCase = ca * ba - ca * ba
lowerCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
lowerCAmelCase = determinant_x / determinant
lowerCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 155 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 108 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
__lowerCAmelCase: Dict = original_name.split("." )[0]
__lowerCAmelCase: Any = key.split("." )
__lowerCAmelCase: Union[str, Any] = int(key_list[key_list.index(__SCREAMING_SNAKE_CASE ) - 2] )
__lowerCAmelCase: List[Any] = int(key_list[key_list.index(__SCREAMING_SNAKE_CASE ) - 1] )
__lowerCAmelCase: List[str] = orig_block_num - offset
__lowerCAmelCase: Tuple = key.replace(F"{orig_block_num}.{layer_num}.{original_name}" , F"block.{new_block_num}.{layer_num}.{new_name}" )
return key
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: List[Any] = OrderedDict()
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
__lowerCAmelCase: Dict = key.replace("network" , "poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
__lowerCAmelCase: int = key[: key.find("proj" )]
__lowerCAmelCase: Dict = key.replace(__SCREAMING_SNAKE_CASE , F"patch_embeddings.{total_embed_found}." )
__lowerCAmelCase: Optional[int] = key.replace("proj" , "projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
__lowerCAmelCase: int = "poolformer.encoder." + key
if "mlp.fc1" in key:
__lowerCAmelCase: Optional[Any] = replace_key_with_offset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , "mlp.fc1" , "output.conv1" )
if "mlp.fc2" in key:
__lowerCAmelCase: Dict = replace_key_with_offset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , "mlp.fc2" , "output.conv2" )
if "norm1" in key:
__lowerCAmelCase: Dict = replace_key_with_offset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , "norm1" , "before_norm" )
if "norm2" in key:
__lowerCAmelCase: Dict = replace_key_with_offset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , "norm2" , "after_norm" )
if "layer_scale_1" in key:
__lowerCAmelCase: Optional[int] = replace_key_with_offset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , "layer_scale_1" , "layer_scale_1" )
if "layer_scale_2" in key:
__lowerCAmelCase: Any = replace_key_with_offset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , "layer_scale_2" , "layer_scale_2" )
if "head" in key:
__lowerCAmelCase: int = key.replace("head" , "classifier" )
__lowerCAmelCase: Tuple = value
return new_state_dict
def a__ ( ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowerCAmelCase: int = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return image
@torch.no_grad()
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Optional[Any]:
__lowerCAmelCase: Any = PoolFormerConfig()
# set attributes based on model_name
__lowerCAmelCase: Any = "huggingface/label-files"
__lowerCAmelCase: int = model_name[-3:]
__lowerCAmelCase: List[Any] = 1_0_0_0
__lowerCAmelCase: Tuple = "imagenet-1k-id2label.json"
__lowerCAmelCase: str = (1, 1_0_0_0)
# set config attributes
__lowerCAmelCase: Dict = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowerCAmelCase: List[str] = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowerCAmelCase: Any = idalabel
__lowerCAmelCase: Any = {v: k for k, v in idalabel.items()}
if size == "s12":
__lowerCAmelCase: Dict = [2, 2, 6, 2]
__lowerCAmelCase: str = [6_4, 1_2_8, 3_2_0, 5_1_2]
__lowerCAmelCase: Optional[Any] = 4.0
__lowerCAmelCase: Union[str, Any] = 0.9
elif size == "s24":
__lowerCAmelCase: Tuple = [4, 4, 1_2, 4]
__lowerCAmelCase: List[str] = [6_4, 1_2_8, 3_2_0, 5_1_2]
__lowerCAmelCase: Tuple = 4.0
__lowerCAmelCase: Optional[int] = 0.9
elif size == "s36":
__lowerCAmelCase: int = [6, 6, 1_8, 6]
__lowerCAmelCase: int = [6_4, 1_2_8, 3_2_0, 5_1_2]
__lowerCAmelCase: List[str] = 4.0
__lowerCAmelCase: Dict = 1E-6
__lowerCAmelCase: List[Any] = 0.9
elif size == "m36":
__lowerCAmelCase: Dict = [6, 6, 1_8, 6]
__lowerCAmelCase: Dict = [9_6, 1_9_2, 3_8_4, 7_6_8]
__lowerCAmelCase: str = 4.0
__lowerCAmelCase: Union[str, Any] = 1E-6
__lowerCAmelCase: Union[str, Any] = 0.95
elif size == "m48":
__lowerCAmelCase: str = [8, 8, 2_4, 8]
__lowerCAmelCase: Optional[int] = [9_6, 1_9_2, 3_8_4, 7_6_8]
__lowerCAmelCase: str = 4.0
__lowerCAmelCase: int = 1E-6
__lowerCAmelCase: str = 0.95
else:
raise ValueError(F"Size {size} not supported" )
# load image processor
__lowerCAmelCase: Union[str, Any] = PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE )
# Prepare image
__lowerCAmelCase: int = prepare_img()
__lowerCAmelCase: Tuple = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors="pt" ).pixel_values
logger.info(F"Converting model {model_name}..." )
# load original state dict
__lowerCAmelCase: Optional[int] = torch.load(__SCREAMING_SNAKE_CASE , map_location=torch.device("cpu" ) )
# rename keys
__lowerCAmelCase: Any = rename_keys(__SCREAMING_SNAKE_CASE )
# create HuggingFace model and load state dict
__lowerCAmelCase: str = PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
model.eval()
# Define image processor
__lowerCAmelCase: Any = PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = image_processor(images=prepare_img() , return_tensors="pt" ).pixel_values
# forward pass
__lowerCAmelCase: int = model(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = outputs.logits
# define expected logit slices for different models
if size == "s12":
__lowerCAmelCase: List[str] = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
__lowerCAmelCase: Optional[int] = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
__lowerCAmelCase: List[str] = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
__lowerCAmelCase: Union[str, Any] = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
__lowerCAmelCase: List[str] = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F"Size {size} not supported" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-2 )
# finally, save model and image processor
logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..." )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="poolformer_s12",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
__A = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 108 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
a_ : str = logging.getLogger(__name__)
require_version("""pytorch_lightning>=1.0.4""")
a_ : Union[str, Any] = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeqaSeqLM,
"translation": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
a_ : Optional[Any] = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
a_ : Optional[int] = sorted(arg_to_scheduler.keys())
a_ : Dict = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class snake_case ( pl.LightningModule ):
"""simple docstring"""
def __init__( self , UpperCamelCase , UpperCamelCase=None , UpperCamelCase="base" , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase=None , **UpperCamelCase , ):
"""simple docstring"""
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 0
lowerCamelCase_ = Path(self.hparams.output_dir )
lowerCamelCase_ = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
lowerCamelCase_ = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({"num_labels": num_labels} if num_labels is not None else {}) , cache_dir=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
lowerCamelCase_ = config
lowerCamelCase_ = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert hasattr(self.config , SCREAMING_SNAKE_CASE_ ), f'''model config doesn\'t have a `{p}` attribute'''
setattr(self.config , SCREAMING_SNAKE_CASE_ , getattr(self.hparams , SCREAMING_SNAKE_CASE_ ) )
if tokenizer is None:
lowerCamelCase_ = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=SCREAMING_SNAKE_CASE_ , )
else:
lowerCamelCase_ = tokenizer
lowerCamelCase_ = MODEL_MODES[mode]
if model is None:
lowerCamelCase_ = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool(".ckpt" in self.hparams.model_name_or_path ) , config=self.config , cache_dir=SCREAMING_SNAKE_CASE_ , )
else:
lowerCamelCase_ = model
def snake_case ( self , *UpperCamelCase , **UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = arg_to_scheduler[self.hparams.lr_scheduler]
lowerCamelCase_ = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
lowerCamelCase_ = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model
lowerCamelCase_ = ["bias", "LayerNorm.weight"]
lowerCamelCase_ = [
{
"params": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
lowerCamelCase_ = Adafactor(
SCREAMING_SNAKE_CASE_ , lr=self.hparams.learning_rate , scale_parameter=SCREAMING_SNAKE_CASE_ , relative_step=SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase_ = AdamW(
SCREAMING_SNAKE_CASE_ , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
lowerCamelCase_ = optimizer
lowerCamelCase_ = self.get_lr_scheduler()
return [optimizer], [scheduler]
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
return self.validation_step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
return self.validation_end(SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
lowerCamelCase_ = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
if stage == "test":
lowerCamelCase_ = len(self.test_dataloader().dataset )
else:
lowerCamelCase_ = self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = len(self.train_dataloader().dataset )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase = False ):
"""simple docstring"""
raise NotImplementedError("You must implement this for your task" )
def snake_case ( self ):
"""simple docstring"""
return self.train_loader
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=SCREAMING_SNAKE_CASE_ )
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
return os.path.join(
self.hparams.data_dir , "cached_{}_{}_{}".format(
SCREAMING_SNAKE_CASE_ , list(filter(SCREAMING_SNAKE_CASE_ , self.hparams.model_name_or_path.split("/" ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = self.output_dir.joinpath("best_tfmr" )
lowerCamelCase_ = self.step_count
self.model.save_pretrained(SCREAMING_SNAKE_CASE_ )
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
@staticmethod
def snake_case ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
parser.add_argument(
"--model_name_or_path" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--config_name" , default="" , type=SCREAMING_SNAKE_CASE_ , help="Pretrained config name or path if not the same as model_name" )
parser.add_argument(
"--tokenizer_name" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument(
"--cache_dir" , default=str(Path(SCREAMING_SNAKE_CASE_ ).parent / "test_run" / "cache" ) , type=SCREAMING_SNAKE_CASE_ , help="Where do you want to store the pre-trained models downloaded from huggingface.co" , )
parser.add_argument(
"--encoder_layerdrop" , type=SCREAMING_SNAKE_CASE_ , help="Encoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--decoder_layerdrop" , type=SCREAMING_SNAKE_CASE_ , help="Decoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--dropout" , type=SCREAMING_SNAKE_CASE_ , help="Dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--attention_dropout" , type=SCREAMING_SNAKE_CASE_ , help="Attention dropout probability (Optional). Goes into model.config" , )
parser.add_argument("--learning_rate" , default=5e-5 , type=SCREAMING_SNAKE_CASE_ , help="The initial learning rate for Adam." )
parser.add_argument(
"--lr_scheduler" , default="linear" , choices=SCREAMING_SNAKE_CASE_ , metavar=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Learning rate scheduler" , )
parser.add_argument("--weight_decay" , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help="Weight decay if we apply some." )
parser.add_argument("--adam_epsilon" , default=1e-8 , type=SCREAMING_SNAKE_CASE_ , help="Epsilon for Adam optimizer." )
parser.add_argument("--warmup_steps" , default=0 , type=SCREAMING_SNAKE_CASE_ , help="Linear warmup over warmup_steps." )
parser.add_argument("--num_workers" , default=4 , type=SCREAMING_SNAKE_CASE_ , help="kwarg passed to DataLoader" )
parser.add_argument("--num_train_epochs" , dest="max_epochs" , default=3 , type=SCREAMING_SNAKE_CASE_ )
parser.add_argument("--train_batch_size" , default=32 , type=SCREAMING_SNAKE_CASE_ )
parser.add_argument("--eval_batch_size" , default=32 , type=SCREAMING_SNAKE_CASE_ )
parser.add_argument("--adafactor" , action="store_true" )
class snake_case ( pl.Callback ):
"""simple docstring"""
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class snake_case ( pl.Callback ):
"""simple docstring"""
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(SCREAMING_SNAKE_CASE_ )
class snake_case ( pl.Callback ):
"""simple docstring"""
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = trainer.lr_schedulers[0]["scheduler"]
lowerCamelCase_ = {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_ )
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
rank_zero_info("***** Validation results *****" )
lowerCamelCase_ = trainer.callback_metrics
# Log results
for key in sorted(SCREAMING_SNAKE_CASE_ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(SCREAMING_SNAKE_CASE_ , str(metrics[key] ) ) )
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
rank_zero_info("***** Test results *****" )
lowerCamelCase_ = trainer.callback_metrics
# Log and save results to file
lowerCamelCase_ = os.path.join(pl_module.hparams.output_dir , "test_results.txt" )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as writer:
for key in sorted(SCREAMING_SNAKE_CASE_ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(SCREAMING_SNAKE_CASE_ , str(metrics[key] ) ) )
writer.write("{} = {}\n".format(SCREAMING_SNAKE_CASE_ , str(metrics[key] ) ) )
def __snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[Any] ):
parser.add_argument(
"--output_dir" , default=str(Path(UpperCAmelCase_ ).parent / "test_run" / "model_checkpoints" ) , type=UpperCAmelCase_ , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=UpperCAmelCase_ , default="O2" , help=(
"For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_tpu_cores" , dest="tpu_cores" , type=UpperCAmelCase_ )
parser.add_argument("--max_grad_norm" , dest="gradient_clip_val" , default=1.0 , type=UpperCAmelCase_ , help="Max gradient norm" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_predict" , action="store_true" , help="Whether to run predictions on the test set." )
parser.add_argument(
"--gradient_accumulation_steps" , dest="accumulate_grad_batches" , type=UpperCAmelCase_ , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--seed" , type=UpperCAmelCase_ , default=42 , help="random seed for initialization" )
parser.add_argument(
"--data_dir" , default=str(Path(UpperCAmelCase_ ).parent / "test_run" / "dummy-train-data" ) , type=UpperCAmelCase_ , help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." , )
def __snake_case ( UpperCAmelCase_ : BaseTransformer , UpperCAmelCase_ : argparse.Namespace , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : List[str]=[] , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : List[Any] , ):
pl.seed_everything(args.seed )
# init model
lowerCamelCase_ = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=UpperCAmelCase_ )
# add custom checkpoints
if checkpoint_callback is None:
lowerCamelCase_ = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="checkpoint" , monitor="val_loss" , mode="min" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(UpperCAmelCase_ )
if logging_callback is None:
lowerCamelCase_ = LoggingCallback()
lowerCamelCase_ = {}
if args.fpaa:
lowerCamelCase_ = 16
if args.gpus > 1:
lowerCamelCase_ = "auto"
lowerCamelCase_ = "ddp"
lowerCamelCase_ = args.accumulate_grad_batches
lowerCamelCase_ = None
lowerCamelCase_ = "auto"
lowerCamelCase_ = pl.Trainer.from_argparse_args(
UpperCAmelCase_ , weights_summary=UpperCAmelCase_ , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=UpperCAmelCase_ , val_check_interval=1 , num_sanity_val_steps=2 , **UpperCAmelCase_ , )
if args.do_train:
trainer.fit(UpperCAmelCase_ )
else:
print("RAG modeling tests with new set functions successfuly executed!" )
return trainer
| 55 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) -> float:
'''simple docstring'''
__UpperCamelCase = x_start
__UpperCamelCase = fnc(snake_case )
__UpperCamelCase = 0.0
for _ in range(snake_case ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
__UpperCamelCase = (x_end - x_start) / steps + xa
__UpperCamelCase = fnc(snake_case )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
__UpperCamelCase = xa
__UpperCamelCase = fxa
return area
if __name__ == "__main__":
def A_ ( snake_case : Tuple ) -> Optional[Any]:
'''simple docstring'''
return x**3 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
lowercase__ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(F"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 1_0
| 328 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class a ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase : str = 42
class a ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , __snake_case : Optional[Any]=3 , __snake_case : str=3 , __snake_case : Tuple=("DownEncoderBlock2D",) , __snake_case : Tuple=(64,) , __snake_case : str=2 , __snake_case : List[str]=32 , __snake_case : Any="silu" , __snake_case : List[Any]=True , ):
super().__init__()
UpperCAmelCase_ = layers_per_block
UpperCAmelCase_ = torch.nn.Convad(
__snake_case , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
UpperCAmelCase_ = None
UpperCAmelCase_ = nn.ModuleList([] )
# down
UpperCAmelCase_ = block_out_channels[0]
for i, down_block_type in enumerate(__snake_case ):
UpperCAmelCase_ = output_channel
UpperCAmelCase_ = block_out_channels[i]
UpperCAmelCase_ = i == len(__snake_case ) - 1
UpperCAmelCase_ = get_down_block(
__snake_case , num_layers=self.layers_per_block , in_channels=__snake_case , out_channels=__snake_case , add_downsample=not is_final_block , resnet_eps=1E-6 , downsample_padding=0 , resnet_act_fn=__snake_case , resnet_groups=__snake_case , attention_head_dim=__snake_case , temb_channels=__snake_case , )
self.down_blocks.append(__snake_case )
# mid
UpperCAmelCase_ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=__snake_case , output_scale_factor=1 , resnet_time_scale_shift='''default''' , attention_head_dim=block_out_channels[-1] , resnet_groups=__snake_case , temb_channels=__snake_case , )
# out
UpperCAmelCase_ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=__snake_case , eps=1E-6 )
UpperCAmelCase_ = nn.SiLU()
UpperCAmelCase_ = 2 * out_channels if double_z else out_channels
UpperCAmelCase_ = nn.Convad(block_out_channels[-1] , __snake_case , 3 , padding=1 )
UpperCAmelCase_ = False
def lowerCamelCase_ ( self : List[str] , __snake_case : int ):
UpperCAmelCase_ = x
UpperCAmelCase_ = self.conv_in(__snake_case )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__snake_case : Union[str, Any] ):
def custom_forward(*__snake_case : str ):
return module(*__snake_case )
return custom_forward
# down
if is_torch_version('''>=''' , '''1.11.0''' ):
for down_block in self.down_blocks:
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(__snake_case ) , __snake_case , use_reentrant=__snake_case )
# middle
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __snake_case , use_reentrant=__snake_case )
else:
for down_block in self.down_blocks:
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(create_custom_forward(__snake_case ) , __snake_case )
# middle
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , __snake_case )
else:
# down
for down_block in self.down_blocks:
UpperCAmelCase_ = down_block(__snake_case )
# middle
UpperCAmelCase_ = self.mid_block(__snake_case )
# post-process
UpperCAmelCase_ = self.conv_norm_out(__snake_case )
UpperCAmelCase_ = self.conv_act(__snake_case )
UpperCAmelCase_ = self.conv_out(__snake_case )
return sample
class a ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , __snake_case : Dict=3 , __snake_case : Tuple=3 , __snake_case : Union[str, Any]=("UpDecoderBlock2D",) , __snake_case : Union[str, Any]=(64,) , __snake_case : List[str]=2 , __snake_case : Optional[int]=32 , __snake_case : Union[str, Any]="silu" , __snake_case : Union[str, Any]="group" , ):
super().__init__()
UpperCAmelCase_ = layers_per_block
UpperCAmelCase_ = nn.Convad(
__snake_case , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
UpperCAmelCase_ = None
UpperCAmelCase_ = nn.ModuleList([] )
UpperCAmelCase_ = in_channels if norm_type == '''spatial''' else None
# mid
UpperCAmelCase_ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=__snake_case , output_scale_factor=1 , resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=__snake_case , temb_channels=__snake_case , )
# up
UpperCAmelCase_ = list(reversed(__snake_case ) )
UpperCAmelCase_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__snake_case ):
UpperCAmelCase_ = output_channel
UpperCAmelCase_ = reversed_block_out_channels[i]
UpperCAmelCase_ = i == len(__snake_case ) - 1
UpperCAmelCase_ = get_up_block(
__snake_case , num_layers=self.layers_per_block + 1 , in_channels=__snake_case , out_channels=__snake_case , prev_output_channel=__snake_case , add_upsample=not is_final_block , resnet_eps=1E-6 , resnet_act_fn=__snake_case , resnet_groups=__snake_case , attention_head_dim=__snake_case , temb_channels=__snake_case , resnet_time_scale_shift=__snake_case , )
self.up_blocks.append(__snake_case )
UpperCAmelCase_ = output_channel
# out
if norm_type == "spatial":
UpperCAmelCase_ = SpatialNorm(block_out_channels[0] , __snake_case )
else:
UpperCAmelCase_ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=__snake_case , eps=1E-6 )
UpperCAmelCase_ = nn.SiLU()
UpperCAmelCase_ = nn.Convad(block_out_channels[0] , __snake_case , 3 , padding=1 )
UpperCAmelCase_ = False
def lowerCamelCase_ ( self : str , __snake_case : Any , __snake_case : Optional[Any]=None ):
UpperCAmelCase_ = z
UpperCAmelCase_ = self.conv_in(__snake_case )
UpperCAmelCase_ = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__snake_case : Dict ):
def custom_forward(*__snake_case : List[Any] ):
return module(*__snake_case )
return custom_forward
if is_torch_version('''>=''' , '''1.11.0''' ):
# middle
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __snake_case , __snake_case , use_reentrant=__snake_case )
UpperCAmelCase_ = sample.to(__snake_case )
# up
for up_block in self.up_blocks:
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(__snake_case ) , __snake_case , __snake_case , use_reentrant=__snake_case )
else:
# middle
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __snake_case , __snake_case )
UpperCAmelCase_ = sample.to(__snake_case )
# up
for up_block in self.up_blocks:
UpperCAmelCase_ = torch.utils.checkpoint.checkpoint(create_custom_forward(__snake_case ) , __snake_case , __snake_case )
else:
# middle
UpperCAmelCase_ = self.mid_block(__snake_case , __snake_case )
UpperCAmelCase_ = sample.to(__snake_case )
# up
for up_block in self.up_blocks:
UpperCAmelCase_ = up_block(__snake_case , __snake_case )
# post-process
if latent_embeds is None:
UpperCAmelCase_ = self.conv_norm_out(__snake_case )
else:
UpperCAmelCase_ = self.conv_norm_out(__snake_case , __snake_case )
UpperCAmelCase_ = self.conv_act(__snake_case )
UpperCAmelCase_ = self.conv_out(__snake_case )
return sample
class a ( nn.Module ):
'''simple docstring'''
def __init__( self : str , __snake_case : Optional[int] , __snake_case : str , __snake_case : str , __snake_case : Tuple=None , __snake_case : Optional[Any]="random" , __snake_case : Optional[Any]=False , __snake_case : Tuple=True ):
super().__init__()
UpperCAmelCase_ = n_e
UpperCAmelCase_ = vq_embed_dim
UpperCAmelCase_ = beta
UpperCAmelCase_ = legacy
UpperCAmelCase_ = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
UpperCAmelCase_ = remap
if self.remap is not None:
self.register_buffer('''used''' , torch.tensor(np.load(self.remap ) ) )
UpperCAmelCase_ = self.used.shape[0]
UpperCAmelCase_ = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
UpperCAmelCase_ = self.re_embed
UpperCAmelCase_ = self.re_embed + 1
print(
F'Remapping {self.n_e} indices to {self.re_embed} indices. '
F'Using {self.unknown_index} for unknown indices.' )
else:
UpperCAmelCase_ = n_e
UpperCAmelCase_ = sane_index_shape
def lowerCamelCase_ ( self : List[str] , __snake_case : List[Any] ):
UpperCAmelCase_ = inds.shape
assert len(__snake_case ) > 1
UpperCAmelCase_ = inds.reshape(ishape[0] , -1 )
UpperCAmelCase_ = self.used.to(__snake_case )
UpperCAmelCase_ = (inds[:, :, None] == used[None, None, ...]).long()
UpperCAmelCase_ = match.argmax(-1 )
UpperCAmelCase_ = match.sum(2 ) < 1
if self.unknown_index == "random":
UpperCAmelCase_ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
UpperCAmelCase_ = self.unknown_index
return new.reshape(__snake_case )
def lowerCamelCase_ ( self : Tuple , __snake_case : Any ):
UpperCAmelCase_ = inds.shape
assert len(__snake_case ) > 1
UpperCAmelCase_ = inds.reshape(ishape[0] , -1 )
UpperCAmelCase_ = self.used.to(__snake_case )
if self.re_embed > self.used.shape[0]: # extra token
UpperCAmelCase_ = 0 # simply set to zero
UpperCAmelCase_ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , __snake_case )
return back.reshape(__snake_case )
def lowerCamelCase_ ( self : Dict , __snake_case : Union[str, Any] ):
# reshape z -> (batch, height, width, channel) and flatten
UpperCAmelCase_ = z.permute(0 , 2 , 3 , 1 ).contiguous()
UpperCAmelCase_ = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
UpperCAmelCase_ = torch.argmin(torch.cdist(__snake_case , self.embedding.weight ) , dim=1 )
UpperCAmelCase_ = self.embedding(__snake_case ).view(z.shape )
UpperCAmelCase_ = None
UpperCAmelCase_ = None
# compute loss for embedding
if not self.legacy:
UpperCAmelCase_ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
UpperCAmelCase_ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
UpperCAmelCase_ = z + (z_q - z).detach()
# reshape back to match original input shape
UpperCAmelCase_ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
UpperCAmelCase_ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
UpperCAmelCase_ = self.remap_to_used(__snake_case )
UpperCAmelCase_ = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
UpperCAmelCase_ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase_ ( self : int , __snake_case : List[str] , __snake_case : Any ):
# shape specifying (batch, height, width, channel)
if self.remap is not None:
UpperCAmelCase_ = indices.reshape(shape[0] , -1 ) # add batch axis
UpperCAmelCase_ = self.unmap_to_all(__snake_case )
UpperCAmelCase_ = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
UpperCAmelCase_ = self.embedding(__snake_case )
if shape is not None:
UpperCAmelCase_ = z_q.view(__snake_case )
# reshape back to match original input shape
UpperCAmelCase_ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class a ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Tuple , __snake_case : str , __snake_case : Any=False ):
UpperCAmelCase_ = parameters
UpperCAmelCase_ , UpperCAmelCase_ = torch.chunk(__snake_case , 2 , dim=1 )
UpperCAmelCase_ = torch.clamp(self.logvar , -30.0 , 20.0 )
UpperCAmelCase_ = deterministic
UpperCAmelCase_ = torch.exp(0.5 * self.logvar )
UpperCAmelCase_ = torch.exp(self.logvar )
if self.deterministic:
UpperCAmelCase_ = UpperCAmelCase_ = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def lowerCamelCase_ ( self : Optional[int] , __snake_case : str = None ):
# make sure sample is on the same device as the parameters and has same dtype
UpperCAmelCase_ = randn_tensor(
self.mean.shape , generator=__snake_case , device=self.parameters.device , dtype=self.parameters.dtype )
UpperCAmelCase_ = self.mean + self.std * sample
return x
def lowerCamelCase_ ( self : Optional[Any] , __snake_case : str=None ):
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def lowerCamelCase_ ( self : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Any=[1, 2, 3] ):
if self.deterministic:
return torch.Tensor([0.0] )
UpperCAmelCase_ = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=__snake_case )
def lowerCamelCase_ ( self : List[str] ):
return self.mean
| 366 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[dict, list, tuple, torch.Tensor] ) -> List[Tuple[int, ...]]:
UpperCAmelCase_ = []
if isinstance(__UpperCamelCase , __UpperCamelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : Tuple[int, ...] ) -> Tuple[int, ...]:
UpperCAmelCase_ = []
for d in reversed(__UpperCamelCase ):
idx.append(flat_idx % d )
UpperCAmelCase_ = flat_idx // d
return tuple(reversed(__UpperCamelCase ) )
@torch.jit.ignore
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Sequence[int] , __UpperCamelCase : Sequence[int] , __UpperCamelCase : Sequence[int] , __UpperCamelCase : Optional[Sequence[bool]] = None , __UpperCamelCase : Optional[Sequence[bool]] = None , ) -> List[Tuple[slice, ...]]:
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(__UpperCamelCase : List[bool] ) -> None:
UpperCAmelCase_ = True
for i in range(len(__UpperCamelCase ) ):
UpperCAmelCase_ = -1 * (i + 1)
l[reversed_idx] &= tally
UpperCAmelCase_ = l[reversed_idx]
if start_edges is None:
UpperCAmelCase_ = [s == 0 for s in start]
reduce_edge_list(__UpperCamelCase )
if end_edges is None:
UpperCAmelCase_ = [e == (d - 1) for e, d in zip(__UpperCamelCase , __UpperCamelCase )]
reduce_edge_list(__UpperCamelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(__UpperCamelCase ) == 0:
return [()]
elif len(__UpperCamelCase ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
UpperCAmelCase_ = []
UpperCAmelCase_ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(__UpperCamelCase , __UpperCamelCase ):
if s == e:
path_list.append(slice(__UpperCamelCase , s + 1 ) )
else:
break
UpperCAmelCase_ = tuple(__UpperCamelCase )
UpperCAmelCase_ = len(__UpperCamelCase )
# start == end, and we're done
if divergence_idx == len(__UpperCamelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
UpperCAmelCase_ = start[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
UpperCAmelCase_ = end[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
UpperCAmelCase_ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : torch.Tensor , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int ) -> torch.Tensor:
UpperCAmelCase_ = t.shape[:no_batch_dims]
UpperCAmelCase_ = list(_flat_idx_to_idx(__UpperCamelCase , __UpperCamelCase ) )
# _get_minimal_slice_set is inclusive
UpperCAmelCase_ = list(_flat_idx_to_idx(flat_end - 1 , __UpperCamelCase ) )
# Get an ordered list of slices to perform
UpperCAmelCase_ = _get_minimal_slice_set(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
UpperCAmelCase_ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Callable , __UpperCamelCase : Dict[str, Any] , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : bool = False , __UpperCamelCase : Any = None , __UpperCamelCase : bool = False , ) -> Any:
if not (len(__UpperCamelCase ) > 0):
raise ValueError('''Must provide at least one input''' )
UpperCAmelCase_ = [shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase )]
UpperCAmelCase_ = tuple([max(__UpperCamelCase ) for s in zip(*__UpperCamelCase )] )
def _prep_inputs(__UpperCamelCase : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
UpperCAmelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
UpperCAmelCase_ = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
UpperCAmelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
UpperCAmelCase_ = tensor_tree_map(_prep_inputs , __UpperCamelCase )
UpperCAmelCase_ = None
if _out is not None:
UpperCAmelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
UpperCAmelCase_ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
UpperCAmelCase_ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(__UpperCamelCase : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
UpperCAmelCase_ = 0
UpperCAmelCase_ = prepped_outputs
for _ in range(__UpperCamelCase ):
# Chunk the input
if not low_mem:
UpperCAmelCase_ = _select_chunk
else:
UpperCAmelCase_ = partial(
_chunk_slice , flat_start=__UpperCamelCase , flat_end=min(__UpperCamelCase , i + chunk_size ) , no_batch_dims=len(__UpperCamelCase ) , )
UpperCAmelCase_ = tensor_tree_map(__UpperCamelCase , __UpperCamelCase )
# Run the layer on the chunk
UpperCAmelCase_ = layer(**__UpperCamelCase )
# Allocate space for the output
if out is None:
UpperCAmelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , __UpperCamelCase )
# Put the chunk in its pre-allocated space
if isinstance(__UpperCamelCase , __UpperCamelCase ):
def assign(__UpperCamelCase : dict , __UpperCamelCase : dict ) -> None:
for k, v in da.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
assign(__UpperCamelCase , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
UpperCAmelCase_ = da[k]
assign(__UpperCamelCase , __UpperCamelCase )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
for xa, xa in zip(__UpperCamelCase , __UpperCamelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
UpperCAmelCase_ = xa
elif isinstance(__UpperCamelCase , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
UpperCAmelCase_ = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
UpperCAmelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.view(orig_batch_dims + t.shape[1:] ) , __UpperCamelCase )
return out
class a :
'''simple docstring'''
def __init__( self : List[Any] , __snake_case : int = 5_12 , ):
UpperCAmelCase_ = max_chunk_size
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def lowerCamelCase_ ( self : List[Any] , __snake_case : Callable , __snake_case : tuple , __snake_case : int ):
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
UpperCAmelCase_ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
UpperCAmelCase_ = [c for c in candidates if c > min_chunk_size]
UpperCAmelCase_ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(__snake_case : int ) -> bool:
try:
with torch.no_grad():
fn(*__snake_case , chunk_size=__snake_case )
return True
except RuntimeError:
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = len(__snake_case ) - 1
while i > min_viable_chunk_size_index:
UpperCAmelCase_ = test_chunk_size(candidates[i] )
if not viable:
UpperCAmelCase_ = (min_viable_chunk_size_index + i) // 2
else:
UpperCAmelCase_ = i
UpperCAmelCase_ = (i + len(__snake_case ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def lowerCamelCase_ ( self : int , __snake_case : Iterable , __snake_case : Iterable ):
UpperCAmelCase_ = True
for aa, aa in zip(__snake_case , __snake_case ):
assert type(__snake_case ) == type(__snake_case )
if isinstance(__snake_case , (list, tuple) ):
consistent &= self._compare_arg_caches(__snake_case , __snake_case )
elif isinstance(__snake_case , __snake_case ):
UpperCAmelCase_ = [v for _, v in sorted(aa.items() , key=lambda __snake_case : x[0] )]
UpperCAmelCase_ = [v for _, v in sorted(aa.items() , key=lambda __snake_case : x[0] )]
consistent &= self._compare_arg_caches(__snake_case , __snake_case )
else:
consistent &= aa == aa
return consistent
def lowerCamelCase_ ( self : str , __snake_case : Callable , __snake_case : tuple , __snake_case : int , ):
UpperCAmelCase_ = True
UpperCAmelCase_ = tree_map(lambda __snake_case : a.shape if isinstance(__snake_case , torch.Tensor ) else a , __snake_case , __snake_case )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(__snake_case )
UpperCAmelCase_ = self._compare_arg_caches(self.cached_arg_data , __snake_case )
else:
# Otherwise, we can reuse the precomputed value
UpperCAmelCase_ = False
if not consistent:
UpperCAmelCase_ = self._determine_favorable_chunk_size(
__snake_case , __snake_case , __snake_case , )
UpperCAmelCase_ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 177 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
_A : Tuple = []
_A : Union[str, Any] = []
_A : Optional[int] = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+""": 1,
"""-""": 1,
} # Priority of each operator
_A : Dict = len(snake_case_ ) if (len(snake_case_ ) > 7) else 7
# Print table header for output
print(
"""Symbol""".center(8 ),"""Stack""".center(snake_case_ ),"""Postfix""".center(snake_case_ ),sep=""" | """,)
print("""-""" * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(snake_case_ ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(snake_case_ ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(snake_case_ ) == 0:
stack.append(snake_case_ ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(snake_case_ ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(snake_case_ ) # push x to stack
print(
x.center(8 ),("""""".join(snake_case_ )).ljust(snake_case_ ),("""""".join(snake_case_ )).ljust(snake_case_ ),sep=""" | """,) # Output in tabular format
while len(snake_case_ ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
""" """.center(8 ),("""""".join(snake_case_ )).ljust(snake_case_ ),("""""".join(snake_case_ )).ljust(snake_case_ ),sep=""" | """,) # Output in tabular format
return "".join(snake_case_ ) # return Postfix as str
def lowerCAmelCase_ ( snake_case_ ):
_A : str = list(infix[::-1] ) # reverse the infix equation
for i in range(len(snake_case_ ) ):
if infix[i] == "(":
_A : Any = """)""" # change "(" to ")"
elif infix[i] == ")":
_A : int = """(""" # change ")" to "("
return (infix_2_postfix("""""".join(snake_case_ ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
_snake_case = input("\nEnter an Infix Equation = ") # Input an Infix equation
_snake_case = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| 26 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Union[str, Any] = []
A : Union[str, Any] = []
for i in range(self.num_layers ):
A : Any = self.in_channels if i == 0 else self.out_channels
A : Optional[Any] = FlaxResnetBlockaD(
in_channels=SCREAMING_SNAKE_CASE , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : Optional[int] = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = resnets
A : Union[str, Any] = attentions
if self.add_downsample:
A : int = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Union[str, Any]:
"""simple docstring"""
A : Optional[Any] = ()
for resnet, attn in zip(self.resnets , self.attentions ):
A : int = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
A : Dict = attn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
if self.add_downsample:
A : Optional[Any] = self.downsamplers_a(SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
return hidden_states, output_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Optional[Any] = []
for i in range(self.num_layers ):
A : Optional[Any] = self.in_channels if i == 0 else self.out_channels
A : List[str] = FlaxResnetBlockaD(
in_channels=SCREAMING_SNAKE_CASE , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : Dict = resnets
if self.add_downsample:
A : Dict = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Optional[Any]:
"""simple docstring"""
A : str = ()
for resnet in self.resnets:
A : Optional[int] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
if self.add_downsample:
A : Optional[int] = self.downsamplers_a(SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
return hidden_states, output_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = []
A : Optional[int] = []
for i in range(self.num_layers ):
A : str = self.in_channels if (i == self.num_layers - 1) else self.out_channels
A : Dict = self.prev_output_channel if i == 0 else self.out_channels
A : List[str] = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : int = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(SCREAMING_SNAKE_CASE )
A : Dict = resnets
A : Optional[Any] = attentions
if self.add_upsample:
A : Optional[int] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Optional[int]:
"""simple docstring"""
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
A : List[str] = res_hidden_states_tuple[-1]
A : int = res_hidden_states_tuple[:-1]
A : List[str] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
A : Union[str, Any] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
A : Tuple = attn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
if self.add_upsample:
A : Dict = self.upsamplers_a(SCREAMING_SNAKE_CASE )
return hidden_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : int = []
for i in range(self.num_layers ):
A : List[Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
A : List[str] = self.prev_output_channel if i == 0 else self.out_channels
A : str = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : List[Any] = resnets
if self.add_upsample:
A : Optional[Any] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Tuple:
"""simple docstring"""
for resnet in self.resnets:
# pop res hidden states
A : Optional[int] = res_hidden_states_tuple[-1]
A : Optional[Any] = res_hidden_states_tuple[:-1]
A : List[Any] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
A : Optional[Any] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
if self.add_upsample:
A : List[str] = self.upsamplers_a(SCREAMING_SNAKE_CASE )
return hidden_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = 1
__magic_name__ = False
__magic_name__ = False
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : str = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
A : List[Any] = []
for _ in range(self.num_layers ):
A : int = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : List[str] = resnets
A : List[str] = attentions
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Dict:
"""simple docstring"""
A : Optional[Any] = self.resnets[0](SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
A : Optional[int] = attn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
A : Union[str, Any] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
return hidden_states
| 3 | 0 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCamelCase : Union[str, Any] = namedtuple(
'''_TestCommandArgs''',
[
'''dataset''',
'''name''',
'''cache_dir''',
'''data_dir''',
'''all_configs''',
'''save_infos''',
'''ignore_verifications''',
'''force_redownload''',
'''clear_cache''',
],
defaults=[None, None, None, False, False, False, False, False],
)
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Dict = _TestCommandArgs(dataset=A_ , all_configs=A_ , save_infos=A_ )
lowerCAmelCase__ : Optional[int] = TestCommand(*A_ )
test_command.run()
lowerCAmelCase__ : int = os.path.join(A_ , '''README.md''' )
assert os.path.exists(A_ )
lowerCAmelCase__ : List[Any] = DatasetInfosDict.from_directory(A_ )
lowerCAmelCase__ : List[str] = DatasetInfosDict(
{
'''default''': DatasetInfo(
features=Features(
{
'''tokens''': Sequence(Value('''string''' ) ),
'''ner_tags''': Sequence(
ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC'''] ) ),
'''langs''': Sequence(Value('''string''' ) ),
'''spans''': Sequence(Value('''string''' ) ),
} ) , splits=[
{
'''name''': '''train''',
'''num_bytes''': 2_35_15_63,
'''num_examples''': 1_00_00,
},
{
'''name''': '''validation''',
'''num_bytes''': 23_84_18,
'''num_examples''': 10_00,
},
] , download_size=3_94_06_80 , dataset_size=2_58_99_81 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = getattr(dataset_infos['''default'''] , A_ ), getattr(expected_dataset_infos['''default'''] , A_ )
if key == "num_bytes":
assert is_apercent_close(A_ , A_ )
elif key == "splits":
assert list(A_ ) == list(A_ )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 74 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 74 | 1 |
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_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = 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.' )
lowerCAmelCase__ : Dict = 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.' )
lowerCAmelCase__ : List[Any] = components[:-1] + [test_fn.replace('''.py''' , '''''' )]
lowerCAmelCase__ : Optional[Any] = '''.'''.join(lowerCAmelCase_ )
return test_module_path
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : str = get_module_path(lowerCAmelCase_ )
lowerCAmelCase__ : List[Any] = importlib.import_module(lowerCAmelCase_ )
return test_module
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : Optional[int] = 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 _a : x.__name__ )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : str = []
lowerCAmelCase__ : Union[str, Any] = get_test_module(lowerCAmelCase_ )
for attr in dir(lowerCAmelCase_ ):
lowerCAmelCase__ : int = 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).
lowerCAmelCase__ : Union[str, Any] = getattr(lowerCAmelCase_ , '''all_model_classes''' , [] )
if len(lowerCAmelCase_ ) > 0:
test_classes.append(lowerCAmelCase_ )
# sort with class names
return sorted(lowerCAmelCase_ , key=lambda _a : x.__name__ )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : str = get_test_classes(lowerCAmelCase_ )
lowerCAmelCase__ : List[Any] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(lowerCAmelCase_ , key=lambda _a : x.__name__ )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = test_class()
if hasattr(lowerCAmelCase_ , '''setUp''' ):
test.setUp()
lowerCAmelCase__ : str = 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:
lowerCAmelCase__ : str = test.model_tester.__class__
return model_tester
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = get_test_classes(lowerCAmelCase_ )
lowerCAmelCase__ : Dict = []
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 _a : x.__name__ )
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = get_test_classes_for_model(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ : Optional[Any] = []
for test_class in test_classes:
lowerCAmelCase__ : List[str] = 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 _a : x.__name__ )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Any = get_test_classes(lowerCAmelCase_ )
lowerCAmelCase__ : Tuple = {test_class: get_model_tester_from_test_class(lowerCAmelCase_ ) for test_class in test_classes}
return test_tester_mapping
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = get_model_classes(lowerCAmelCase_ )
lowerCAmelCase__ : Dict = {
model_class: get_test_classes_for_model(lowerCAmelCase_ , lowerCAmelCase_ ) for model_class in model_classes
}
return model_test_mapping
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = get_model_classes(lowerCAmelCase_ )
lowerCAmelCase__ : Union[str, Any] = {
model_class: get_tester_classes_for_model(lowerCAmelCase_ , lowerCAmelCase_ ) for model_class in model_classes
}
return model_to_tester_mapping
def lowerCamelCase_ ( _a ):
"""simple docstring"""
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
| 131 |
def _lowerCAmelCase ( lowerCAmelCase_ :int = 1_000 )->int:
'''simple docstring'''
snake_case_ , snake_case_ = 1, 1
snake_case_ = 2
while True:
snake_case_ = 0
snake_case_ = fa + fa
snake_case_ , snake_case_ = fa, f
index += 1
for _ in str(lowerCAmelCase_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 159 | 0 |
"""simple docstring"""
from __future__ import annotations
class lowerCAmelCase__ :
def __init__( self : str , snake_case__ : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = order
# a_{0} ... a_{k}
UpperCAmelCase__ : Any = [1.0] + [0.0] * order
# b_{0} ... b_{k}
UpperCAmelCase__ : List[Any] = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
UpperCAmelCase__ : int = [0.0] * self.order
# y[n-1] ... y[n-k]
UpperCAmelCase__ : List[str] = [0.0] * self.order
def __a ( self : List[Any] , snake_case__ : list[float] , snake_case__ : list[float] ):
'''simple docstring'''
if len(snake_case__ ) < self.order:
UpperCAmelCase__ : int = [1.0, *a_coeffs]
if len(snake_case__ ) != self.order + 1:
UpperCAmelCase__ : Optional[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(snake_case__ )}'
)
raise ValueError(snake_case__ )
if len(snake_case__ ) != self.order + 1:
UpperCAmelCase__ : Any = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(snake_case__ )}'
)
raise ValueError(snake_case__ )
UpperCAmelCase__ : Any = a_coeffs
UpperCAmelCase__ : int = b_coeffs
def __a ( self : Dict , snake_case__ : float ):
'''simple docstring'''
UpperCAmelCase__ : str = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
UpperCAmelCase__ : Dict = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
UpperCAmelCase__ : Any = self.input_history[:-1]
UpperCAmelCase__ : List[str] = self.output_history[:-1]
UpperCAmelCase__ : Optional[int] = sample
UpperCAmelCase__ : str = result
return result
| 298 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] )-> Any:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def SCREAMING_SNAKE_CASE__ ( )-> List[Any]:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def SCREAMING_SNAKE_CASE__ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : int = "mock-s3-bucket"
UpperCAmelCase__ : Any = f's3://{mock_bucket}'
UpperCAmelCase__ : Tuple = extract_path_from_uri(snake_case )
assert dataset_path.startswith("s3://" ) is False
UpperCAmelCase__ : str = "./local/path"
UpperCAmelCase__ : Union[str, Any] = extract_path_from_uri(snake_case )
assert dataset_path == new_dataset_path
def SCREAMING_SNAKE_CASE__ ( snake_case : Any )-> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = is_remote_filesystem(snake_case )
assert is_remote is True
UpperCAmelCase__ : str = fsspec.filesystem("file" )
UpperCAmelCase__ : Optional[Any] = is_remote_filesystem(snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , snake_case )
def SCREAMING_SNAKE_CASE__ ( snake_case : Union[str, Any] , snake_case : Any , snake_case : List[str] , snake_case : Optional[int] , snake_case : Union[str, Any] , snake_case : Optional[Any] , snake_case : int )-> int:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
UpperCAmelCase__ : Dict = input_paths[compression_fs_class.protocol]
if input_path is None:
UpperCAmelCase__ : Optional[Any] = f'for \'{compression_fs_class.protocol}\' compression protocol, '
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case )
UpperCAmelCase__ : Optional[Any] = fsspec.filesystem(compression_fs_class.protocol , fo=snake_case )
assert isinstance(snake_case , snake_case )
UpperCAmelCase__ : Union[str, Any] = os.path.basename(snake_case )
UpperCAmelCase__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(snake_case , "r" , encoding="utf-8" ) as f, open(snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple , snake_case : Dict , snake_case : Tuple )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : List[str] = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
UpperCAmelCase__ : int = compressed_file_paths[protocol]
UpperCAmelCase__ : Any = "dataset.jsonl"
UpperCAmelCase__ : Any = f'{protocol}://{member_file_path}::{compressed_file_path}'
UpperCAmelCase__ , *UpperCAmelCase__ : Optional[int] = fsspec.get_fs_token_paths(snake_case )
assert fs.isfile(snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def SCREAMING_SNAKE_CASE__ ( snake_case : List[str] , snake_case : Dict , snake_case : Dict , snake_case : Dict )-> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = hf_api.dataset_info(snake_case , token=snake_case )
UpperCAmelCase__ : str = HfFileSystem(repo_info=snake_case , token=snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def SCREAMING_SNAKE_CASE__ ( )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(snake_case , snake_case , clobber=snake_case )
with pytest.warns(snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(snake_case ) == 1
assert (
str(warning_info[0].message )
== f'A filesystem protocol was already set for {protocol} and will be overwritten.'
)
| 298 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
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 TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=13 , __lowercase=30 , __lowercase=2 , __lowercase=3 , __lowercase=True , __lowercase=True , __lowercase=32 , __lowercase=2 , __lowercase=4 , __lowercase=37 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=10 , __lowercase=0.02 , __lowercase=3 , __lowercase=0.6 , __lowercase=None , ) -> Tuple:
__UpperCamelCase :List[str] = parent
__UpperCamelCase :List[Any] = batch_size
__UpperCamelCase :str = image_size
__UpperCamelCase :List[Any] = patch_size
__UpperCamelCase :List[str] = num_channels
__UpperCamelCase :Union[str, Any] = is_training
__UpperCamelCase :List[str] = use_labels
__UpperCamelCase :Tuple = hidden_size
__UpperCamelCase :str = num_hidden_layers
__UpperCamelCase :List[Any] = num_attention_heads
__UpperCamelCase :Optional[Any] = intermediate_size
__UpperCamelCase :List[str] = hidden_act
__UpperCamelCase :str = hidden_dropout_prob
__UpperCamelCase :List[str] = attention_probs_dropout_prob
__UpperCamelCase :Union[str, Any] = type_sequence_label_size
__UpperCamelCase :List[str] = initializer_range
__UpperCamelCase :Optional[int] = mask_ratio
__UpperCamelCase :Optional[int] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__UpperCamelCase :Optional[Any] = (image_size // patch_size) ** 2
__UpperCamelCase :Any = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCamelCase :Tuple = None
if self.use_labels:
__UpperCamelCase :str = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCamelCase :List[Any] = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self) -> Tuple:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__lowercase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> Any:
__UpperCamelCase :Any = TFViTMAEModel(config=__lowercase)
__UpperCamelCase :Union[str, Any] = model(__lowercase , training=__lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> List[str]:
__UpperCamelCase :str = TFViTMAEForPreTraining(__lowercase)
__UpperCamelCase :str = model(__lowercase , training=__lowercase)
# expected sequence length = num_patches
__UpperCamelCase :List[str] = (self.image_size // self.patch_size) ** 2
__UpperCamelCase :Union[str, Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
# test greyscale images
__UpperCamelCase :List[str] = 1
__UpperCamelCase :List[str] = TFViTMAEForPreTraining(__lowercase)
__UpperCamelCase :int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__UpperCamelCase :Dict = model(__lowercase , training=__lowercase)
__UpperCamelCase :List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Optional[int] = self.prepare_config_and_inputs()
((__UpperCamelCase) , (__UpperCamelCase) , (__UpperCamelCase)) :List[str] = config_and_inputs
__UpperCamelCase :Dict = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : str = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
a__ : Dict = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
a__ : Tuple = False
a__ : str = False
a__ : Optional[Any] = False
a__ : Union[str, Any] = False
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :List[str] = TFViTMAEModelTester(self)
__UpperCamelCase :List[str] = ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37)
def UpperCamelCase__ ( self) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
def UpperCamelCase__ ( self) -> str:
pass
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase , __UpperCamelCase :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase :List[Any] = model_class(__lowercase)
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer))
__UpperCamelCase :Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , tf.keras.layers.Layer))
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase , __UpperCamelCase :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase :Tuple = model_class(__lowercase)
__UpperCamelCase :int = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCamelCase :Optional[int] = [*signature.parameters.keys()]
__UpperCamelCase :Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase)
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase)
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__lowercase)
def UpperCamelCase__ ( self) -> Optional[Any]:
# make the mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :Tuple = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
__UpperCamelCase :str = model_class(__lowercase)
__UpperCamelCase :Optional[int] = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Dict = model(__lowercase , noise=__lowercase)
__UpperCamelCase :int = copy.deepcopy(self._prepare_for_class(__lowercase , __lowercase))
__UpperCamelCase :Union[str, Any] = model(**__lowercase , noise=__lowercase)
__UpperCamelCase :Tuple = outputs_dict[0].numpy()
__UpperCamelCase :Union[str, Any] = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)) , 1E-6)
def UpperCamelCase__ ( self) -> Optional[int]:
# make the mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :str = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :int = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :str = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
def prepare_numpy_arrays(__lowercase):
__UpperCamelCase :Optional[int] = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__lowercase):
__UpperCamelCase :Optional[Any] = v.numpy()
else:
__UpperCamelCase :Optional[int] = np.array(__lowercase)
return inputs_np_dict
for model_class in self.all_model_classes:
__UpperCamelCase :int = model_class(__lowercase)
__UpperCamelCase :Tuple = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Any = prepare_numpy_arrays(__lowercase)
__UpperCamelCase :Any = model(__lowercase , noise=__lowercase)
__UpperCamelCase :Tuple = model(**__lowercase , noise=__lowercase)
self.assert_outputs_same(__lowercase , __lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> List[Any]:
# make masks reproducible
np.random.seed(2)
__UpperCamelCase :Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
__UpperCamelCase :Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
__UpperCamelCase :Dict = tf.constant(__lowercase)
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__UpperCamelCase :Any = tf_noise
super().check_pt_tf_models(__lowercase , __lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Tuple:
# make mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :Optional[int] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__),)
for module_member_name in dir(__lowercase)
if module_member_name.endswith('''MainLayer''')
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('''MainLayer''')] == model_class.__name__[: -len('''Model''')]
for module_member in (getattr(__lowercase , __lowercase),)
if isinstance(__lowercase , __lowercase)
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__lowercase , '''_keras_serializable''' , __lowercase)
}
__UpperCamelCase :Union[str, Any] = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :List[str] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
__UpperCamelCase :str = tf.convert_to_tensor(__lowercase)
inputs_dict.update({'''noise''': noise})
for main_layer_class in tf_main_layer_classes:
__UpperCamelCase :Optional[int] = main_layer_class(__lowercase)
__UpperCamelCase :Optional[Any] = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
__UpperCamelCase :Dict = tf.keras.Model(__lowercase , outputs=main_layer(__lowercase))
__UpperCamelCase :str = model(__lowercase)
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :str = os.path.join(__lowercase , '''keras_model.h5''')
model.save(__lowercase)
__UpperCamelCase :List[Any] = tf.keras.models.load_model(
__lowercase , custom_objects={main_layer_class.__name__: main_layer_class})
assert isinstance(__lowercase , tf.keras.Model)
__UpperCamelCase :Optional[Any] = model(__lowercase)
self.assert_outputs_same(__lowercase , __lowercase)
@slow
def UpperCamelCase__ ( self) -> Dict:
# make mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :Optional[Any] = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :Any = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
__UpperCamelCase :Optional[int] = model_class(__lowercase)
__UpperCamelCase :Union[str, Any] = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Optional[int] = model(__lowercase , noise=__lowercase)
if model_class.__name__ == "TFViTMAEModel":
__UpperCamelCase :Any = outputs.last_hidden_state.numpy()
__UpperCamelCase :Optional[Any] = 0
else:
__UpperCamelCase :List[str] = outputs.logits.numpy()
__UpperCamelCase :Optional[int] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowercase , saved_model=__lowercase)
__UpperCamelCase :Optional[int] = model_class.from_pretrained(__lowercase)
__UpperCamelCase :List[str] = model(__lowercase , noise=__lowercase)
if model_class.__name__ == "TFViTMAEModel":
__UpperCamelCase :List[Any] = after_outputs['''last_hidden_state'''].numpy()
__UpperCamelCase :List[Any] = 0
else:
__UpperCamelCase :Any = after_outputs['''logits'''].numpy()
__UpperCamelCase :Tuple = 0
__UpperCamelCase :Any = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(__lowercase , 1E-5)
def UpperCamelCase__ ( self) -> Union[str, Any]:
# make mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :str = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
__UpperCamelCase :Tuple = model_class(__lowercase)
__UpperCamelCase :Any = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Tuple = model(__lowercase , noise=__lowercase)
__UpperCamelCase :List[Any] = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__lowercase)
__UpperCamelCase :Optional[Any] = model_class.from_config(model.get_config())
# make sure it also accepts a normal config
__UpperCamelCase :Any = model_class.from_config(model.config)
__UpperCamelCase :List[Any] = new_model(__lowercase) # Build model
new_model.set_weights(model.get_weights())
__UpperCamelCase :str = new_model(__lowercase , noise=__lowercase)
self.assert_outputs_same(__lowercase , __lowercase)
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''')
def UpperCamelCase__ ( self) -> Dict:
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''')
def UpperCamelCase__ ( self) -> Any:
pass
@slow
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :List[Any] = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''')
self.assertIsNotNone(__lowercase)
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self) -> Optional[Any]:
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''') if is_vision_available() else None
@slow
def UpperCamelCase__ ( self) -> List[str]:
# make random mask reproducible across the PT and TF model
np.random.seed(2)
__UpperCamelCase :Optional[Any] = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''')
__UpperCamelCase :Optional[int] = self.default_image_processor
__UpperCamelCase :Optional[int] = prepare_img()
__UpperCamelCase :Optional[int] = image_processor(images=__lowercase , return_tensors='''tf''')
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
__UpperCamelCase :Union[str, Any] = ViTMAEConfig()
__UpperCamelCase :Union[str, Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
__UpperCamelCase :Tuple = np.random.uniform(size=(1, num_patches))
# forward pass
__UpperCamelCase :int = model(**__lowercase , noise=__lowercase)
# verify the logits
__UpperCamelCase :Optional[int] = tf.convert_to_tensor([1, 196, 768])
self.assertEqual(outputs.logits.shape , __lowercase)
__UpperCamelCase :List[Any] = tf.convert_to_tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])
tf.debugging.assert_near(outputs.logits[0, :3, :3] , __lowercase , atol=1E-4)
| 43 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import RemBertTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}
__snake_case = {
"""vocab_file""": {
"""google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
},
"""tokenizer_file""": {
"""google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
},
}
__snake_case = {
"""google/rembert""": 256,
}
__snake_case = """▁"""
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : List[str] = VOCAB_FILES_NAMES
__UpperCAmelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : str = RemBertTokenizer
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=False , UpperCamelCase__="[CLS]" , UpperCamelCase__="[SEP]" , UpperCamelCase__="<unk>" , UpperCamelCase__="[SEP]" , UpperCamelCase__="<pad>" , UpperCamelCase__="[CLS]" , UpperCamelCase__="[MASK]" , **UpperCamelCase__ , ) -> Tuple:
'''simple docstring'''
snake_case : Dict = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else mask_token
super().__init__(
UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , do_lower_case=UpperCamelCase__ , remove_space=UpperCamelCase__ , keep_accents=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case : int = do_lower_case
snake_case : Union[str, Any] = remove_space
snake_case : Optional[int] = keep_accents
snake_case : Any = vocab_file
snake_case : Any = False if not self.vocab_file else True
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
'''simple docstring'''
snake_case : Optional[Any] = [self.sep_token_id]
snake_case : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if 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(UpperCamelCase__ )) + [1] + ([0] * len(UpperCamelCase__ )) + [1]
return [1] + ([0] * len(UpperCamelCase__ )) + [1]
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
'''simple docstring'''
snake_case : Union[str, Any] = [self.sep_token_id]
snake_case : Optional[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]
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(UpperCamelCase__ ):
logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__ ) )
return
snake_case : str = 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__ ):
copyfile(self.vocab_file , UpperCamelCase__ )
return (out_vocab_file,)
| 203 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
_lowerCamelCase : List[str] = """\
Text data.
Second line of data."""
_lowerCamelCase : Any = """file"""
@pytest.fixture(scope='session' )
def a_ ( __lowercase : Optional[int] ) -> str:
_snake_case = tmp_path_factory.mktemp('data' ) / (FILE_PATH + '.zstd')
_snake_case = bytes(__lowercase , 'utf-8' )
with zstd.open(__lowercase , 'wb' ) as f:
f.write(__lowercase )
return path
@pytest.fixture
def a_ ( __lowercase : Any ) -> Optional[int]:
with open(os.path.join(tmpfs.local_root_dir , __lowercase ) , 'w' ) as f:
f.write(__lowercase )
return FILE_PATH
@pytest.mark.parametrize('compression_format' , ['gzip', 'xz', 'zstd'] )
def a_ ( __lowercase : Dict , __lowercase : Any , __lowercase : str , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Optional[Any] ) -> List[Any]:
_snake_case = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
_snake_case = input_paths[compression_format]
_snake_case = tmp_path / 'cache'
_snake_case = DownloadConfig(cache_dir=__lowercase , extract_compressed_file=__lowercase )
_snake_case = cached_path(__lowercase , download_config=__lowercase )
with open(__lowercase ) as f:
_snake_case = f.read()
with open(__lowercase ) as f:
_snake_case = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted' , [True, False] )
@pytest.mark.parametrize('default_cache_dir' , [True, False] )
def a_ ( __lowercase : int , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Tuple , __lowercase : Union[str, Any] ) -> Optional[Any]:
_snake_case = 'custom_cache'
_snake_case = 'custom_extracted_dir'
_snake_case = tmp_path / 'custom_extracted_path'
if default_extracted:
_snake_case = ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
else:
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR' , __lowercase )
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(__lowercase ) )
_snake_case = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_snake_case = xz_file
_snake_case = (
DownloadConfig(extract_compressed_file=__lowercase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=__lowercase )
)
_snake_case = cached_path(__lowercase , download_config=__lowercase )
assert Path(__lowercase ).parent.parts[-2:] == expected
def a_ ( __lowercase : Tuple ) -> List[Any]:
# absolute path
_snake_case = str(Path(__lowercase ).resolve() )
assert cached_path(__lowercase ) == text_file
# relative path
_snake_case = str(Path(__lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(__lowercase ) == text_file
def a_ ( __lowercase : Union[str, Any] ) -> List[str]:
# absolute path
_snake_case = str(tmp_path.resolve() / '__missing_file__.txt' )
with pytest.raises(__lowercase ):
cached_path(__lowercase )
# relative path
_snake_case = './__missing_file__.txt'
with pytest.raises(__lowercase ):
cached_path(__lowercase )
def a_ ( __lowercase : Optional[int] ) -> List[str]:
_snake_case = get_from_cache(f'''tmp://{tmpfs_file}''' )
with open(__lowercase ) as f:
_snake_case = f.read()
assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE' , __lowercase )
def a_ ( ) -> Optional[Any]:
with pytest.raises(__lowercase ):
cached_path('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , __lowercase )
def a_ ( __lowercase : Optional[Any] ) -> Optional[Any]:
_snake_case = tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(__lowercase ):
http_get('https://huggingface.co' , temp_file=__lowercase )
with pytest.raises(__lowercase ):
http_head('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , __lowercase )
def a_ ( __lowercase : List[Any] ) -> List[Any]:
_snake_case = tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(__lowercase ):
ftp_get('ftp://huggingface.co' , temp_file=__lowercase )
with pytest.raises(__lowercase ):
ftp_head('ftp://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , __lowercase )
def a_ ( __lowercase : Tuple ) -> str:
_snake_case = tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(__lowercase ):
fsspec_get('s3://huggingface.co' , temp_file=__lowercase )
with pytest.raises(__lowercase ):
fsspec_head('s3://huggingface.co' ) | 359 |
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 : int = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''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 : List[str] = {
'''b0''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1_408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1_536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1_792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2_048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2_304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2_560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def a_ ( __lowercase : int ) -> Optional[int]:
_snake_case = EfficientNetConfig()
_snake_case = CONFIG_MAP[model_name]['hidden_dim']
_snake_case = CONFIG_MAP[model_name]['width_coef']
_snake_case = CONFIG_MAP[model_name]['depth_coef']
_snake_case = CONFIG_MAP[model_name]['image_size']
_snake_case = CONFIG_MAP[model_name]['dropout_rate']
_snake_case = CONFIG_MAP[model_name]['dw_padding']
_snake_case = 'huggingface/label-files'
_snake_case = 'imagenet-1k-id2label.json'
_snake_case = 1_000
_snake_case = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='dataset' ) , 'r' ) )
_snake_case = {int(__lowercase ): v for k, v in idalabel.items()}
_snake_case = idalabel
_snake_case = {v: k for k, v in idalabel.items()}
return config
def a_ ( ) -> Any:
_snake_case = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_snake_case = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
return im
def a_ ( __lowercase : Union[str, Any] ) -> Tuple:
_snake_case = CONFIG_MAP[model_name]['image_size']
_snake_case = EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=__lowercase , )
return preprocessor
def a_ ( __lowercase : str ) -> List[Any]:
_snake_case = [v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
_snake_case = sorted(set(__lowercase ) )
_snake_case = len(__lowercase )
_snake_case = {b: str(__lowercase ) for b, i in zip(__lowercase , range(__lowercase ) )}
_snake_case = []
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:
_snake_case = 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') )
_snake_case = {}
for item in rename_keys:
if item[0] in original_param_names:
_snake_case = 'efficientnet.' + item[1]
_snake_case = 'classifier.weight'
_snake_case = 'classifier.bias'
return key_mapping
def a_ ( __lowercase : Any , __lowercase : Any , __lowercase : Any ) -> Optional[Any]:
for key, value in tf_params.items():
if "normalization" in key:
continue
_snake_case = key_mapping[key]
if "_conv" in key and "kernel" in key:
_snake_case = torch.from_numpy(__lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
_snake_case = torch.from_numpy(__lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
_snake_case = torch.from_numpy(np.transpose(__lowercase ) )
else:
_snake_case = torch.from_numpy(__lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__lowercase )
@torch.no_grad()
def a_ ( __lowercase : List[Any] , __lowercase : Any , __lowercase : int , __lowercase : str ) -> Dict:
_snake_case = model_classes[model_name](
include_top=__lowercase , weights='imagenet' , input_tensor=__lowercase , input_shape=__lowercase , pooling=__lowercase , classes=1_000 , classifier_activation='softmax' , )
_snake_case = original_model.trainable_variables
_snake_case = original_model.non_trainable_variables
_snake_case = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_snake_case = param.numpy()
_snake_case = list(tf_params.keys() )
# Load HuggingFace model
_snake_case = get_efficientnet_config(__lowercase )
_snake_case = EfficientNetForImageClassification(__lowercase ).eval()
_snake_case = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
_snake_case = rename_keys(__lowercase )
replace_params(__lowercase , __lowercase , __lowercase )
# Initialize preprocessor and preprocess input image
_snake_case = convert_image_processor(__lowercase )
_snake_case = preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
_snake_case = hf_model(**__lowercase )
_snake_case = outputs.logits.detach().numpy()
# Original model inference
_snake_case = False
_snake_case = CONFIG_MAP[model_name]['image_size']
_snake_case = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
_snake_case = image.img_to_array(__lowercase )
_snake_case = np.expand_dims(__lowercase , axis=0 )
_snake_case = original_model.predict(__lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__lowercase , __lowercase , 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(__lowercase ):
os.mkdir(__lowercase )
# Save converted model and image processor
hf_model.save_pretrained(__lowercase )
preprocessor.save_pretrained(__lowercase )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
_snake_case = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(__lowercase )
hf_model.push_to_hub(__lowercase )
if __name__ == "__main__":
_lowerCamelCase : int = 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 : List[str] = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub) | 130 | 0 |
import string
def __magic_name__ ( A : str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
a = ""
for symbol in message:
if symbol in string.ascii_uppercase:
a = string.ascii_uppercase.find(A )
a = num - key
if num < 0:
a = num + len(string.ascii_uppercase )
a = translated + string.ascii_uppercase[num]
else:
a = translated + symbol
print(F"""Decryption using Key #{key}: {translated}""" )
def __magic_name__ ( ):
'''simple docstring'''
a = input("Encrypted message: " )
a = message.upper()
decrypt(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 107 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for key, value in counts.items() if value > 1]
lowerCAmelCase_ = []
for duplicate_key in duplicates:
lowerCAmelCase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(_A ) > 1:
raise ValueError(
f"{duplicate_key} is present several times in the documentation table of content at "
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(_A , key=lambda _A : s["title"].lower() )
def __UpperCamelCase ( _A=False ):
with open(_A , encoding='''utf-8''' ) as f:
lowerCAmelCase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowerCAmelCase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowerCAmelCase_ = content[api_idx]['''sections''']
# Then to the model doc
lowerCAmelCase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowerCAmelCase_ = api_doc[model_idx]['''sections''']
lowerCAmelCase_ = [(idx, section) for idx, section in enumerate(_A ) if '''sections''' in section]
lowerCAmelCase_ = False
for idx, modality_doc in modalities_docs:
lowerCAmelCase_ = modality_doc['''sections''']
lowerCAmelCase_ = clean_model_doc_toc(_A )
if old_modality_doc != new_modality_doc:
lowerCAmelCase_ = True
if overwrite:
lowerCAmelCase_ = new_modality_doc
if diff:
if overwrite:
lowerCAmelCase_ = model_doc
lowerCAmelCase_ = api_doc
with open(_A , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(_A , allow_unicode=_A ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_A = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 278 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = '''Wav2Vec2FeatureExtractor'''
snake_case_ = '''AutoTokenizer'''
def __init__( self, lowercase_, lowercase_ ) -> str:
super().__init__(lowercase_, lowercase_ )
snake_case = self.feature_extractor
snake_case = False
@classmethod
def _lowerCamelCase ( cls, lowercase_, **lowercase_ ) -> Optional[Any]:
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_, )
snake_case = WavaVecaFeatureExtractor.from_pretrained(lowercase_, **lowercase_ )
snake_case = WavaVecaCTCTokenizer.from_pretrained(lowercase_, **lowercase_ )
return cls(feature_extractor=lowercase_, tokenizer=lowercase_ )
def __call__( self, *lowercase_, **lowercase_ ) -> Optional[Any]:
# For backward compatibility
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.' )
snake_case = kwargs.pop('raw_speech' )
else:
snake_case = kwargs.pop('audio', lowercase_ )
snake_case = kwargs.pop('sampling_rate', lowercase_ )
snake_case = kwargs.pop('text', lowercase_ )
if len(lowercase_ ) > 0:
snake_case = args[0]
snake_case = 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:
snake_case = self.feature_extractor(lowercase_, *lowercase_, sampling_rate=lowercase_, **lowercase_ )
if text is not None:
snake_case = self.tokenizer(lowercase_, **lowercase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['input_ids']
return inputs
def _lowerCamelCase ( self, *lowercase_, **lowercase_ ) -> int:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*lowercase_, **lowercase_ )
snake_case = kwargs.pop('input_features', lowercase_ )
snake_case = kwargs.pop('labels', lowercase_ )
if len(lowercase_ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if input_features is not None:
snake_case = self.feature_extractor.pad(lowercase_, *lowercase_, **lowercase_ )
if labels is not None:
snake_case = self.tokenizer.pad(lowercase_, **lowercase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
snake_case = labels['input_ids']
return input_features
def _lowerCamelCase ( self, *lowercase_, **lowercase_ ) -> Optional[int]:
return self.tokenizer.batch_decode(*lowercase_, **lowercase_ )
def _lowerCamelCase ( self, *lowercase_, **lowercase_ ) -> Optional[Any]:
return self.tokenizer.decode(*lowercase_, **lowercase_ )
@contextmanager
def _lowerCamelCase ( self ) -> Union[str, 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.' )
snake_case = True
snake_case = self.tokenizer
yield
snake_case = self.feature_extractor
snake_case = False
| 332 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in range(A )]
for i in my_list:
buckets[int(i - min_value )].append(A )
return [v for bucket in buckets for v in sorted(A )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 332 | 1 |
class _lowercase :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : int = ''
lowerCamelCase__ : List[Any] = ''
lowerCamelCase__ : Optional[Any] = []
def lowerCAmelCase ( self : Optional[int] , __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
lowerCamelCase__ : Optional[Any] = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
lowerCamelCase__ : Any = self.__min_dist_top_down_dp(__snake_case , n - 1 )
lowerCamelCase__ : Dict = self.__min_dist_top_down_dp(m - 1 , __snake_case )
lowerCamelCase__ : Any = self.__min_dist_top_down_dp(m - 1 , n - 1 )
lowerCamelCase__ : str = 1 + min(__snake_case , __snake_case , __snake_case )
return self.dp[m][n]
def lowerCAmelCase ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : str ):
'''simple docstring'''
lowerCamelCase__ : List[str] = worda
lowerCamelCase__ : Any = worda
lowerCamelCase__ : Union[str, Any] = [[-1 for _ in range(len(__snake_case ) )] for _ in range(len(__snake_case ) )]
return self.__min_dist_top_down_dp(len(__snake_case ) - 1 , len(__snake_case ) - 1 )
def lowerCAmelCase ( self : List[Any] , __lowerCamelCase : str , __lowerCamelCase : str ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = worda
lowerCamelCase__ : int = worda
lowerCamelCase__ : Union[str, Any] = len(__snake_case )
lowerCamelCase__ : Tuple = len(__snake_case )
lowerCamelCase__ : int = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
lowerCamelCase__ : str = j
elif j == 0: # second string is empty
lowerCamelCase__ : Union[str, Any] = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
lowerCamelCase__ : int = self.dp[i - 1][j - 1]
else:
lowerCamelCase__ : Optional[Any] = self.dp[i][j - 1]
lowerCamelCase__ : Union[str, Any] = self.dp[i - 1][j]
lowerCamelCase__ : Optional[int] = self.dp[i - 1][j - 1]
lowerCamelCase__ : str = 1 + min(__snake_case , __snake_case , __snake_case )
return self.dp[m][n]
if __name__ == "__main__":
A : Optional[Any] = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
A : Dict = input("Enter the first string: ").strip()
A : Union[str, Any] = input("Enter the second string: ").strip()
print()
print(f'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
print(f'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| 184 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 297 | 0 |
# 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 typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''dandelin/vilt-b32-finetuned-vqa'''
A__ = (
'''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
'''image containing the information, as well as a `question` which should be the question in English. It '''
'''returns a text that is the answer to the question.'''
)
A__ = '''image_qa'''
A__ = AutoProcessor
A__ = AutoModelForVisualQuestionAnswering
A__ = ['''image''', '''text''']
A__ = ['''text''']
def __init__(self : List[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""vision"""] )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : str , _UpperCAmelCase : "Image" , _UpperCAmelCase : str ) -> List[Any]:
"""simple docstring"""
return self.pre_processor(_UpperCAmelCase , _UpperCAmelCase , return_tensors="""pt""" )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
with torch.no_grad():
return self.model(**_UpperCAmelCase ).logits
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 146 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A :
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=13 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Dict=3 , _UpperCAmelCase : Dict=4 , _UpperCAmelCase : Union[str, Any]=[10, 20, 30, 40] , _UpperCAmelCase : Optional[int]=[2, 2, 3, 2] , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=37 , _UpperCAmelCase : str="gelu" , _UpperCAmelCase : Dict=10 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : List[Any]=["stage2", "stage3", "stage4"] , _UpperCAmelCase : List[Any]=[2, 3, 4] , _UpperCAmelCase : Optional[Any]=None , ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = num_stages
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_labels
lowercase__ = initializer_range
lowercase__ = out_features
lowercase__ = out_indices
lowercase__ = scope
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Any:
"""simple docstring"""
lowercase__ = ConvNextVaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase__ (self : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = ConvNextVaForImageClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = ConvNextVaBackbone(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase__ = None
lowercase__ = ConvNextVaBackbone(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase__ (self : List[Any] ) -> int:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
def lowerCamelCase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
A__ = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
A__ = False
def lowerCamelCase__ (self : int ) -> List[Any]:
"""simple docstring"""
lowercase__ = ConvNextVaModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : int ) -> Tuple:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""" )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""" )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""" )
def lowerCamelCase__ (self : int ) -> str:
"""simple docstring"""
pass
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_with_labels()
lowercase__ = True
if model_class.__name__ in [
*get_values(_UpperCAmelCase ),
*get_values(_UpperCAmelCase ),
]:
continue
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.train()
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
lowercase__ = model(**_UpperCAmelCase ).loss
loss.backward()
def lowerCamelCase__ (self : Optional[Any] ) -> int:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_with_labels()
lowercase__ = False
lowercase__ = True
if (
model_class.__name__
in [*get_values(_UpperCAmelCase ), *get_values(_UpperCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.gradient_checkpointing_enable()
model.train()
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
lowercase__ = model(**_UpperCAmelCase ).loss
loss.backward()
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCamelCase__ (self : int ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
def check_hidden_states_output(_UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str ):
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__ = self.model_tester.num_stages
self.assertEqual(len(_UpperCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )
@slow
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = ConvNextVaModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def UpperCamelCase ( ) -> int:
"""simple docstring"""
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ) if is_vision_available() else None
@slow
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
lowercase__ = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ).to(_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = preprocessor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
# verify the logits
lowercase__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor([0.9_996, 0.1_966, -0.4_386] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 146 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
a : Optional[int] = logging.get_logger(__name__)
a : Any = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
a : Optional[int] = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : List[Any] = torch.load(__lowerCamelCase , map_location="""cpu""" )
return sd
def lowerCamelCase__ ( __lowerCamelCase : Dict , __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any]=rename_keys_prefix ):
__UpperCAmelCase : Optional[int] = OrderedDict()
__UpperCAmelCase : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
__UpperCAmelCase : Optional[Any] = key
for name_pair in rename_keys_prefix:
__UpperCAmelCase : Optional[int] = new_key.replace(name_pair[0] , name_pair[1] )
__UpperCAmelCase : List[str] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__UpperCAmelCase : Optional[Any] = new_d["""cls.predictions.bias"""]
return new_d
@torch.no_grad()
def lowerCamelCase__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any] ):
assert (
checkpoint_path.split("""/""" )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
__UpperCAmelCase : Union[str, Any] = """pretraining"""
if "vcr" in checkpoint_path:
__UpperCAmelCase : str = {"""visual_embedding_dim""": 512}
elif "vqa_advanced" in checkpoint_path:
__UpperCAmelCase : Optional[int] = {"""visual_embedding_dim""": 2048}
elif "vqa" in checkpoint_path:
__UpperCAmelCase : Dict = {"""visual_embedding_dim""": 2048}
elif "nlvr" in checkpoint_path:
__UpperCAmelCase : Any = {"""visual_embedding_dim""": 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
__UpperCAmelCase : str = {"""visual_embedding_dim""": 512}
__UpperCAmelCase : int = """multichoice"""
elif "vqa_advanced" in checkpoint_path:
__UpperCAmelCase : List[Any] = {"""visual_embedding_dim""": 2048}
__UpperCAmelCase : Optional[int] = """vqa_advanced"""
elif "vqa" in checkpoint_path:
__UpperCAmelCase : str = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
__UpperCAmelCase : Any = """vqa"""
elif "nlvr" in checkpoint_path:
__UpperCAmelCase : Tuple = {
"""visual_embedding_dim""": 1024,
"""num_labels""": 2,
}
__UpperCAmelCase : List[Any] = """nlvr"""
__UpperCAmelCase : str = VisualBertConfig(**__lowerCamelCase )
# Load State Dict
__UpperCAmelCase : Dict = load_state_dict(__lowerCamelCase )
__UpperCAmelCase : Any = get_new_dict(__lowerCamelCase , __lowerCamelCase )
if model_type == "pretraining":
__UpperCAmelCase : Union[str, Any] = VisualBertForPreTraining(__lowerCamelCase )
elif model_type == "vqa":
__UpperCAmelCase : int = VisualBertForQuestionAnswering(__lowerCamelCase )
elif model_type == "nlvr":
__UpperCAmelCase : Dict = VisualBertForVisualReasoning(__lowerCamelCase )
elif model_type == "multichoice":
__UpperCAmelCase : Optional[Any] = VisualBertForMultipleChoice(__lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# Save Checkpoints
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
a : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
a : Optional[int] = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 114 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class a :
"""simple docstring"""
def __init__( self : List[Any] , __lowercase : List[Any] , __lowercase : Any=13 , __lowercase : str=7 , __lowercase : Union[str, Any]=True , __lowercase : Any=True , __lowercase : int=True , __lowercase : Optional[int]=True , __lowercase : List[str]=99 , __lowercase : str=32 , __lowercase : Dict=5 , __lowercase : List[str]=4 , __lowercase : Dict=37 , __lowercase : Optional[int]="gelu" , __lowercase : int=0.1 , __lowercase : List[Any]=0.1 , __lowercase : Tuple=128 , __lowercase : Union[str, Any]=32 , __lowercase : str=16 , __lowercase : List[str]=2 , __lowercase : Optional[int]=0.02 , __lowercase : Any=3 , __lowercase : Any=4 , __lowercase : Optional[Any]=None , ) -> Any:
__UpperCAmelCase : Optional[Any] = parent
__UpperCAmelCase : Optional[int] = batch_size
__UpperCAmelCase : int = seq_length
__UpperCAmelCase : Optional[Any] = is_training
__UpperCAmelCase : int = use_input_mask
__UpperCAmelCase : Tuple = use_token_type_ids
__UpperCAmelCase : List[str] = use_labels
__UpperCAmelCase : List[str] = vocab_size
__UpperCAmelCase : Any = hidden_size
__UpperCAmelCase : Any = num_hidden_layers
__UpperCAmelCase : List[Any] = num_attention_heads
__UpperCAmelCase : List[str] = intermediate_size
__UpperCAmelCase : Tuple = hidden_act
__UpperCAmelCase : Tuple = hidden_dropout_prob
__UpperCAmelCase : Tuple = attention_probs_dropout_prob
__UpperCAmelCase : List[Any] = max_position_embeddings
__UpperCAmelCase : Dict = type_vocab_size
__UpperCAmelCase : int = type_sequence_label_size
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : List[Any] = num_labels
__UpperCAmelCase : Optional[int] = num_choices
__UpperCAmelCase : Any = scope
def UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
__UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : Any = None
if self.use_input_mask:
__UpperCAmelCase : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCAmelCase : Any = None
if self.use_token_type_ids:
__UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : List[str] = None
if self.use_labels:
__UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCAmelCase : int = ids_tensor([self.batch_size] , self.num_choices )
__UpperCAmelCase : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : str ) -> str:
return NezhaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowercase , initializer_range=self.initializer_range , )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : List[str] = self.prepare_config_and_inputs()
__UpperCAmelCase : str = True
__UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase ( self : str , __lowercase : Union[str, Any] , __lowercase : str , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : str , __lowercase : int ) -> Any:
__UpperCAmelCase : Union[str, Any] = NezhaModel(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : int = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
__UpperCAmelCase : Optional[Any] = model(__lowercase , token_type_ids=__lowercase )
__UpperCAmelCase : List[Any] = model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase ( self : Optional[int] , __lowercase : Union[str, Any] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Optional[int] , __lowercase : Any , __lowercase : str , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Tuple , ) -> Optional[int]:
__UpperCAmelCase : List[Any] = True
__UpperCAmelCase : Optional[Any] = NezhaModel(__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : List[Any] = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , )
__UpperCAmelCase : Optional[Any] = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , encoder_hidden_states=__lowercase , )
__UpperCAmelCase : List[str] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase ( self : Any , __lowercase : int , __lowercase : str , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Optional[int] ) -> Optional[int]:
__UpperCAmelCase : Optional[int] = NezhaForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Optional[int] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Dict , __lowercase : str , __lowercase : Optional[Any] , __lowercase : int , __lowercase : List[str] , __lowercase : Dict , __lowercase : str , __lowercase : str ) -> Dict:
__UpperCAmelCase : Optional[int] = NezhaForNextSentencePrediction(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Union[str, Any] = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def UpperCAmelCase ( self : Tuple , __lowercase : Any , __lowercase : Dict , __lowercase : Optional[int] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : List[str] ) -> int:
__UpperCAmelCase : Optional[Any] = NezhaForPreTraining(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : int = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , next_sentence_label=__lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def UpperCAmelCase ( self : Tuple , __lowercase : List[str] , __lowercase : Any , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : List[Any] , __lowercase : Dict ) -> List[Any]:
__UpperCAmelCase : Optional[Any] = NezhaForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Any = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : Tuple , __lowercase : Dict , __lowercase : Any , __lowercase : Any , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Dict , __lowercase : List[Any] ) -> Optional[Any]:
__UpperCAmelCase : Optional[int] = self.num_labels
__UpperCAmelCase : Union[str, Any] = NezhaForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Optional[Any] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Tuple , __lowercase : List[Any] , __lowercase : Union[str, Any] , __lowercase : str , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Dict ) -> str:
__UpperCAmelCase : Union[str, Any] = self.num_labels
__UpperCAmelCase : Dict = NezhaForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Dict = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : Tuple , __lowercase : Any , __lowercase : List[Any] , __lowercase : Dict , __lowercase : Tuple , __lowercase : List[Any] , __lowercase : Any , __lowercase : int ) -> Optional[int]:
__UpperCAmelCase : List[str] = self.num_choices
__UpperCAmelCase : int = NezhaForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
__UpperCAmelCase : List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : Tuple = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : Tuple = model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Dict ) -> List[Any]:
__UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : Tuple = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
a : Union[str, Any] = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
a : Tuple = (
{
'feature-extraction': NezhaModel,
'fill-mask': NezhaForMaskedLM,
'question-answering': NezhaForQuestionAnswering,
'text-classification': NezhaForSequenceClassification,
'token-classification': NezhaForTokenClassification,
'zero-shot': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
a : Dict = True
def UpperCAmelCase ( self : Optional[int] , __lowercase : str , __lowercase : Dict , __lowercase : int=False ) -> Dict:
__UpperCAmelCase : Optional[Any] = super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
__UpperCAmelCase : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowercase )
__UpperCAmelCase : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def UpperCAmelCase ( self : Any ) -> int:
__UpperCAmelCase : Tuple = NezhaModelTester(self )
__UpperCAmelCase : List[Any] = ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def UpperCAmelCase ( self : Dict ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : str ) -> List[Any]:
__UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__lowercase )
def UpperCAmelCase ( self : int ) -> str:
# This regression test was failing with PyTorch < 1.3
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
__UpperCAmelCase : int = None
self.model_tester.create_and_check_model_as_decoder(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowercase )
def UpperCAmelCase ( self : int ) -> Dict:
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowercase )
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*__lowercase )
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__lowercase )
def UpperCAmelCase ( self : int ) -> Dict:
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowercase )
def UpperCAmelCase ( self : List[str] ) -> str:
__UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowercase )
def UpperCAmelCase ( self : Optional[Any] ) -> str:
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowercase )
@slow
def UpperCAmelCase ( self : str ) -> Any:
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : int = NezhaModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
@slow
@require_torch_gpu
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
__UpperCAmelCase : Optional[Any] = True
__UpperCAmelCase : Dict = model_class(config=__lowercase )
__UpperCAmelCase : Union[str, Any] = self._prepare_for_class(__lowercase , __lowercase )
__UpperCAmelCase : Union[str, Any] = torch.jit.trace(
__lowercase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(__lowercase , os.path.join(__lowercase , """bert.pt""" ) )
__UpperCAmelCase : Optional[int] = torch.jit.load(os.path.join(__lowercase , """bert.pt""" ) , map_location=__lowercase )
loaded(inputs_dict["""input_ids"""].to(__lowercase ) , inputs_dict["""attention_mask"""].to(__lowercase ) )
@require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__UpperCAmelCase : Tuple = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
__UpperCAmelCase : List[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__UpperCAmelCase : Any = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__UpperCAmelCase : Optional[Any] = model(__lowercase , attention_mask=__lowercase )[0]
__UpperCAmelCase : Dict = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , __lowercase )
__UpperCAmelCase : Any = torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __lowercase , atol=1e-4 ) )
@slow
def UpperCAmelCase ( self : str ) -> List[str]:
__UpperCAmelCase : int = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
__UpperCAmelCase : Tuple = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__UpperCAmelCase : Any = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__UpperCAmelCase : Dict = model(__lowercase , attention_mask=__lowercase )[0]
__UpperCAmelCase : Union[str, Any] = torch.Size((1, 6, 21128) )
self.assertEqual(output.shape , __lowercase )
__UpperCAmelCase : int = torch.tensor(
[[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __lowercase , atol=1e-4 ) )
| 114 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def __snake_case( _lowerCAmelCase ) -> Union[str, Any]:
snake_case__ , snake_case__ : Union[str, Any] = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(A__ ):
for j in range(A__ ):
snake_case__ : int = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
__a = imread("image_data/lena.jpg", 1)
# convert to its negative
__a = convert_to_negative(img)
# show result image
imshow("negative of original image", img)
waitKey(0)
destroyAllWindows()
| 357 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
snake_case__ : List[Any] = 0
snake_case__ : Union[str, Any] = str(_lowerCAmelCase )
while len(_lowerCAmelCase ) != 1:
snake_case__ : List[Any] = [int(_lowerCAmelCase ) for i in num_string]
snake_case__ : str = 1
for i in range(0 , len(_lowerCAmelCase ) ):
total *= numbers[i]
snake_case__ : Optional[Any] = str(_lowerCAmelCase )
steps += 1
return steps
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
snake_case__ : Union[str, Any] = 0
snake_case__ : List[str] = str(_lowerCAmelCase )
while len(_lowerCAmelCase ) != 1:
snake_case__ : Optional[int] = [int(_lowerCAmelCase ) for i in num_string]
snake_case__ : Dict = 0
for i in range(0 , len(_lowerCAmelCase ) ):
total += numbers[i]
snake_case__ : List[Any] = str(_lowerCAmelCase )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')
lowerCAmelCase__ = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
a : Optional[int] =field(
default=1_28 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
a : bool =field(
default=lowercase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
a : bool =field(
default=lowercase , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
a : Optional[int] =field(
default=lowercase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
a : Optional[int] =field(
default=lowercase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
a : Optional[int] =field(
default=lowercase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
a : str =field(
default=lowercase , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
a : str =field(
default=lowercase , metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."} )
a : Optional[str] =field(
default=lowercase , metadata={"help": "Train language if it is different from the evaluation language."} )
a : Optional[str] =field(
default=lowercase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
a : Optional[str] =field(
default=lowercase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
a : Optional[str] =field(
default=lowercase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
a : Optional[bool] =field(
default=lowercase , metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"} , )
a : bool =field(
default=lowercase , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
a : str =field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
a : bool =field(
default=lowercase , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
a : bool =field(
default=lowercase , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_xnli" , SCREAMING_SNAKE_CASE )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCAmelCase : Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(SCREAMING_SNAKE_CASE )
datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE )
transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowerCAmelCase : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase : Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
lowerCAmelCase : Union[str, Any] = load_dataset(
"xnli" , model_args.language , split="train" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowerCAmelCase : List[Any] = load_dataset(
"xnli" , model_args.train_language , split="train" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase : int = train_dataset.features["label"].names
if training_args.do_eval:
lowerCAmelCase : List[Any] = load_dataset(
"xnli" , model_args.language , split="validation" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase : Dict = eval_dataset.features["label"].names
if training_args.do_predict:
lowerCAmelCase : List[str] = load_dataset(
"xnli" , model_args.language , split="test" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase : List[Any] = predict_dataset.features["label"].names
# Labels
lowerCAmelCase : Optional[int] = len(SCREAMING_SNAKE_CASE )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=SCREAMING_SNAKE_CASE , idalabel={str(SCREAMING_SNAKE_CASE ): label for i, label in enumerate(SCREAMING_SNAKE_CASE )} , labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE )} , finetuning_task="xnli" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
lowerCAmelCase : Optional[int] = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowerCAmelCase : Union[str, Any] = False
def preprocess_function(SCREAMING_SNAKE_CASE : Dict ):
# Tokenize the texts
return tokenizer(
examples["premise"] , examples["hypothesis"] , padding=SCREAMING_SNAKE_CASE , max_length=data_args.max_seq_length , truncation=SCREAMING_SNAKE_CASE , )
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCAmelCase : str = min(len(SCREAMING_SNAKE_CASE ) , data_args.max_train_samples )
lowerCAmelCase : Union[str, Any] = train_dataset.select(range(SCREAMING_SNAKE_CASE ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
lowerCAmelCase : Dict = train_dataset.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on train dataset" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(SCREAMING_SNAKE_CASE ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCAmelCase : Dict = min(len(SCREAMING_SNAKE_CASE ) , data_args.max_eval_samples )
lowerCAmelCase : int = eval_dataset.select(range(SCREAMING_SNAKE_CASE ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
lowerCAmelCase : List[Any] = eval_dataset.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on validation dataset" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
lowerCAmelCase : Any = min(len(SCREAMING_SNAKE_CASE ) , data_args.max_predict_samples )
lowerCAmelCase : List[Any] = predict_dataset.select(range(SCREAMING_SNAKE_CASE ) )
with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
lowerCAmelCase : Optional[int] = predict_dataset.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on prediction dataset" , )
# Get the metric function
lowerCAmelCase : List[str] = evaluate.load("xnli" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(SCREAMING_SNAKE_CASE : EvalPrediction ):
lowerCAmelCase : List[str] = p.predictions[0] if isinstance(p.predictions , SCREAMING_SNAKE_CASE ) else p.predictions
lowerCAmelCase : Dict = np.argmax(SCREAMING_SNAKE_CASE , axis=1 )
return metric.compute(predictions=SCREAMING_SNAKE_CASE , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowerCAmelCase : Optional[Any] = default_data_collator
elif training_args.fpaa:
lowerCAmelCase : Tuple = DataCollatorWithPadding(SCREAMING_SNAKE_CASE , pad_to_multiple_of=8 )
else:
lowerCAmelCase : List[str] = None
# Initialize our Trainer
lowerCAmelCase : Optional[int] = Trainer(
model=SCREAMING_SNAKE_CASE , args=SCREAMING_SNAKE_CASE , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , data_collator=SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
lowerCAmelCase : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
lowerCAmelCase : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCAmelCase : Any = last_checkpoint
lowerCAmelCase : Optional[Any] = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE )
lowerCAmelCase : Optional[int] = train_result.metrics
lowerCAmelCase : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE )
)
lowerCAmelCase : Tuple = min(SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train" , SCREAMING_SNAKE_CASE )
trainer.save_metrics("train" , SCREAMING_SNAKE_CASE )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
lowerCAmelCase : Optional[Any] = trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE )
lowerCAmelCase : Any = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE )
lowerCAmelCase : List[Any] = min(SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) )
trainer.log_metrics("eval" , SCREAMING_SNAKE_CASE )
trainer.save_metrics("eval" , SCREAMING_SNAKE_CASE )
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***" )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : str = trainer.predict(SCREAMING_SNAKE_CASE , metric_key_prefix="predict" )
lowerCAmelCase : Optional[int] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(SCREAMING_SNAKE_CASE )
)
lowerCAmelCase : List[Any] = min(SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) )
trainer.log_metrics("predict" , SCREAMING_SNAKE_CASE )
trainer.save_metrics("predict" , SCREAMING_SNAKE_CASE )
lowerCAmelCase : Optional[Any] = np.argmax(SCREAMING_SNAKE_CASE , axis=1 )
lowerCAmelCase : Any = os.path.join(training_args.output_dir , "predictions.txt" )
if trainer.is_world_process_zero():
with open(SCREAMING_SNAKE_CASE , "w" ) as writer:
writer.write("index\tprediction\n" )
for index, item in enumerate(SCREAMING_SNAKE_CASE ):
lowerCAmelCase : Optional[Any] = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 108 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/data2vec-vision-base-ft''': (
'''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : List[Any] ="data2vec-vision"
def __init__( self , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=224 , snake_case__=16 , snake_case__=3 , snake_case__=False , snake_case__=False , snake_case__=False , snake_case__=False , snake_case__=0.1 , snake_case__=0.1 , snake_case__=True , snake_case__=[3, 5, 7, 11] , snake_case__=[1, 2, 3, 6] , snake_case__=True , snake_case__=0.4 , snake_case__=256 , snake_case__=1 , snake_case__=False , snake_case__=255 , **snake_case__ , ):
"""simple docstring"""
super().__init__(**snake_case__ )
lowerCAmelCase : Tuple = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : Tuple = num_attention_heads
lowerCAmelCase : Optional[int] = intermediate_size
lowerCAmelCase : Optional[int] = hidden_act
lowerCAmelCase : Dict = hidden_dropout_prob
lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
lowerCAmelCase : int = initializer_range
lowerCAmelCase : Dict = layer_norm_eps
lowerCAmelCase : Optional[int] = image_size
lowerCAmelCase : Optional[Any] = patch_size
lowerCAmelCase : Optional[Any] = num_channels
lowerCAmelCase : Union[str, Any] = use_mask_token
lowerCAmelCase : str = use_absolute_position_embeddings
lowerCAmelCase : Any = use_relative_position_bias
lowerCAmelCase : List[str] = use_shared_relative_position_bias
lowerCAmelCase : str = layer_scale_init_value
lowerCAmelCase : Union[str, Any] = drop_path_rate
lowerCAmelCase : Any = use_mean_pooling
# decode head attributes (semantic segmentation)
lowerCAmelCase : Optional[int] = out_indices
lowerCAmelCase : Union[str, Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
lowerCAmelCase : str = use_auxiliary_head
lowerCAmelCase : int = auxiliary_loss_weight
lowerCAmelCase : Tuple = auxiliary_channels
lowerCAmelCase : List[str] = auxiliary_num_convs
lowerCAmelCase : Tuple = auxiliary_concat_input
lowerCAmelCase : List[str] = semantic_loss_ignore_index
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Union[str, Any] =version.parse("1.11" )
@property
def lowercase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowercase__ ( self ):
"""simple docstring"""
return 1e-4
| 108 | 1 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : int = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """xlm-prophetnet"""
UpperCamelCase__ = ["""past_key_values"""]
UpperCamelCase__ = {
"""num_attention_heads""": """num_encoder_attention_heads""",
}
def __init__( self : Optional[int] , __UpperCamelCase : Optional[float] = 0.1 , __UpperCamelCase : Optional[Union[str, Callable]] = "gelu" , __UpperCamelCase : Optional[int] = 3_0_5_2_2 , __UpperCamelCase : Optional[int] = 1_0_2_4 , __UpperCamelCase : Optional[int] = 4_0_9_6 , __UpperCamelCase : Optional[int] = 1_2 , __UpperCamelCase : Optional[int] = 1_6 , __UpperCamelCase : Optional[int] = 4_0_9_6 , __UpperCamelCase : Optional[int] = 1_2 , __UpperCamelCase : Optional[int] = 1_6 , __UpperCamelCase : Optional[float] = 0.1 , __UpperCamelCase : Optional[float] = 0.1 , __UpperCamelCase : Optional[int] = 5_1_2 , __UpperCamelCase : Optional[float] = 0.0_2 , __UpperCamelCase : Optional[bool] = True , __UpperCamelCase : Optional[bool] = True , __UpperCamelCase : Optional[int] = 0 , __UpperCamelCase : Optional[int] = 2 , __UpperCamelCase : Optional[int] = 3_2 , __UpperCamelCase : Optional[int] = 1_2_8 , __UpperCamelCase : Optional[bool] = False , __UpperCamelCase : Optional[float] = 0.0 , __UpperCamelCase : Optional[bool] = True , __UpperCamelCase : Optional[int] = 0 , __UpperCamelCase : Optional[int] = 1 , __UpperCamelCase : Optional[int] = 2 , **__UpperCamelCase : Optional[Any] , )->List[Any]:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = encoder_ffn_dim
_UpperCAmelCase = num_encoder_layers
_UpperCAmelCase = num_encoder_attention_heads
_UpperCAmelCase = decoder_ffn_dim
_UpperCAmelCase = num_decoder_layers
_UpperCAmelCase = num_decoder_attention_heads
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = init_std # Normal(0, this parameter)
_UpperCAmelCase = activation_function
# parameters for xlmprophetnet
_UpperCAmelCase = ngram
_UpperCAmelCase = num_buckets
_UpperCAmelCase = relative_max_distance
_UpperCAmelCase = disable_ngram_loss
_UpperCAmelCase = eps
# 3 Types of Dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = dropout
_UpperCAmelCase = use_cache
super().__init__(
pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , is_encoder_decoder=__UpperCamelCase , add_cross_attention=__UpperCamelCase , decoder_start_token_id=__UpperCamelCase , **__UpperCamelCase , )
@property
def lowercase__ ( self : List[str] )->int:
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def lowercase__ ( self : Any , __UpperCamelCase : List[str] )->Union[str, Any]:
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 326 |
"""simple docstring"""
__A : Tuple = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : Union[str, Any] = frozenset(["prompt", "negative_prompt"])
__A : str = frozenset([])
__A : List[str] = frozenset(["image"])
__A : Optional[Any] = frozenset(
[
"image",
"height",
"width",
"guidance_scale",
]
)
__A : Optional[int] = frozenset(["image"])
__A : Optional[int] = frozenset(
[
"prompt",
"image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
__A : Optional[Any] = frozenset(["prompt", "image", "negative_prompt"])
__A : str = frozenset(
[
# Text guided image variation with an image mask
"prompt",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
__A : Tuple = frozenset(["prompt", "image", "mask_image", "negative_prompt"])
__A : List[str] = frozenset(
[
# image variation with an image mask
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
__A : List[Any] = frozenset(["image", "mask_image"])
__A : List[str] = frozenset(
[
"example_image",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
__A : Tuple = frozenset(["example_image", "image", "mask_image"])
__A : Dict = frozenset(["class_labels"])
__A : str = frozenset(["class_labels"])
__A : str = frozenset(["batch_size"])
__A : Union[str, Any] = frozenset([])
__A : str = frozenset(["batch_size"])
__A : Optional[int] = frozenset([])
__A : Any = frozenset(
[
"prompt",
"audio_length_in_s",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : List[str] = frozenset(["prompt", "negative_prompt"])
__A : Tuple = frozenset(["input_tokens"])
__A : Optional[int] = frozenset(["input_tokens"])
| 326 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
lowercase_ : List[Any] =KandinskyVaaInpaintPipeline
lowercase_ : Union[str, Any] =['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
lowercase_ : Optional[int] =[
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
'''mask_image''',
]
lowercase_ : Union[str, Any] =[
'''generator''',
'''height''',
'''width''',
'''latents''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
lowercase_ : Tuple =False
@property
def A__ ( self):
return 3_2
@property
def A__ ( self):
return 3_2
@property
def A__ ( self):
return self.time_input_dim
@property
def A__ ( self):
return self.time_input_dim * 4
@property
def A__ ( self):
return 1_0_0
@property
def A__ ( self):
torch.manual_seed(0)
lowercase = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase = UNetaDConditionModel(**A__)
return model
@property
def A__ ( self):
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A__ ( self):
torch.manual_seed(0)
lowercase = VQModel(**self.dummy_movq_kwargs)
return model
def A__ ( self):
lowercase = self.dummy_unet
lowercase = self.dummy_movq
lowercase = DDIMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='''linear''' ,beta_start=0.00085 ,beta_end=0.012 ,clip_sample=A__ ,set_alpha_to_one=A__ ,steps_offset=1 ,prediction_type='''epsilon''' ,thresholding=A__ ,)
lowercase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A__ ( self ,A__ ,A__=0):
lowercase = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(A__)).to(A__)
lowercase = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1)).to(
A__)
# create init_image
lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(A__)).to(A__)
lowercase = image.cpu().permute(0 ,2 ,3 ,1)[0]
lowercase = Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((2_5_6, 2_5_6))
# create mask
lowercase = np.ones((6_4, 6_4) ,dtype=np.floataa)
lowercase = 0
if str(A__).startswith('''mps'''):
lowercase = torch.manual_seed(A__)
else:
lowercase = torch.Generator(device=A__).manual_seed(A__)
lowercase = {
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def A__ ( self):
lowercase = '''cpu'''
lowercase = self.get_dummy_components()
lowercase = self.pipeline_class(**A__)
lowercase = pipe.to(A__)
pipe.set_progress_bar_config(disable=A__)
lowercase = pipe(**self.get_dummy_inputs(A__))
lowercase = output.images
lowercase = pipe(
**self.get_dummy_inputs(A__) ,return_dict=A__ ,)[0]
lowercase = image[0, -3:, -3:, -1]
lowercase = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}')
assert image.shape == (1, 6_4, 6_4, 3)
lowercase = np.array(
[0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
def A__ ( self):
super().test_inference_batch_single_identical(expected_max_diff=3E-3)
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
def A__ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self):
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''')
lowercase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
lowercase = np.ones((7_6_8, 7_6_8) ,dtype=np.floataa)
lowercase = 0
lowercase = '''a hat'''
lowercase = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' ,torch_dtype=torch.floataa)
pipe_prior.to(A__)
lowercase = KandinskyVaaInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder-inpaint''' ,torch_dtype=torch.floataa)
lowercase = pipeline.to(A__)
pipeline.set_progress_bar_config(disable=A__)
lowercase = torch.Generator(device='''cpu''').manual_seed(0)
lowercase , lowercase = pipe_prior(
A__ ,generator=A__ ,num_inference_steps=5 ,negative_prompt='''''' ,).to_tuple()
lowercase = pipeline(
image=A__ ,mask_image=A__ ,image_embeds=A__ ,negative_image_embeds=A__ ,generator=A__ ,num_inference_steps=1_0_0 ,height=7_6_8 ,width=7_6_8 ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(A__ ,A__)
| 101 | """simple docstring"""
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 YolosImageProcessor
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=7 , _UpperCAmelCase=3 , _UpperCAmelCase=30 , _UpperCAmelCase=400 , _UpperCAmelCase=True , _UpperCAmelCase=None , _UpperCAmelCase=True , _UpperCAmelCase=[0.5, 0.5, 0.5] , _UpperCAmelCase=[0.5, 0.5, 0.5] , _UpperCAmelCase=True , _UpperCAmelCase=1 / 255 , _UpperCAmelCase=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase__: str = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
lowercase__: Optional[Any] = parent
lowercase__: List[Any] = batch_size
lowercase__: Tuple = num_channels
lowercase__: Optional[Any] = min_resolution
lowercase__: Dict = max_resolution
lowercase__: Optional[int] = do_resize
lowercase__: Any = size
lowercase__: Optional[Any] = do_normalize
lowercase__: Union[str, Any] = image_mean
lowercase__: Tuple = image_std
lowercase__: str = do_rescale
lowercase__: Any = rescale_factor
lowercase__: List[Any] = do_pad
def _snake_case ( self ):
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 _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=False ):
if not batched:
lowercase__: Optional[Any] = image_inputs[0]
if isinstance(_UpperCAmelCase , Image.Image ):
lowercase__, lowercase__: Dict = image.size
else:
lowercase__, lowercase__: Optional[Any] = image.shape[1], image.shape[2]
if w < h:
lowercase__: List[str] = int(self.size['''shortest_edge'''] * h / w )
lowercase__: Union[str, Any] = self.size['''shortest_edge''']
elif w > h:
lowercase__: int = self.size['''shortest_edge''']
lowercase__: int = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase__: Union[str, Any] = self.size['''shortest_edge''']
lowercase__: Union[str, Any] = self.size['''shortest_edge''']
else:
lowercase__: Optional[int] = []
for image in image_inputs:
lowercase__, lowercase__: int = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase__: Union[str, Any] = max(_UpperCAmelCase , key=lambda _UpperCAmelCase : item[0] )[0]
lowercase__: Dict = max(_UpperCAmelCase , key=lambda _UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :Optional[int] = YolosImageProcessor if is_vision_available() else None
def _snake_case ( self ):
lowercase__: int = YolosImageProcessingTester(self )
@property
def _snake_case ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _snake_case ( self ):
lowercase__: List[str] = 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 , '''size''' ) )
def _snake_case ( self ):
lowercase__: Any = 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 , _UpperCAmelCase )
lowercase__: Any = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_UpperCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , _UpperCAmelCase )
def _snake_case ( self ):
pass
def _snake_case ( self ):
# Initialize image_processing
lowercase__: Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__: Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , Image.Image )
# Test not batched input
lowercase__: int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase__, lowercase__: Optional[int] = self.image_processor_tester.get_expected_values(_UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__, lowercase__: Any = self.image_processor_tester.get_expected_values(_UpperCAmelCase , batched=_UpperCAmelCase )
lowercase__: 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,
expected_height,
expected_width,
) , )
def _snake_case ( self ):
# Initialize image_processing
lowercase__: List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__: Optional[Any] = 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
lowercase__: List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase__, lowercase__: str = self.image_processor_tester.get_expected_values(_UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__: Dict = image_processing(_UpperCAmelCase , return_tensors='''pt''' ).pixel_values
lowercase__, lowercase__: str = self.image_processor_tester.get_expected_values(_UpperCAmelCase , batched=_UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _snake_case ( self ):
# Initialize image_processing
lowercase__: Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__: Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , torchify=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , torch.Tensor )
# Test not batched input
lowercase__: Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase__, lowercase__: int = self.image_processor_tester.get_expected_values(_UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__: List[str] = image_processing(_UpperCAmelCase , return_tensors='''pt''' ).pixel_values
lowercase__, lowercase__: List[Any] = self.image_processor_tester.get_expected_values(_UpperCAmelCase , batched=_UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _snake_case ( self ):
# Initialize image_processings
lowercase__: Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
lowercase__: Optional[Any] = self.image_processing_class(do_resize=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_rescale=_UpperCAmelCase )
# create random PyTorch tensors
lowercase__: Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , torchify=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowercase__: List[str] = image_processing_a.pad(_UpperCAmelCase , return_tensors='''pt''' )
lowercase__: Tuple = image_processing_a(_UpperCAmelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def _snake_case ( self ):
# prepare image and target
lowercase__: Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase__: Any = json.loads(f.read() )
lowercase__: Dict = {'''image_id''': 39769, '''annotations''': target}
# encode them
lowercase__: Dict = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
lowercase__: Any = image_processing(images=_UpperCAmelCase , annotations=_UpperCAmelCase , return_tensors='''pt''' )
# verify pixel values
lowercase__: Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _UpperCAmelCase )
lowercase__: Optional[Any] = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _UpperCAmelCase , atol=1e-4 ) )
# verify area
lowercase__: Tuple = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _UpperCAmelCase ) )
# verify boxes
lowercase__: str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _UpperCAmelCase )
lowercase__: List[Any] = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _UpperCAmelCase , atol=1e-3 ) )
# verify image_id
lowercase__: Optional[int] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _UpperCAmelCase ) )
# verify is_crowd
lowercase__: Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _UpperCAmelCase ) )
# verify class_labels
lowercase__: Dict = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _UpperCAmelCase ) )
# verify orig_size
lowercase__: List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _UpperCAmelCase ) )
# verify size
lowercase__: List[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _UpperCAmelCase ) )
@slow
def _snake_case ( self ):
# prepare image, target and masks_path
lowercase__: str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase__: str = json.loads(f.read() )
lowercase__: List[Any] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
lowercase__: Dict = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase__: Union[str, Any] = YolosImageProcessor(format='''coco_panoptic''' )
lowercase__: Optional[Any] = image_processing(images=_UpperCAmelCase , annotations=_UpperCAmelCase , masks_path=_UpperCAmelCase , return_tensors='''pt''' )
# verify pixel values
lowercase__: Optional[int] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _UpperCAmelCase )
lowercase__: Dict = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _UpperCAmelCase , atol=1e-4 ) )
# verify area
lowercase__: str = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _UpperCAmelCase ) )
# verify boxes
lowercase__: List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _UpperCAmelCase )
lowercase__: List[Any] = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _UpperCAmelCase , atol=1e-3 ) )
# verify image_id
lowercase__: int = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _UpperCAmelCase ) )
# verify is_crowd
lowercase__: int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _UpperCAmelCase ) )
# verify class_labels
lowercase__: Dict = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _UpperCAmelCase ) )
# verify masks
lowercase__: Union[str, Any] = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _UpperCAmelCase )
# verify orig_size
lowercase__: List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _UpperCAmelCase ) )
# verify size
lowercase__: Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _UpperCAmelCase ) )
| 177 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase : List[str] = logging.get_logger(__name__)
class lowerCAmelCase ( __a ):
'''simple docstring'''
def __init__( self : str , *__a : Optional[Any] , **__a : List[Any] ) -> None:
"""simple docstring"""
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" , __a , )
super().__init__(*__a , **__a ) | 306 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Dict = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
__lowercase : List[str] = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
__lowercase : Optional[Any] = model(__a )["""last_hidden_state"""]
__lowercase : Any = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , __a )
# compare the actual values for a slice.
__lowercase : Dict = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) ) | 306 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( snake_case__ : int ): # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def _snake_case ( ):
with parallel_backend('spark' ):
assert ParallelBackendConfig.backend_name == "spark"
A = [1, 2, 3]
with pytest.raises(snake_case__ ):
with parallel_backend('unsupported backend' ):
map_nested(snake_case__ , snake_case__ , num_proc=2 )
with pytest.raises(snake_case__ ):
with parallel_backend('unsupported backend' ):
map_nested(snake_case__ , snake_case__ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc' , [2, -1] )
def _snake_case ( snake_case__ : Union[str, Any] ):
A = [1, 2]
A = {'a': 1, 'b': 2}
A = {'a': [1, 2], 'b': [3, 4]}
A = {'a': {'1': 1}, 'b': 2}
A = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
A = [2, 3]
A = {'a': 2, 'b': 3}
A = {'a': [2, 3], 'b': [4, 5]}
A = {'a': {'1': 2}, 'b': 3}
A = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
with parallel_backend('spark' ):
assert map_nested(snake_case__ , snake_case__ , num_proc=snake_case__ ) == expected_map_nested_sa
assert map_nested(snake_case__ , snake_case__ , num_proc=snake_case__ ) == expected_map_nested_sa
assert map_nested(snake_case__ , snake_case__ , num_proc=snake_case__ ) == expected_map_nested_sa
assert map_nested(snake_case__ , snake_case__ , num_proc=snake_case__ ) == expected_map_nested_sa
assert map_nested(snake_case__ , snake_case__ , num_proc=snake_case__ ) == expected_map_nested_sa | 74 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : int ,A_ : int ) -> Union[str, Any]:
A = n
A = [None] * self.n
A = 0 # index of the first element
A = 0
A = 0
def __len__( self : int ) -> int:
return self.size
def _SCREAMING_SNAKE_CASE ( self : Any ) -> bool:
return self.size == 0
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
return False if self.is_empty() else self.array[self.front]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : List[Any] ) -> int:
if self.size >= self.n:
raise Exception('QUEUE IS FULL' )
A = data
A = (self.rear + 1) % self.n
self.size += 1
return self
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[Any]:
if self.size == 0:
raise Exception('UNDERFLOW' )
A = self.array[self.front]
A = None
A = (self.front + 1) % self.n
self.size -= 1
return temp | 74 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Any = {
'''huggingface/time-series-transformer-tourism-monthly''': (
'''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Tuple = 'time_series_transformer'
A : List[Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "student_t" , _SCREAMING_SNAKE_CASE = "nll" , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = [1, 2, 3, 4, 5, 6, 7] , _SCREAMING_SNAKE_CASE = "mean" , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 32 , _SCREAMING_SNAKE_CASE = 32 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "gelu" , _SCREAMING_SNAKE_CASE = 64 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = 0.02 , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , ) -> Optional[int]:
# time series specific configuration
snake_case_ : int = prediction_length
snake_case_ : List[Any] = context_length or prediction_length
snake_case_ : Optional[Any] = distribution_output
snake_case_ : List[Any] = loss
snake_case_ : Tuple = input_size
snake_case_ : List[Any] = num_time_features
snake_case_ : int = lags_sequence
snake_case_ : Tuple = scaling
snake_case_ : Any = num_dynamic_real_features
snake_case_ : List[Any] = num_static_real_features
snake_case_ : Any = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(_SCREAMING_SNAKE_CASE ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
snake_case_ : int = cardinality
else:
snake_case_ : Optional[int] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(_SCREAMING_SNAKE_CASE ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
snake_case_ : int = embedding_dimension
else:
snake_case_ : int = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
snake_case_ : Tuple = num_parallel_samples
# Transformer architecture configuration
snake_case_ : Optional[int] = input_size * len(_SCREAMING_SNAKE_CASE ) + self._number_of_features
snake_case_ : str = d_model
snake_case_ : List[str] = encoder_attention_heads
snake_case_ : Optional[Any] = decoder_attention_heads
snake_case_ : Optional[int] = encoder_ffn_dim
snake_case_ : Optional[int] = decoder_ffn_dim
snake_case_ : Optional[Any] = encoder_layers
snake_case_ : Union[str, Any] = decoder_layers
snake_case_ : Optional[int] = dropout
snake_case_ : List[Any] = attention_dropout
snake_case_ : Tuple = activation_dropout
snake_case_ : str = encoder_layerdrop
snake_case_ : Optional[Any] = decoder_layerdrop
snake_case_ : Optional[int] = activation_function
snake_case_ : int = init_std
snake_case_ : Optional[Any] = use_cache
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def _lowerCAmelCase ( self ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
) | 366 |
def lowerCAmelCase__ ( _a : dict ):
snake_case_ : List[Any] = set()
# edges = list of graph's edges
snake_case_ : int = get_edges(_a )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
snake_case_ , snake_case_ : Dict = edges.pop()
chosen_vertices.add(_a )
chosen_vertices.add(_a )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_a )
return chosen_vertices
def lowerCAmelCase__ ( _a : dict ):
snake_case_ : List[str] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 36 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase="" , _UpperCAmelCase="train" ) -> Any:
assert os.path.isdir(_UpperCAmelCase )
__UpperCamelCase : str = []
__UpperCamelCase : Any = os.listdir(_UpperCAmelCase )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
__UpperCamelCase : Optional[int] = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if not os.path.isfile(_UpperCAmelCase ):
continue
self.documents.append(_UpperCAmelCase )
def __len__(self ) -> Dict:
return len(self.documents )
def __getitem__(self , _UpperCAmelCase ) -> Optional[Any]:
__UpperCamelCase : int = self.documents[idx]
__UpperCamelCase : Dict = document_path.split("/" )[-1]
with open(_UpperCAmelCase , encoding="utf-8" ) as source:
__UpperCamelCase : Optional[int] = source.read()
__UpperCamelCase , __UpperCamelCase : List[str] = process_story(_UpperCAmelCase )
return document_name, story_lines, summary_lines
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Optional[Any] = list(filter(lambda snake_case__ : len(snake_case__ ) != 0 , [line.strip() for line in raw_story.split("\n" )] ) )
# for some unknown reason some lines miss a period, add it
__UpperCamelCase : int = [_add_missing_period(snake_case__ ) for line in nonempty_lines]
# gather article lines
__UpperCamelCase : List[str] = []
__UpperCamelCase : Any = deque(snake_case__ )
while True:
try:
__UpperCamelCase : Dict = lines.popleft()
if element.startswith("@highlight" ):
break
story_lines.append(snake_case__ )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
__UpperCamelCase : Dict = list(filter(lambda snake_case__ : not t.startswith("@highlight" ) , snake_case__ ) )
return story_lines, summary_lines
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : List[str] = [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
if line.startswith("@highlight" ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
if len(snake_case__ ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(snake_case__ )) )
return sequence
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
__UpperCamelCase : Dict = torch.ones_like(snake_case__ )
__UpperCamelCase : Optional[Any] = sequence == pad_token_id
__UpperCamelCase : Any = 0
return mask
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
__UpperCamelCase : List[Any] = [tokenizer.encode(snake_case__ ) for line in story_lines]
__UpperCamelCase : Any = [token for sentence in story_lines_token_ids for token in sentence]
__UpperCamelCase : Union[str, Any] = [tokenizer.encode(snake_case__ ) for line in summary_lines]
__UpperCamelCase : Optional[Any] = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
__UpperCamelCase : Tuple = []
for sequence in batch:
__UpperCamelCase : List[Any] = -1
__UpperCamelCase : List[Any] = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(snake_case__ )
return torch.tensor(snake_case__ )
| 298 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 298 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def snake_case__ ( lowerCamelCase__ : int ) -> int:
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : Dict = f'Input value of [number={number}] must be an integer'
raise TypeError(lowerCamelCase__ )
if is_prime(lowerCamelCase__ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 4 | 1 |
from __future__ import annotations
import math
def __lowerCAmelCase ( a__ ) -> list[int]:
if num <= 0:
__a = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(a__ )
__a = [True] * (num + 1)
__a = []
__a = 2
__a = int(math.sqrt(a__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(a__ )
# Set multiples of start be False
for i in range(start * start , num + 1 , a__ ):
if sieve[i] is True:
__a = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(a__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip()))) | 6 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = logging.getLogger(__name__)
@dataclass(frozen=_UpperCamelCase )
class snake_case__:
"""simple docstring"""
lowercase_ = 42
lowercase_ = 42
lowercase_ = None
lowercase_ = None
lowercase_ = None
@dataclass(frozen=_UpperCamelCase )
class snake_case__:
"""simple docstring"""
lowercase_ = 42
lowercase_ = None
lowercase_ = None
lowercase_ = None
lowercase_ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = 42
def __init__( self : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : PreTrainedTokenizer , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Tuple=False , SCREAMING_SNAKE_CASE : bool = False , ):
lowercase__ : List[str] = hans_processors[task]()
lowercase__ : Dict = os.path.join(
SCREAMING_SNAKE_CASE , "cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train" , tokenizer.__class__.__name__ , str(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE , ) , )
lowercase__ : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase__ , lowercase__ : Union[str, Any] = label_list[2], label_list[1]
lowercase__ : Any = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ : int = cached_features_file + ".lock"
with FileLock(SCREAMING_SNAKE_CASE ):
if os.path.exists(SCREAMING_SNAKE_CASE ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
lowercase__ : Any = torch.load(SCREAMING_SNAKE_CASE )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
lowercase__ : List[str] = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE )
)
logger.info("Training examples: %s" , len(SCREAMING_SNAKE_CASE ) )
lowercase__ : Tuple = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
logger.info("Saving features into cached file %s" , SCREAMING_SNAKE_CASE )
torch.save(self.features , SCREAMING_SNAKE_CASE )
def __len__( self : List[Any] ):
return len(self.features )
def __getitem__( self : str , SCREAMING_SNAKE_CASE : List[str] ):
return self.features[i]
def snake_case ( self : Any ):
return self.label_list
if is_tf_available():
import tensorflow as tf
class snake_case__:
"""simple docstring"""
lowercase_ = 42
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : PreTrainedTokenizer , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[int] = 128 , SCREAMING_SNAKE_CASE : int=False , SCREAMING_SNAKE_CASE : bool = False , ):
lowercase__ : str = hans_processors[task]()
lowercase__ : Union[str, Any] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase__ , lowercase__ : str = label_list[2], label_list[1]
lowercase__ : Optional[int] = label_list
lowercase__ : Any = processor.get_dev_examples(SCREAMING_SNAKE_CASE ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="convert examples to features" ):
if ex_index % 10_000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(SCREAMING_SNAKE_CASE )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
lowercase__ : Optional[int] = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE , (
{
"example_id": tf.intaa,
"input_ids": tf.intaa,
"attention_mask": tf.intaa,
"token_type_ids": tf.intaa,
},
tf.intaa,
) , (
{
"example_id": tf.TensorShape([] ),
"input_ids": tf.TensorShape([None, None] ),
"attention_mask": tf.TensorShape([None, None] ),
"token_type_ids": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def snake_case ( self : int ):
return self.dataset
def __len__( self : List[str] ):
return len(self.features )
def __getitem__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Any ):
return self.features[i]
def snake_case ( self : Any ):
return self.label_list
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
def snake_case ( self : Dict , SCREAMING_SNAKE_CASE : int ):
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE , "heuristics_train_set.txt" ) ) , "train" )
def snake_case ( self : Any , SCREAMING_SNAKE_CASE : Any ):
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE , "heuristics_evaluation_set.txt" ) ) , "dev" )
def snake_case ( self : Union[str, Any] ):
return ["contradiction", "entailment", "neutral"]
def snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict ):
lowercase__ : Dict = []
for i, line in enumerate(SCREAMING_SNAKE_CASE ):
if i == 0:
continue
lowercase__ : str = "%s-%s" % (set_type, line[0])
lowercase__ : str = line[5]
lowercase__ : List[str] = line[6]
lowercase__ : Dict = line[7][2:] if line[7].startswith("ex" ) else line[7]
lowercase__ : Union[str, Any] = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE , text_a=SCREAMING_SNAKE_CASE , text_b=SCREAMING_SNAKE_CASE , label=SCREAMING_SNAKE_CASE , pairID=SCREAMING_SNAKE_CASE ) )
return examples
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
"""simple docstring"""
lowercase__ : str = {label: i for i, label in enumerate(lowerCamelCase__ )}
lowercase__ : str = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__ ) , desc="convert examples to features" ):
if ex_index % 10_000 == 0:
logger.info("Writing example %d" % (ex_index) )
lowercase__ : Any = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="max_length" , truncation=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , )
lowercase__ : Optional[int] = label_map[example.label] if example.label in label_map else 0
lowercase__ : Any = int(example.pairID )
features.append(InputFeatures(**lowerCamelCase__ , label=lowerCamelCase__ , pairID=lowerCamelCase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info("*** Example ***" )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
lowerCAmelCase__ = {
'''hans''': 3,
}
lowerCAmelCase__ = {
'''hans''': HansProcessor,
}
| 130 | 0 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
_lowerCAmelCase : Tuple = '''base_with_context'''
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : int = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
_lowerCamelCase : Optional[Any] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_lowerCamelCase )
for lyr_num, lyr in enumerate(model.encoders ):
_lowerCamelCase : List[Any] = weights[F"""layers_{lyr_num}"""]
_lowerCamelCase : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
_lowerCamelCase : Optional[int] = ly_weight["attention"]
_lowerCamelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_lowerCamelCase : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_lowerCamelCase : int = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_lowerCamelCase : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_lowerCamelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
_lowerCamelCase : List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
_lowerCamelCase : Tuple = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
_lowerCamelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
_lowerCamelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> Union[str, Any]:
'''simple docstring'''
_lowerCamelCase : int = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
_lowerCamelCase : Union[str, Any] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_lowerCamelCase )
for lyr_num, lyr in enumerate(model.encoders ):
_lowerCamelCase : Tuple = weights[F"""layers_{lyr_num}"""]
_lowerCamelCase : Tuple = ly_weight["attention"]
_lowerCamelCase : Any = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_lowerCamelCase : Any = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_lowerCamelCase : Any = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_lowerCamelCase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_lowerCamelCase : Optional[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
_lowerCamelCase : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
_lowerCamelCase : List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
_lowerCamelCase : Dict = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
_lowerCamelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
_lowerCamelCase : Optional[int] = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> Optional[int]:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
_lowerCamelCase : Optional[int] = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
_lowerCamelCase : Dict = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_lowerCamelCase )
_lowerCamelCase : List[str] = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_lowerCamelCase : Union[str, Any] = weights[F"""layers_{lyr_num}"""]
_lowerCamelCase : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
_lowerCamelCase : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
_lowerCamelCase : Union[str, Any] = ly_weight["self_attention"]
_lowerCamelCase : int = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_lowerCamelCase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_lowerCamelCase : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_lowerCamelCase : int = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_lowerCamelCase : Optional[int] = ly_weight["MultiHeadDotProductAttention_0"]
_lowerCamelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_lowerCamelCase : Any = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_lowerCamelCase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_lowerCamelCase : Any = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_lowerCamelCase : str = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
_lowerCamelCase : str = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
_lowerCamelCase : int = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
_lowerCamelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
_lowerCamelCase : int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
_lowerCamelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
_lowerCamelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
_lowerCamelCase : str = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def lowerCamelCase_( _lowerCamelCase ) -> Dict:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_lowerCamelCase : Dict = jnp.tree_util.tree_map(onp.array , _lowerCamelCase )
_lowerCamelCase : int = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
_lowerCamelCase : Tuple = os.path.join(args.checkpoint_path , ".." , "config.gin" )
_lowerCamelCase : Any = inference.parse_training_gin_file(_lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : int = inference.InferenceModel(args.checkpoint_path , _lowerCamelCase )
_lowerCamelCase : str = DDPMScheduler(beta_schedule="squaredcos_cap_v2" , variance_type="fixed_large" )
_lowerCamelCase : Dict = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
_lowerCamelCase : Dict = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["targets_context"] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
_lowerCamelCase : Optional[int] = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["targets_context"] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_lowerCamelCase : Union[str, Any] = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] , _lowerCamelCase )
_lowerCamelCase : Dict = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] , _lowerCamelCase )
_lowerCamelCase : List[Any] = load_decoder(ta_checkpoint["target"]["decoder"] , _lowerCamelCase )
_lowerCamelCase : List[Any] = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
_lowerCamelCase : Dict = SpectrogramDiffusionPipeline(
notes_encoder=_lowerCamelCase , continuous_encoder=_lowerCamelCase , decoder=_lowerCamelCase , scheduler=_lowerCamelCase , melgan=_lowerCamelCase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
_lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
parser.add_argument(
'''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
)
parser.add_argument(
'''--checkpoint_path''',
default=f'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help='''Path to the original jax model checkpoint.''',
)
_lowerCAmelCase : Dict = parser.parse_args()
main(args) | 340 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def lowerCamelCase_( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_lowerCamelCase : int = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def lowerCamelCase_( _lowerCamelCase ) -> Optional[int]:
'''simple docstring'''
_lowerCamelCase : Dict = plt.imshow(_lowerCamelCase )
fig.axes.get_xaxis().set_visible(_lowerCamelCase )
fig.axes.get_yaxis().set_visible(_lowerCamelCase )
plt.show()
def lowerCamelCase_( ) -> str:
'''simple docstring'''
_lowerCamelCase : Tuple = datetime.now()
_lowerCamelCase : Tuple = current_time.strftime("%H:%M:%S" )
return timestamp | 340 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : List[Any] = "Wav2Vec2FeatureExtractor"
a__ : int = "AutoTokenizer"
def __init__( self : Dict , _lowercase : Dict , _lowercase : Union[str, Any] ):
super().__init__(_lowercase , _lowercase )
__UpperCAmelCase = self.feature_extractor
__UpperCAmelCase = False
@classmethod
def a ( cls : List[str] , _lowercase : Optional[Any] , **_lowercase : Union[str, Any] ):
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 : Tuple , *_lowercase : Dict , **_lowercase : Any ):
# For backward compatibility
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 a ( self : Optional[int] , *_lowercase : Any , **_lowercase : Union[str, Any] ):
# For backward compatibility
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 a ( self : Dict , *_lowercase : Dict , **_lowercase : Dict ):
return self.tokenizer.batch_decode(*_lowercase , **_lowercase )
def a ( self : Optional[Any] , *_lowercase : Any , **_lowercase : int ):
return self.tokenizer.decode(*_lowercase , **_lowercase )
@contextmanager
def a ( self : Optional[int] ):
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
| 332 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowercase : Union[str, Any] = transforms.Compose(
[
transforms.Resize((2_56, 2_56)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def lowercase__ ( snake_case_ :List[Any] ):
if isinstance(snake_case_ , torch.Tensor ):
return image
elif isinstance(snake_case_ , PIL.Image.Image ):
__UpperCAmelCase = [image]
__UpperCAmelCase = [trans(img.convert('''RGB''' ) ) for img in image]
__UpperCAmelCase = torch.stack(snake_case_ )
return image
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : Any , _lowercase : str , _lowercase : str ):
super().__init__()
# make sure scheduler can always be converted to DDIM
__UpperCAmelCase = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_lowercase , scheduler=_lowercase )
def a ( self : int , _lowercase : List[str] ):
if strength < 0 or strength > 1:
raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def a ( self : List[Any] , _lowercase : List[Any] , _lowercase : Optional[Any] , _lowercase : int ):
# get the original timestep using init_timestep
__UpperCAmelCase = min(int(num_inference_steps * strength ) , _lowercase )
__UpperCAmelCase = max(num_inference_steps - init_timestep , 0 )
__UpperCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a ( self : Optional[Any] , _lowercase : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : Optional[Any] , _lowercase : List[str] , _lowercase : Tuple , _lowercase : Optional[int]=None ):
if not isinstance(_lowercase , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase )}''' )
__UpperCAmelCase = image.to(device=_lowercase , dtype=_lowercase )
if isinstance(_lowercase , _lowercase ) and len(_lowercase ) != batch_size:
raise ValueError(
F'''You have passed a list of generators of length {len(_lowercase )}, but requested an effective batch'''
F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
__UpperCAmelCase = init_latents.shape
__UpperCAmelCase = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
# get latents
print('''add noise to latents at timestep''' , _lowercase )
__UpperCAmelCase = self.scheduler.add_noise(_lowercase , _lowercase , _lowercase )
__UpperCAmelCase = init_latents
return latents
@torch.no_grad()
def __call__( self : Any , _lowercase : Union[torch.FloatTensor, PIL.Image.Image] = None , _lowercase : float = 0.8 , _lowercase : int = 1 , _lowercase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase : float = 0.0 , _lowercase : int = 50 , _lowercase : Optional[bool] = None , _lowercase : Optional[str] = "pil" , _lowercase : bool = True , ):
self.check_inputs(_lowercase )
# 2. Preprocess image
__UpperCAmelCase = preprocess(_lowercase )
# 3. set timesteps
self.scheduler.set_timesteps(_lowercase , device=self.device )
__UpperCAmelCase , __UpperCAmelCase = self.get_timesteps(_lowercase , _lowercase , self.device )
__UpperCAmelCase = timesteps[:1].repeat(_lowercase )
# 4. Prepare latent variables
__UpperCAmelCase = self.prepare_latents(_lowercase , _lowercase , _lowercase , self.unet.dtype , self.device , _lowercase )
__UpperCAmelCase = latents
# 5. Denoising loop
for t in self.progress_bar(_lowercase ):
# 1. predict noise model_output
__UpperCAmelCase = self.unet(_lowercase , _lowercase ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__UpperCAmelCase = self.scheduler.step(
_lowercase , _lowercase , _lowercase , eta=_lowercase , use_clipped_model_output=_lowercase , generator=_lowercase , ).prev_sample
__UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
__UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__UpperCAmelCase = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=_lowercase )
| 332 | 1 |
'''simple docstring'''
def a_ ( __snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def a_ ( __snake_case : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =credit_card_number
lowerCamelCase_ =0
lowerCamelCase_ =len(a__ ) - 2
for i in range(a__ , -1 , -2 ):
# double the value of every second digit
lowerCamelCase_ =int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
lowerCamelCase_ =cc_number[:i] + str(a__ ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(a__ ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def a_ ( __snake_case : Any ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''' )
return False
if not 13 <= len(a__ ) <= 16:
print(F'''{error_message} of its length.''' )
return False
if not validate_initial_digits(a__ ):
print(F'''{error_message} of its first two digits.''' )
return False
if not luhn_validation(a__ ):
print(F'''{error_message} it fails the Luhn check.''' )
return False
print(F'''{credit_card_number} is a valid credit card number.''' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("""4111111111111111""")
validate_credit_card_number("""32323""")
| 368 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def a_ ( __snake_case : str , __snake_case : bool = True , __snake_case : float = math.inf , __snake_case : float = -math.inf , __snake_case : float = math.inf , __snake_case : float = -math.inf , __snake_case : bool = False , __snake_case : float = 100 , __snake_case : float = 0.0_1 , __snake_case : float = 1 , ) -> Any:
"""simple docstring"""
lowerCamelCase_ =False
lowerCamelCase_ =search_prob
lowerCamelCase_ =start_temperate
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =None
while not search_end:
lowerCamelCase_ =current_state.score()
if best_state is None or current_score > best_state.score():
lowerCamelCase_ =current_state
scores.append(__snake_case )
iterations += 1
lowerCamelCase_ =None
lowerCamelCase_ =current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
lowerCamelCase_ =random.randint(0 , len(__snake_case ) - 1 ) # picking a random neighbor
lowerCamelCase_ =neighbors.pop(__snake_case )
lowerCamelCase_ =picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
lowerCamelCase_ =change * -1 # in case we are finding minimum
if change > 0: # improves the solution
lowerCamelCase_ =picked_neighbor
else:
lowerCamelCase_ =(math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
lowerCamelCase_ =picked_neighbor
lowerCamelCase_ =current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
lowerCamelCase_ =True
else:
lowerCamelCase_ =next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(__snake_case ) , __snake_case )
plt.xlabel('''Iterations''' )
plt.ylabel('''Function values''' )
plt.show()
return best_state
if __name__ == "__main__":
def a_ ( __snake_case : List[str] , __snake_case : Optional[int] ) -> str:
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
a_ : str = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
a_ : Optional[int] = simulated_annealing(
prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
a_ : str = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
a_ : List[str] = simulated_annealing(
prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def a_ ( __snake_case : Dict , __snake_case : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return (3 * x**2) - (6 * y)
a_ : Tuple = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
a_ : Optional[Any] = simulated_annealing(prob, find_max=False, visualization=True)
print(
"""The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F"""{local_min.score()}"""
)
a_ : Dict = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
a_ : Optional[int] = simulated_annealing(prob, find_max=True, visualization=True)
print(
"""The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F"""{local_min.score()}"""
)
| 6 | 0 |
import argparse
__UpperCamelCase : Dict = "docs/source/_static/js/custom.js"
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCamelCase__ : List[str] = f.readlines()
UpperCamelCase__ : Dict = 0
# First let's put the right version
while not lines[index].startswith('''const stableVersion =''' ):
index += 1
UpperCamelCase__ : int = F"const stableVersion = \"v{version}\"\n"
# Then update the dictionary
while not lines[index].startswith('''const versionMapping = {''' ):
index += 1
# We go until the end
while not lines[index].startswith('''}''' ):
index += 1
# We add the new version at the end
lines[index - 1] += F" \"v{version}\": \"v{version}\",\n"
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument("--version", help="Release version.")
__UpperCamelCase : Any = parser.parse_args()
update_custom_js(args.version)
| 146 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : List[Any] = LxmertConfig.from_json_file(SCREAMING_SNAKE_CASE )
print(F"Building PyTorch model from configuration: {config}" )
UpperCamelCase__ : List[str] = LxmertForPreTraining(SCREAMING_SNAKE_CASE )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__UpperCamelCase : Any = 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 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 : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 146 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : Tuple ,_snake_case : bool = True ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : Optional[Any] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : str = size if size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case )
lowercase__ : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case ,param_name='''crop_size''' )
lowercase__ : Tuple = do_resize
lowercase__ : List[Any] = do_rescale
lowercase__ : Any = do_normalize
lowercase__ : List[str] = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : Union[str, Any] = size
lowercase__ : Any = resample
lowercase__ : int = rescale_factor
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : str = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : str ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "shortest_edge" in size:
lowercase__ : str = get_resize_output_image_size(_snake_case ,size=size['''shortest_edge'''] ,default_to_square=_snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase__ : int = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : np.ndarray ,_snake_case : float ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : int = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : Union[str, ChannelDimension] = ChannelDimension.FIRST ,**_snake_case : List[str] ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Tuple = get_size_dict(_snake_case ,param_name='''crop_size''' ,default_to_square=_snake_case )
lowercase__ : Tuple = resample if resample is not None else self.resample
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : int = get_size_dict(_snake_case )
if not is_batched(_snake_case ):
lowercase__ : Optional[Any] = [images]
if not valid_images(_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:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
lowercase__ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : int = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
lowercase__ : str = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
lowercase__ : Optional[Any] = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
lowercase__ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Any = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 302 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : int = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class __snake_case ( a ):
UpperCAmelCase__ : Optional[int] = '''transfo-xl'''
UpperCAmelCase__ : Optional[Any] = ['''mems''']
UpperCAmelCase__ : Optional[Any] = {
'''n_token''': '''vocab_size''',
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : List[str] , _snake_case : Union[str, Any]=267735 , _snake_case : str=[20000, 40000, 200000] , _snake_case : List[Any]=1024 , _snake_case : Any=1024 , _snake_case : Optional[Any]=16 , _snake_case : Tuple=64 , _snake_case : Dict=4096 , _snake_case : Optional[int]=4 , _snake_case : Optional[int]=False , _snake_case : Optional[int]=18 , _snake_case : List[str]=1600 , _snake_case : Any=1000 , _snake_case : Tuple=True , _snake_case : Optional[Any]=True , _snake_case : Optional[Any]=0 , _snake_case : List[str]=-1 , _snake_case : str=True , _snake_case : List[Any]=0.1 , _snake_case : int=0.0 , _snake_case : Optional[int]=True , _snake_case : Optional[int]="normal" , _snake_case : Optional[Any]=0.0_1 , _snake_case : Tuple=0.0_1 , _snake_case : Tuple=0.0_2 , _snake_case : Optional[int]=1e-5 , _snake_case : Union[str, Any]=0 , **_snake_case : Optional[Any] , ):
"""simple docstring"""
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = []
self.cutoffs.extend(_snake_case)
if proj_share_all_but_first:
UpperCAmelCase_ = [False] + [True] * len(self.cutoffs)
else:
UpperCAmelCase_ = [False] + [False] * len(self.cutoffs)
UpperCAmelCase_ = d_model
UpperCAmelCase_ = d_embed
UpperCAmelCase_ = d_head
UpperCAmelCase_ = d_inner
UpperCAmelCase_ = div_val
UpperCAmelCase_ = pre_lnorm
UpperCAmelCase_ = n_layer
UpperCAmelCase_ = n_head
UpperCAmelCase_ = mem_len
UpperCAmelCase_ = same_length
UpperCAmelCase_ = attn_type
UpperCAmelCase_ = clamp_len
UpperCAmelCase_ = sample_softmax
UpperCAmelCase_ = adaptive
UpperCAmelCase_ = dropout
UpperCAmelCase_ = dropatt
UpperCAmelCase_ = untie_r
UpperCAmelCase_ = init
UpperCAmelCase_ = init_range
UpperCAmelCase_ = proj_init_std
UpperCAmelCase_ = init_std
UpperCAmelCase_ = layer_norm_epsilon
super().__init__(eos_token_id=_snake_case , **_snake_case)
@property
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
return -1
@max_position_embeddings.setter
def lowerCamelCase ( self : Union[str, Any] , _snake_case : Dict):
"""simple docstring"""
raise NotImplementedError(
F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
| 51 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__lowercase = logging.get_logger(__name__)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Optional[Any] = ["""pixel_values"""]
def __init__( self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , ) -> None:
__UpperCamelCase :Optional[int] = do_resize
__UpperCamelCase :Any = do_rescale
__UpperCamelCase :str = size_divisor
__UpperCamelCase :Dict = resample
super().__init__(**__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase) -> np.ndarray:
__UpperCamelCase , __UpperCamelCase :int = get_image_size(__lowercase)
# Rounds the height and width down to the closest multiple of size_divisor
__UpperCamelCase :List[Any] = height // size_divisor * size_divisor
__UpperCamelCase :List[str] = width // size_divisor * size_divisor
__UpperCamelCase :str = resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
return image
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase = None , **__lowercase) -> np.ndarray:
return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , ) -> BatchFeature:
__UpperCamelCase :Union[str, Any] = do_resize if do_resize is not None else self.do_resize
__UpperCamelCase :Tuple = do_rescale if do_rescale is not None else self.do_rescale
__UpperCamelCase :List[str] = size_divisor if size_divisor is not None else self.size_divisor
__UpperCamelCase :List[Any] = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError('''size_divisor is required for resizing''')
__UpperCamelCase :List[Any] = make_list_of_images(__lowercase)
if not valid_images(__lowercase):
raise ValueError('''Invalid image(s)''')
# All transformations expect numpy arrays.
__UpperCamelCase :Optional[Any] = [to_numpy_array(__lowercase) for img in images]
if do_resize:
__UpperCamelCase :List[str] = [self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]
if do_rescale:
__UpperCamelCase :Dict = [self.rescale(__lowercase , scale=1 / 255) for image in images]
__UpperCamelCase :str = [to_channel_dimension_format(__lowercase , __lowercase) for image in images]
__UpperCamelCase :int = {'''pixel_values''': images}
return BatchFeature(data=__lowercase , tensor_type=__lowercase)
| 43 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(lowercase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 176 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[Any] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Tuple = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Dict = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Any = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCamelCase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 176 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class _lowerCamelCase ( a ):
"""simple docstring"""
UpperCAmelCase_ : Dict ="xlm-prophetnet"
UpperCAmelCase_ : int =["past_key_values"]
UpperCAmelCase_ : str ={
"num_attention_heads": "num_encoder_attention_heads",
}
def __init__( self , UpperCAmelCase = 0.1 , UpperCAmelCase = "gelu" , UpperCAmelCase = 30522 , UpperCAmelCase = 1024 , UpperCAmelCase = 4096 , UpperCAmelCase = 12 , UpperCAmelCase = 16 , UpperCAmelCase = 4096 , UpperCAmelCase = 12 , UpperCAmelCase = 16 , UpperCAmelCase = 0.1 , UpperCAmelCase = 0.1 , UpperCAmelCase = 512 , UpperCAmelCase = 0.02 , UpperCAmelCase = True , UpperCAmelCase = True , UpperCAmelCase = 0 , UpperCAmelCase = 2 , UpperCAmelCase = 32 , UpperCAmelCase = 128 , UpperCAmelCase = False , UpperCAmelCase = 0.0 , UpperCAmelCase = True , UpperCAmelCase = 0 , UpperCAmelCase = 1 , UpperCAmelCase = 2 , **UpperCAmelCase , ) -> str:
'''simple docstring'''
__snake_case : List[Any] = vocab_size
__snake_case : Optional[Any] = hidden_size
__snake_case : Union[str, Any] = encoder_ffn_dim
__snake_case : Optional[int] = num_encoder_layers
__snake_case : Optional[Any] = num_encoder_attention_heads
__snake_case : Union[str, Any] = decoder_ffn_dim
__snake_case : List[str] = num_decoder_layers
__snake_case : List[str] = num_decoder_attention_heads
__snake_case : str = max_position_embeddings
__snake_case : str = init_std # Normal(0, this parameter)
__snake_case : Optional[int] = activation_function
# parameters for xlmprophetnet
__snake_case : Tuple = ngram
__snake_case : Optional[int] = num_buckets
__snake_case : Optional[int] = relative_max_distance
__snake_case : Any = disable_ngram_loss
__snake_case : Optional[Any] = eps
# 3 Types of Dropout
__snake_case : List[Any] = attention_dropout
__snake_case : List[str] = activation_dropout
__snake_case : Optional[Any] = dropout
__snake_case : Union[str, Any] = use_cache
super().__init__(
pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , is_encoder_decoder=UpperCAmelCase , add_cross_attention=UpperCAmelCase , decoder_start_token_id=UpperCAmelCase , **UpperCAmelCase , )
@property
def UpperCAmelCase ( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def UpperCAmelCase ( self , UpperCAmelCase ) -> List[str]:
'''simple docstring'''
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
" `num_decoder_layers`." )
| 326 |
def lowerCAmelCase__( lowercase : list[int] , lowercase : int ) -> bool:
__snake_case : List[str] = len(lowercase )
__snake_case : int = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
__snake_case : Optional[Any] = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
__snake_case : Union[str, Any] = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
__snake_case : List[str] = subset[i - 1][j]
if arr[i - 1] <= j:
__snake_case : Union[str, Any] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 326 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : Dict = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : str = StableDiffusionLatentUpscalePipeline
lowercase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'height',
'width',
'cross_attention_kwargs',
'negative_prompt_embeds',
'prompt_embeds',
}
lowercase : str = PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
lowercase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowercase : Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : List[str] = frozenset([] )
lowercase : str = True
@property
def a_ ( self ):
UpperCamelCase : Tuple = 1
UpperCamelCase : List[str] = 4
UpperCamelCase : str = (16, 16)
UpperCamelCase : List[Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
return image
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = UNetaDConditionModel(
act_fn="""gelu""" , attention_head_dim=8 , norm_num_groups=SCREAMING_SNAKE_CASE_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"""KDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
) , in_channels=8 , mid_block_type=SCREAMING_SNAKE_CASE_ , only_cross_attention=SCREAMING_SNAKE_CASE_ , out_channels=5 , resnet_time_scale_shift="""scale_shift""" , time_embedding_type="""fourier""" , timestep_post_act="""gelu""" , up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") , )
UpperCamelCase : Optional[Any] = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
UpperCamelCase : Optional[int] = EulerDiscreteScheduler(prediction_type="""sample""" )
UpperCamelCase : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""quick_gelu""" , projection_dim=512 , )
UpperCamelCase : int = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
UpperCamelCase : Tuple = {
"""unet""": model.eval(),
"""vae""": vae.eval(),
"""scheduler""": scheduler,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : Any = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Optional[int] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": self.dummy_image.cpu(),
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self ):
UpperCamelCase : Union[str, Any] = """cpu"""
UpperCamelCase : Optional[Any] = self.get_dummy_components()
UpperCamelCase : int = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = pipe(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : Tuple = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
UpperCamelCase : Optional[int] = np.array(
[0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] )
UpperCamelCase : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
def a_ ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=7e-3 )
def a_ ( self ):
super().test_cpu_offload_forward_pass(expected_max_diff=3e-3 )
def a_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def a_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=7e-3 )
def a_ ( self ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3 )
def a_ ( self ):
super().test_save_load_local(expected_max_difference=3e-3 )
def a_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def a_ ( self ):
UpperCamelCase : Dict = [
"""DDIMScheduler""",
"""DDPMScheduler""",
"""PNDMScheduler""",
"""HeunDiscreteScheduler""",
"""EulerAncestralDiscreteScheduler""",
"""KDPM2DiscreteScheduler""",
"""KDPM2AncestralDiscreteScheduler""",
"""DPMSolverSDEScheduler""",
]
UpperCamelCase : List[Any] = self.get_dummy_components()
UpperCamelCase : Tuple = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = 2
UpperCamelCase : Dict = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
UpperCamelCase : Optional[int] = getattr(SCREAMING_SNAKE_CASE_ , scheduler_enum.name )
UpperCamelCase : Optional[int] = scheduler_cls.from_config(pipe.scheduler.config )
UpperCamelCase : Dict = pipe(**SCREAMING_SNAKE_CASE_ )[0]
outputs.append(SCREAMING_SNAKE_CASE_ )
assert check_same_shape(SCREAMING_SNAKE_CASE_ )
@require_torch_gpu
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self ):
UpperCamelCase : Tuple = torch.manual_seed(33 )
UpperCamelCase : List[Any] = StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" , torch_dtype=torch.floataa )
pipe.to("""cuda""" )
UpperCamelCase : List[str] = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
UpperCamelCase : Optional[Any] = """a photo of an astronaut high resolution, unreal engine, ultra realistic"""
UpperCamelCase : str = pipe(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , output_type="""latent""" ).images
UpperCamelCase : Tuple = upscaler(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , num_inference_steps=20 , guidance_scale=0 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , ).images[0]
UpperCamelCase : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy""" )
assert np.abs((expected_image - image).mean() ) < 5e-2
def a_ ( self ):
UpperCamelCase : List[Any] = torch.manual_seed(33 )
UpperCamelCase : List[Any] = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
UpperCamelCase : List[str] = """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""
UpperCamelCase : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png""" )
UpperCamelCase : List[str] = upscaler(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , num_inference_steps=20 , guidance_scale=0 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , ).images[0]
UpperCamelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy""" )
assert np.abs((expected_image - image).max() ) < 5e-2
| 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(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 : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( 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_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = 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.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
__snake_case : List[Any] = XLMRobertaTokenizer
__snake_case : Optional[Any] = XLMRobertaTokenizerFast
__snake_case : Optional[Any] = True
__snake_case : int = True
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """<pad>"""
_SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) , UpperCAmelCase_ )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(UpperCAmelCase_ ) , 1_002 )
def UpperCamelCase ( self: Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_002 )
def UpperCamelCase ( self: str ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 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]] , )
_SCREAMING_SNAKE_CASE = 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""",
""".""",
] , )
_SCREAMING_SNAKE_CASE = 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]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_SCREAMING_SNAKE_CASE = 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 UpperCamelCase ( self: str ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 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 ) )
_SCREAMING_SNAKE_CASE = 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
_SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 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_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=True
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(UpperCAmelCase_ , legacy_format=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Checks everything loads correctly in the same way
_SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 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
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(UpperCAmelCase_ , legacy_format=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 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
_SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 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_ )
@cached_property
def UpperCamelCase ( self: Any ):
'''simple docstring'''
return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(UpperCAmelCase_ , f.name )
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizer(f.name , keep_accents=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = pickle.dumps(UpperCAmelCase_ )
pickle.loads(UpperCAmelCase_ )
def UpperCamelCase ( self: int ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """Hello World!"""
_SCREAMING_SNAKE_CASE = [0, 35_378, 6_661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(UpperCAmelCase_ , self.big_tokenizer.encode(UpperCAmelCase_ ) )
@slow
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
_SCREAMING_SNAKE_CASE = [
0,
3_293,
83,
10,
4_552,
4_989,
7_986,
678,
10,
5_915,
111,
179_459,
124_850,
4,
6_044,
237,
12,
6,
5,
6,
4,
6_780,
705,
15,
1_388,
44,
378,
10_114,
711,
152,
20,
6,
5,
22_376,
642,
1_221,
15_190,
34_153,
450,
5_608,
959,
1_119,
57_702,
136,
186,
47,
1_098,
29_367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_044,
237,
6_284,
50_901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(UpperCAmelCase_ , self.big_tokenizer.encode(UpperCAmelCase_ ) )
@slow
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=UpperCAmelCase_ , model_name="""xlm-roberta-base""" , revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" , )
| 306 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase = {
'''tokenizer_file''': {
'''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
},
}
class __UpperCAmelCase (_UpperCAmelCase ):
__snake_case : Tuple = VOCAB_FILES_NAMES
__snake_case : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__snake_case : Optional[Any] = ["input_ids", "attention_mask"]
__snake_case : Optional[int] = None
def __init__( self: Dict , UpperCAmelCase_: Union[str, Any]=None , UpperCAmelCase_: str=None , UpperCAmelCase_: Optional[int]=None , UpperCAmelCase_: int="<unk>" , UpperCAmelCase_: List[str]="<s>" , UpperCAmelCase_: Tuple="</s>" , UpperCAmelCase_: List[Any]="<pad>" , UpperCAmelCase_: Dict=False , UpperCAmelCase_: Dict=False , **UpperCAmelCase_: Dict , ):
'''simple docstring'''
super().__init__(
UpperCAmelCase_ , UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , add_prefix_space=UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_ , **UpperCAmelCase_ , )
_SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , UpperCAmelCase_ ) != add_prefix_space:
_SCREAMING_SNAKE_CASE = getattr(UpperCAmelCase_ , pre_tok_state.pop("""type""" ) )
_SCREAMING_SNAKE_CASE = add_prefix_space
_SCREAMING_SNAKE_CASE = pre_tok_class(**UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = add_prefix_space
def UpperCamelCase ( self: List[str] , *UpperCAmelCase_: Any , **UpperCAmelCase_: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = kwargs.get("""is_split_into_words""" , UpperCAmelCase_ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'
""" pretokenized inputs.""" )
return super()._batch_encode_plus(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCamelCase ( self: Union[str, Any] , *UpperCAmelCase_: Dict , **UpperCAmelCase_: Union[str, Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = kwargs.get("""is_split_into_words""" , UpperCAmelCase_ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'
""" pretokenized inputs.""" )
return super()._encode_plus(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: str , UpperCAmelCase_: Optional[str] = None ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_ )
return tuple(UpperCAmelCase_ )
def UpperCamelCase ( self: Tuple , UpperCAmelCase_: "Conversation" ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ ) + [self.eos_token_id] )
if len(UpperCAmelCase_ ) > self.model_max_length:
_SCREAMING_SNAKE_CASE = input_ids[-self.model_max_length :]
return input_ids
| 306 | 1 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase_( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = 9
_lowerCamelCase : Tuple = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_lowerCamelCase : str = kruskal(_lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : Optional[Any] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_lowerCamelCase ) == sorted(_lowerCamelCase ) | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
_lowerCamelCase : list = []
for char_count in range(_lowerCamelCase ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(_lowerCamelCase )
if __name__ == "__main__":
print(alternative_string_arrange('''AB''', '''XYZ'''), end=''' ''') | 340 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
A__ = FileLock(str(tmpdir / 'foo.lock' ) )
A__ = FileLock(str(tmpdir / 'foo.lock' ) )
A__ = 0.0_1
with locka.acquire():
with pytest.raises(_lowerCamelCase ):
A__ = time.time()
locka.acquire(_lowerCamelCase )
assert time.time() - _start > timeout
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
A__ = "a" * 1_000 + ".lock"
A__ = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(_lowerCamelCase )
assert len(os.path.basename(locka._lock_file ) ) <= 255
A__ = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(_lowerCamelCase ):
locka.acquire(0 )
| 221 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = BartphoTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : str = ["▁This", "▁is", "▁a", "▁t", "est"]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[Any] = {"unk_token": "<unk>"}
_lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
with open(self.monolingual_vocab_file, "w", encoding="utf-8") as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n")
_lowerCAmelCase : Optional[Any] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BartphoTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "This is a là test"
_lowerCAmelCase : Optional[int] = "This is a<unk><unk> test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
_lowerCAmelCase : List[Any] = "This is a là test"
_lowerCAmelCase : str = "▁This ▁is ▁a ▁l à ▁t est".split()
_lowerCAmelCase : str = tokenizer.tokenize(__a)
self.assertListEqual(__a, __a)
_lowerCAmelCase : Tuple = tokens + [tokenizer.unk_token]
_lowerCAmelCase : List[str] = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), __a)
| 36 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 'ylacombe/bark-small'
_snake_case = tempfile.mkdtemp()
_snake_case = 'en_speaker_1'
_snake_case = 'This is a test string'
_snake_case = 'speaker_embeddings_path.json'
_snake_case = 'speaker_embeddings'
def lowerCamelCase ( self , **lowerCAmelCase_ ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.get_tokenizer()
_snake_case = BarkProcessor(tokenizer=lowerCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
_snake_case = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
_snake_case = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
_snake_case = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
_snake_case = 35
_snake_case = 2
_snake_case = 8
_snake_case = {
'semantic_prompt': np.ones(lowerCAmelCase_ ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
_snake_case = processor(text=self.input_string , voice_preset=lowerCAmelCase_ )
_snake_case = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowerCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
_snake_case = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(lowerCAmelCase_ , **lowerCAmelCase_ )
_snake_case = processor(text=self.input_string , voice_preset=lowerCAmelCase_ )
_snake_case = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowerCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
_snake_case = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.get_tokenizer()
_snake_case = BarkProcessor(tokenizer=lowerCAmelCase_ )
_snake_case = processor(text=self.input_string )
_snake_case = tokenizer(
self.input_string , padding='max_length' , max_length=2_56 , add_special_tokens=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 359 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __UpperCAmelCase :
@staticmethod
def lowerCamelCase ( *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def SCREAMING_SNAKE_CASE__ ( __A ) -> Dict:
_snake_case = np.array(__A )
_snake_case = npimg.shape
return {"hash": hashimage(__A ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
__lowercase = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__lowercase = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = MaskGenerationPipeline(model=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@slow
@require_torch
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = pipeline('mask-generation' , model='facebook/sam-vit-huge' )
_snake_case = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' , points_per_batch=2_56 )
# Shortening by hashing
_snake_case = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCAmelCase_ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (4_80, 6_40)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.021},
{'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0053},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_80, 6_40)}, 'scores': 0.9967},
{'mask': {'hash': '453c7844bd', 'shape': (4_80, 6_40)}, 'scores': 0.993},
{'mask': {'hash': '3d44f2926d', 'shape': (4_80, 6_40)}, 'scores': 0.9909},
{'mask': {'hash': '64033ddc3f', 'shape': (4_80, 6_40)}, 'scores': 0.9879},
{'mask': {'hash': '801064ff79', 'shape': (4_80, 6_40)}, 'scores': 0.9834},
{'mask': {'hash': '6172f276ef', 'shape': (4_80, 6_40)}, 'scores': 0.9716},
{'mask': {'hash': 'b49e60e084', 'shape': (4_80, 6_40)}, 'scores': 0.9612},
{'mask': {'hash': 'a811e775fd', 'shape': (4_80, 6_40)}, 'scores': 0.9599},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_80, 6_40)}, 'scores': 0.9552},
{'mask': {'hash': '9d8257e080', 'shape': (4_80, 6_40)}, 'scores': 0.9532},
{'mask': {'hash': '32de6454a8', 'shape': (4_80, 6_40)}, 'scores': 0.9516},
{'mask': {'hash': 'af3d4af2c8', 'shape': (4_80, 6_40)}, 'scores': 0.9499},
{'mask': {'hash': '3c6db475fb', 'shape': (4_80, 6_40)}, 'scores': 0.9483},
{'mask': {'hash': 'c290813fb9', 'shape': (4_80, 6_40)}, 'scores': 0.9464},
{'mask': {'hash': 'b6f0b8f606', 'shape': (4_80, 6_40)}, 'scores': 0.943},
{'mask': {'hash': '92ce16bfdf', 'shape': (4_80, 6_40)}, 'scores': 0.943},
{'mask': {'hash': 'c749b25868', 'shape': (4_80, 6_40)}, 'scores': 0.9408},
{'mask': {'hash': 'efb6cab859', 'shape': (4_80, 6_40)}, 'scores': 0.9335},
{'mask': {'hash': '1ff2eafb30', 'shape': (4_80, 6_40)}, 'scores': 0.9326},
{'mask': {'hash': '788b798e24', 'shape': (4_80, 6_40)}, 'scores': 0.9262},
{'mask': {'hash': 'abea804f0e', 'shape': (4_80, 6_40)}, 'scores': 0.8999},
{'mask': {'hash': '7b9e8ddb73', 'shape': (4_80, 6_40)}, 'scores': 0.8986},
{'mask': {'hash': 'cd24047c8a', 'shape': (4_80, 6_40)}, 'scores': 0.8984},
{'mask': {'hash': '6943e6bcbd', 'shape': (4_80, 6_40)}, 'scores': 0.8873},
{'mask': {'hash': 'b5f47c9191', 'shape': (4_80, 6_40)}, 'scores': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 'facebook/sam-vit-huge'
_snake_case = pipeline('mask-generation' , model=lowerCAmelCase_ )
_snake_case = image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
_snake_case = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCAmelCase_ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (4_80, 6_40)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.0210},
{'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0053},
] , )
| 160 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def a_ ( lowerCamelCase : int ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
lowerCAmelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCamelCase )
if is_prime(lowerCamelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCase ) - 1:
lowerCAmelCase = SYMBOLS[numerals[index]]
lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def a_ ( lowerCamelCase : int ):
lowerCAmelCase = ''
lowerCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
lowerCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def a_ ( lowerCamelCase : str = "/p089_roman.txt" ):
lowerCAmelCase = 0
with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
lowerCAmelCase = filea.readlines()
for line in lines:
lowerCAmelCase = line.strip()
lowerCAmelCase = parse_roman_numerals(lowerCamelCase )
lowerCAmelCase = generate_roman_numerals(lowerCamelCase )
savings += len(lowerCamelCase ) - len(lowerCamelCase )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 4 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase_ ( __lowerCAmelCase = 8 )-> str:
'''simple docstring'''
UpperCAmelCase : str =ascii_letters + digits + punctuation
return "".join(secrets.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
i -= len(__lowerCAmelCase )
UpperCAmelCase : Tuple =i // 3
UpperCAmelCase : Tuple =i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
UpperCAmelCase : Optional[int] =(
chars_incl
+ random(__lowerCAmelCase , quotient + remainder )
+ random(__lowerCAmelCase , __lowerCAmelCase )
+ random(__lowerCAmelCase , __lowerCAmelCase )
)
UpperCAmelCase : List[Any] =list(__lowerCAmelCase )
shuffle(__lowerCAmelCase )
return "".join(__lowerCAmelCase )
# random is a generalised function for letters, characters and numbers
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
return "".join(secrets.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
pass # Put your code here...
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
pass # Put your code here...
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
pass # Put your code here...
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 8 )-> bool:
'''simple docstring'''
if len(__lowerCAmelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
UpperCAmelCase : Optional[Any] =any(char in ascii_uppercase for char in password )
UpperCAmelCase : str =any(char in ascii_lowercase for char in password )
UpperCAmelCase : Tuple =any(char in digits for char in password )
UpperCAmelCase : List[str] =any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCAmelCase_ ( )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Dict =int(input('''Please indicate the max length of your password: ''' ).strip() )
UpperCAmelCase : str =input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''' , password_generator(__lowerCAmelCase ) )
print(
'''Alternative Password generated:''' , alternative_password_generator(__lowerCAmelCase , __lowerCAmelCase ) , )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 78 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 78 | 1 |
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 DetrImageProcessor
class lowercase ( unittest.TestCase ):
def __init__( self , _a , _a=7 , _a=3 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=1 / 255 , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , _a=True , ) -> Dict:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_A : List[str] = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
_A : int = parent
_A : Optional[Any] = batch_size
_A : List[str] = num_channels
_A : Dict = min_resolution
_A : Union[str, Any] = max_resolution
_A : Optional[Any] = do_resize
_A : Optional[int] = size
_A : Optional[Any] = do_rescale
_A : Optional[Any] = rescale_factor
_A : Optional[int] = do_normalize
_A : List[str] = image_mean
_A : Optional[Any] = image_std
_A : Union[str, Any] = do_pad
def a__ ( self ) -> Dict:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def a__ ( self , _a , _a=False ) -> str:
if not batched:
_A : Optional[Any] = image_inputs[0]
if isinstance(_a , Image.Image ):
_A , _A : Union[str, Any] = image.size
else:
_A , _A : int = image.shape[1], image.shape[2]
if w < h:
_A : Optional[int] = int(self.size["""shortest_edge"""] * h / w )
_A : Optional[Any] = self.size["""shortest_edge"""]
elif w > h:
_A : List[Any] = self.size["""shortest_edge"""]
_A : List[str] = int(self.size["""shortest_edge"""] * w / h )
else:
_A : List[Any] = self.size["""shortest_edge"""]
_A : Any = self.size["""shortest_edge"""]
else:
_A : Optional[int] = []
for image in image_inputs:
_A , _A : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_A : List[Any] = max(_a , key=lambda _a : item[0] )[0]
_A : Tuple = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( UpperCamelCase__,unittest.TestCase ):
_a = DetrImageProcessor if is_vision_available() else None
def a__ ( self ) -> List[str]:
_A : List[str] = DetrImageProcessingTester(self )
@property
def a__ ( self ) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def a__ ( self ) -> Optional[Any]:
_A : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , """image_mean""" ) )
self.assertTrue(hasattr(_a , """image_std""" ) )
self.assertTrue(hasattr(_a , """do_normalize""" ) )
self.assertTrue(hasattr(_a , """do_rescale""" ) )
self.assertTrue(hasattr(_a , """rescale_factor""" ) )
self.assertTrue(hasattr(_a , """do_resize""" ) )
self.assertTrue(hasattr(_a , """size""" ) )
self.assertTrue(hasattr(_a , """do_pad""" ) )
def a__ ( self ) -> str:
_A : Optional[Any] = 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 , _a )
_A : Any = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_a )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , _a )
def a__ ( self ) -> Optional[int]:
pass
def a__ ( self ) -> int:
# Initialize image_processing
_A : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
_A : int = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
_A , _A : int = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A , _A : List[Any] = self.image_processor_tester.get_expected_values(_a , batched=_a )
_A : Dict = 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,
expected_height,
expected_width,
) , )
def a__ ( self ) -> List[Any]:
# Initialize image_processing
_A : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A : List[str] = 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
_A : List[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
_A , _A : Optional[int] = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A : Union[str, Any] = image_processing(_a , return_tensors="""pt""" ).pixel_values
_A , _A : Dict = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def a__ ( self ) -> Optional[int]:
# Initialize image_processing
_A : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A : Any = 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
_A : List[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
_A , _A : str = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A : Tuple = image_processing(_a , return_tensors="""pt""" ).pixel_values
_A , _A : List[str] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def a__ ( self ) -> Tuple:
# prepare image and target
_A : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
_A : Union[str, Any] = json.loads(f.read() )
_A : List[str] = {"""image_id""": 3_9769, """annotations""": target}
# encode them
_A : List[Any] = DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50""" )
_A : Any = image_processing(images=_a , annotations=_a , return_tensors="""pt""" )
# verify pixel values
_A : Tuple = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _a )
_A : str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
_A : Dict = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _a ) )
# verify boxes
_A : str = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _a )
_A : List[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _a , atol=1e-3 ) )
# verify image_id
_A : Tuple = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _a ) )
# verify is_crowd
_A : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _a ) )
# verify class_labels
_A : int = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _a ) )
# verify orig_size
_A : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _a ) )
# verify size
_A : Optional[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _a ) )
@slow
def a__ ( self ) -> Optional[int]:
# prepare image, target and masks_path
_A : str = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
_A : Any = json.loads(f.read() )
_A : Optional[Any] = {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}
_A : Tuple = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
_A : Optional[int] = DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic""" )
_A : Tuple = image_processing(images=_a , annotations=_a , masks_path=_a , return_tensors="""pt""" )
# verify pixel values
_A : str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _a )
_A : Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
_A : List[Any] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _a ) )
# verify boxes
_A : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _a )
_A : Any = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _a , atol=1e-3 ) )
# verify image_id
_A : Optional[Any] = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _a ) )
# verify is_crowd
_A : Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _a ) )
# verify class_labels
_A : Union[str, Any] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _a ) )
# verify masks
_A : List[str] = 82_2873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , _a )
# verify orig_size
_A : Union[str, Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _a ) )
# verify size
_A : Dict = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _a ) )
| 26 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self : List[Any] , _A : str , _A : str=13 , _A : Union[str, Any]=7 , _A : Tuple=True , _A : Dict=True , _A : List[str]=True , _A : Optional[int]=True , _A : Dict=99 , _A : Optional[Any]=32 , _A : Optional[int]=5 , _A : str=4 , _A : str=37 , _A : Tuple="gelu" , _A : Any=0.1 , _A : Dict=0.1 , _A : str=512 , _A : Tuple=16 , _A : str=2 , _A : int=0.02 , _A : int=False , _A : List[str]=True , _A : List[Any]="None" , _A : List[str]=3 , _A : Optional[Any]=4 , _A : Dict=None , ) -> Dict:
__magic_name__ : Union[str, Any] = parent
__magic_name__ : Any = batch_size
__magic_name__ : Optional[int] = seq_length
__magic_name__ : List[str] = is_training
__magic_name__ : Optional[Any] = use_input_mask
__magic_name__ : Dict = use_token_type_ids
__magic_name__ : str = use_labels
__magic_name__ : int = vocab_size
__magic_name__ : List[Any] = hidden_size
__magic_name__ : Dict = num_hidden_layers
__magic_name__ : Dict = num_attention_heads
__magic_name__ : Tuple = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : Union[str, Any] = hidden_dropout_prob
__magic_name__ : Union[str, Any] = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Any = type_vocab_size
__magic_name__ : Union[str, Any] = type_sequence_label_size
__magic_name__ : Union[str, Any] = initializer_range
__magic_name__ : str = num_labels
__magic_name__ : Tuple = num_choices
__magic_name__ : Any = relative_attention
__magic_name__ : str = position_biased_input
__magic_name__ : str = pos_att_type
__magic_name__ : Union[str, Any] = scope
def __lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : List[Any] = None
if self.use_input_mask:
__magic_name__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__magic_name__ : int = None
if self.use_token_type_ids:
__magic_name__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : List[str] = None
__magic_name__ : Tuple = None
__magic_name__ : Union[str, Any] = None
if self.use_labels:
__magic_name__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
__magic_name__ : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
return DebertaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __lowerCAmelCase ( self : str ) -> Optional[Any]:
__magic_name__ : List[Any] = self.get_config()
__magic_name__ : Union[str, Any] = 300
return config
def __lowerCAmelCase ( self : int , _A : Dict ) -> Tuple:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __lowerCAmelCase ( self : Any , _A : Optional[int] , _A : Optional[Any] , _A : Optional[int] , _A : Optional[int] , _A : Any , _A : str , _A : List[Any] ) -> List[Any]:
__magic_name__ : Dict = DebertaModel(config=_A )
model.to(_A )
model.eval()
__magic_name__ : Optional[Any] = model(_A , attention_mask=_A , token_type_ids=_A )[0]
__magic_name__ : Optional[int] = model(_A , token_type_ids=_A )[0]
__magic_name__ : List[str] = model(_A )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __lowerCAmelCase ( self : Any , _A : Union[str, Any] , _A : Optional[Any] , _A : Dict , _A : Optional[Any] , _A : Dict , _A : Optional[Any] , _A : Optional[int] ) -> Dict:
__magic_name__ : List[str] = DebertaForMaskedLM(config=_A )
model.to(_A )
model.eval()
__magic_name__ : List[str] = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : str , _A : Union[str, Any] , _A : List[str] , _A : Optional[int] , _A : Optional[int] , _A : str , _A : Union[str, Any] , _A : Any ) -> Union[str, Any]:
__magic_name__ : Optional[int] = self.num_labels
__magic_name__ : Optional[Any] = DebertaForSequenceClassification(_A )
model.to(_A )
model.eval()
__magic_name__ : Any = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(_A )
def __lowerCAmelCase ( self : Tuple , _A : str , _A : str , _A : int , _A : str , _A : int , _A : Optional[int] , _A : List[str] ) -> Optional[int]:
__magic_name__ : str = self.num_labels
__magic_name__ : int = DebertaForTokenClassification(config=_A )
model.to(_A )
model.eval()
__magic_name__ : List[str] = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self : Optional[Any] , _A : str , _A : Tuple , _A : Optional[int] , _A : Any , _A : Optional[int] , _A : Dict , _A : Union[str, Any] ) -> List[Any]:
__magic_name__ : int = DebertaForQuestionAnswering(config=_A )
model.to(_A )
model.eval()
__magic_name__ : Optional[int] = model(
_A , attention_mask=_A , token_type_ids=_A , start_positions=_A , end_positions=_A , )
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 __lowerCAmelCase ( self : Optional[int] ) -> List[Any]:
__magic_name__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) ,
) : int = config_and_inputs
__magic_name__ : Optional[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class _lowerCamelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
A_ : List[Any] = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
A_ : Tuple = (
{
"""feature-extraction""": DebertaModel,
"""fill-mask""": DebertaForMaskedLM,
"""question-answering""": DebertaForQuestionAnswering,
"""text-classification""": DebertaForSequenceClassification,
"""token-classification""": DebertaForTokenClassification,
"""zero-shot""": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ : Union[str, Any] = True
A_ : Any = False
A_ : Dict = False
A_ : str = False
A_ : Dict = False
def __lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
__magic_name__ : List[str] = DebertaModelTester(self )
__magic_name__ : Tuple = ConfigTester(self , config_class=_A , hidden_size=37 )
def __lowerCAmelCase ( self : List[str] ) -> Tuple:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
__magic_name__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*_A )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
__magic_name__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*_A )
def __lowerCAmelCase ( self : Any ) -> str:
__magic_name__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*_A )
def __lowerCAmelCase ( self : Any ) -> Tuple:
__magic_name__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*_A )
def __lowerCAmelCase ( self : str ) -> List[Any]:
__magic_name__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*_A )
@slow
def __lowerCAmelCase ( self : str ) -> Optional[Any]:
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : int = DebertaModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
pass
@slow
def __lowerCAmelCase ( self : Dict ) -> Tuple:
__magic_name__ : int = DebertaModel.from_pretrained('microsoft/deberta-base' )
__magic_name__ : List[Any] = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__magic_name__ : Union[str, Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__magic_name__ : Optional[int] = model(_A , attention_mask=_A )[0]
# compare the actual values for a slice.
__magic_name__ : Tuple = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _A , atol=1E-4 ) , F'{output[:, 1:4, 1:4]}' ) | 331 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : str = [True] * n
A : Union[str, Any] = False
A : Optional[Any] = False
A : List[str] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
A : Dict = i * 2
while index < n:
A : int = False
A : int = index + i
A : Union[str, Any] = [2]
for i in range(3 , snake_case__ , 2 ):
if is_prime[i]:
primes.append(snake_case__ )
return primes
def lowerCAmelCase_ ( snake_case__ = 9999_6666_3333 ):
'''simple docstring'''
A : Any = math.floor(math.sqrt(snake_case__ ) ) + 100
A : List[Any] = prime_sieve(snake_case__ )
A : Optional[int] = 0
A : Any = 0
A : str = primes[prime_index]
while (last_prime**2) <= limit:
A : Any = primes[prime_index + 1]
A : Any = last_prime**2
A : Dict = next_prime**2
# Get numbers divisible by lps(current)
A : str = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
A : Dict = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
A : Tuple = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
A : List[str] = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 311 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(snake_case__ )
if n > 1:
factors.append(snake_case__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = int(np.ceil((x_end - xa) / step_size ) )
lowerCamelCase_ = np.zeros((n + 1,) )
lowerCamelCase_ = ya
lowerCamelCase_ = xa
for k in range(lowerCamelCase__ ):
lowerCamelCase_ = y[k] + step_size * ode_func(lowerCamelCase__ , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 19 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(4_2)
A : List[str] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
A : Optional[int] = 'zero2'
A : str = 'zero3'
A : Tuple = [ZEROa, ZEROa]
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Tuple:
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
__a = parameterized.to_safe_name('''_'''.join(str(a__ ) for x in param.args ) )
return F"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
A : Union[str, Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __A( a ):
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Any:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@require_torch_multi_gpu
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> int:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> str:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@require_torch_multi_gpu
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = 10 , _snake_case = True , _snake_case = True , _snake_case = True , ) -> Any:
'''simple docstring'''
__a = models[model]
__a = self.run_trainer(
stage=_snake_case , model_name=_snake_case , eval_steps=_snake_case , num_train_epochs=1 , distributed=_snake_case , fpaa=_snake_case , )
self.do_checks(_snake_case )
return output_dir
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = 10 , _snake_case = 1 , _snake_case = True , _snake_case = True , ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_auto_remove_tmp_dir('''./xxx''' , after=_snake_case )
__a = F"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(_snake_case )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(['''--fp16'''] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__a = F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
__a = [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
__a = self.get_launcher(_snake_case )
__a = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_snake_case , env=self.get_env() )
return output_dir
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=False ) -> List[str]:
'''simple docstring'''
__a = min(2 , get_gpu_count() ) if distributed else 1
return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split() | 6 | 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
__snake_case = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__snake_case = 256047
__snake_case = 256145
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelCase : List[str] = NllbTokenizer
__UpperCAmelCase : Optional[int] = NllbTokenizerFast
__UpperCAmelCase : str = True
__UpperCAmelCase : Dict = True
__UpperCAmelCase : int = {}
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
snake_case : Optional[int] = NllbTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : Union[str, Any] = NllbTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
snake_case : List[str] = 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]] , )
snake_case : str = 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",
".",
] , )
snake_case : List[str] = 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]
] , )
snake_case : int = 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 ) -> str:
'''simple docstring'''
snake_case : str = (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})' ):
snake_case : Optional[Any] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
snake_case : Union[str, Any] = self.tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
snake_case : Tuple = tempfile.mkdtemp()
snake_case : Any = tokenizer_r.save_pretrained(UpperCamelCase__ )
snake_case : Union[str, Any] = 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 ) )
snake_case : List[str] = 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
snake_case : Dict = tokenizer_r.from_pretrained(UpperCamelCase__ )
snake_case : Union[str, Any] = 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
snake_case : int = tempfile.mkdtemp()
snake_case : List[str] = tokenizer_r.save_pretrained(UpperCamelCase__ , legacy_format=UpperCamelCase__ )
snake_case : Union[str, Any] = tokenizer_p.save_pretrained(UpperCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase__ , UpperCamelCase__ )
# Checks everything loads correctly in the same way
snake_case : str = tokenizer_r.from_pretrained(UpperCamelCase__ )
snake_case : Tuple = 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
snake_case : Optional[Any] = tempfile.mkdtemp()
snake_case : Optional[Any] = tokenizer_r.save_pretrained(UpperCamelCase__ , legacy_format=UpperCamelCase__ )
snake_case : Tuple = 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
snake_case : Optional[int] = tokenizer_r.from_pretrained(UpperCamelCase__ )
snake_case : Union[str, Any] = 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 ) -> List[Any]:
'''simple docstring'''
if not self.test_seqaseq:
return
snake_case : Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
snake_case : Any = [
" 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.",
]
snake_case : Optional[int] = [
"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:
snake_case : List[Any] = 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
snake_case : List[str] = 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 )
snake_case : List[Any] = 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 ) -> Dict:
'''simple docstring'''
pass
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
snake_case : List[str] = [AddedToken("<special>" , lstrip=UpperCamelCase__ )]
snake_case : Dict = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , **UpperCamelCase__ )
snake_case : Optional[Any] = tokenizer_r.encode("Hey this is a <special> token" )
snake_case : int = tokenizer_r.encode("<special>" , add_special_tokens=UpperCamelCase__ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
snake_case : int = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case : Optional[int] = self.tokenizer_class.from_pretrained(
UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , **UpperCamelCase__ )
snake_case : int = tokenizer_p.encode("Hey this is a <special> token" )
snake_case : str = 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 _lowerCAmelCase ( unittest.TestCase ):
__UpperCAmelCase : List[Any] = '''facebook/nllb-200-distilled-600M'''
__UpperCAmelCase : Dict = [
''' 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.''',
]
__UpperCAmelCase : int = [
'''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.''',
]
__UpperCAmelCase : Any = [
256047,
16297,
134408,
8165,
248066,
14734,
950,
1135,
105721,
3573,
83,
27352,
108,
49486,
2,
]
@classmethod
def lowerCamelCase ( cls ) -> Any:
'''simple docstring'''
snake_case : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
snake_case : Union[str, Any] = 1
return cls
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
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 ) -> Optional[Any]:
'''simple docstring'''
snake_case : Union[str, Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase__ )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
self.assertIn(UpperCamelCase__ , self.tokenizer.all_special_ids )
# fmt: off
snake_case : Any = [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
snake_case : Union[str, Any] = self.tokenizer.decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ )
snake_case : Any = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase__ )
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCamelCase__ )
snake_case : Any = 10
snake_case : Optional[int] = 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 ) -> Dict:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_6203, 3] )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Any = tempfile.mkdtemp()
snake_case : Optional[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase__ )
snake_case : Any = NllbTokenizer.from_pretrained(UpperCamelCase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase__ )
@require_torch
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
snake_case : List[str] = 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 )
snake_case : str = 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 ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = self.tokenizer(self.src_text , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=3 , return_tensors="pt" )
snake_case : int = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=10 , return_tensors="pt" )
snake_case : Optional[Any] = targets["input_ids"]
snake_case : Union[str, Any] = 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 ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[int] = 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 ) -> Any:
'''simple docstring'''
snake_case : Dict = True
snake_case : List[Any] = 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] )
snake_case : Optional[Any] = False
snake_case : Optional[int] = 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] )
| 351 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(lowercase : int , lowercase : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case : Dict = update_area_of_max_square(lowercase , col + 1 )
snake_case : Tuple = update_area_of_max_square(row + 1 , col + 1 )
snake_case : Any = update_area_of_max_square(row + 1 , lowercase )
if mat[row][col]:
snake_case : List[Any] = 1 + min([right, diagonal, down] )
snake_case : Any = max(largest_square_area[0] , lowercase )
return sub_problem_sol
else:
return 0
snake_case : int = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case : List[str] = update_area_of_max_square_using_dp_array(lowercase , col + 1 , lowercase )
snake_case : Optional[int] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , lowercase )
snake_case : Optional[int] = update_area_of_max_square_using_dp_array(row + 1 , lowercase , lowercase )
if mat[row][col]:
snake_case : Dict = 1 + min([right, diagonal, down] )
snake_case : Union[str, Any] = max(largest_square_area[0] , lowercase )
snake_case : str = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case : Union[str, Any] = [0]
snake_case : int = [[-1] * cols for _ in range(lowercase )]
update_area_of_max_square_using_dp_array(0 , 0 , lowercase )
return largest_square_area[0]
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
"""simple docstring"""
snake_case : int = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case : List[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
snake_case : Tuple = dp_array[row][col + 1]
snake_case : Any = dp_array[row + 1][col + 1]
snake_case : List[str] = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case : Optional[int] = 1 + min(lowercase , lowercase , lowercase )
snake_case : Tuple = max(dp_array[row][col] , lowercase )
else:
snake_case : List[Any] = 0
return largest_square_area
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
"""simple docstring"""
snake_case : Any = [0] * (cols + 1)
snake_case : Any = [0] * (cols + 1)
snake_case : Any = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
snake_case : Dict = current_row[col + 1]
snake_case : List[Any] = next_row[col + 1]
snake_case : Dict = next_row[col]
if mat[row][col] == 1:
snake_case : Union[str, Any] = 1 + min(lowercase , lowercase , lowercase )
snake_case : Optional[int] = max(current_row[col] , lowercase )
else:
snake_case : Optional[Any] = 0
snake_case : str = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 112 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase=1_3 , _UpperCAmelCase=7 , _UpperCAmelCase=False , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=1_9 , _UpperCAmelCase=3_2 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=3_7 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=5_1_2 , _UpperCAmelCase=1_6 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ) -> Tuple:
__UpperCamelCase : Tuple = parent
__UpperCamelCase : Tuple = batch_size
__UpperCamelCase : Optional[int] = seq_length
__UpperCamelCase : List[str] = is_training
__UpperCamelCase : Dict = use_input_mask
__UpperCamelCase : List[str] = use_token_type_ids
__UpperCamelCase : List[Any] = use_labels
__UpperCamelCase : Any = vocab_size
__UpperCamelCase : List[str] = hidden_size
__UpperCamelCase : int = num_hidden_layers
__UpperCamelCase : Union[str, Any] = num_attention_heads
__UpperCamelCase : Any = intermediate_size
__UpperCamelCase : Any = hidden_act
__UpperCamelCase : str = hidden_dropout_prob
__UpperCamelCase : List[str] = attention_probs_dropout_prob
__UpperCamelCase : Union[str, Any] = max_position_embeddings
__UpperCamelCase : Dict = type_vocab_size
__UpperCamelCase : Tuple = type_sequence_label_size
__UpperCamelCase : List[Any] = initializer_range
__UpperCamelCase : List[str] = num_labels
__UpperCamelCase : Tuple = num_choices
__UpperCamelCase : Tuple = scope
def a_ (self ) -> Optional[Any]:
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : str = None
if self.use_input_mask:
__UpperCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : Any = None
__UpperCamelCase : int = None
__UpperCamelCase : Union[str, Any] = None
if self.use_labels:
__UpperCamelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase : Tuple = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ (self ) -> List[Any]:
__UpperCamelCase : Dict = EsmConfig(
vocab_size=3_3 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=_UpperCAmelCase , esmfold_config={"trunk": {"num_blocks": 2}, "fp16_esm": False} , )
return config
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[Any]:
__UpperCamelCase : int = EsmForProteinFolding(config=_UpperCAmelCase ).float()
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : str = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
__UpperCamelCase : List[str] = model(_UpperCAmelCase )
__UpperCamelCase : int = model(_UpperCAmelCase )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 1_4, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : List[Any] = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : List[str] = config_and_inputs
__UpperCamelCase : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = False
A = (EsmForProteinFolding,) if is_torch_available() else ()
A = ()
A = {} if is_torch_available() else {}
A = False
def a_ (self ) -> str:
__UpperCamelCase : Dict = EsmFoldModelTester(self )
__UpperCamelCase : str = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=3_7 )
def a_ (self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def a_ (self ) -> List[Any]:
__UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
@unittest.skip("Does not support attention outputs" )
def a_ (self ) -> List[str]:
pass
@unittest.skip
def a_ (self ) -> Tuple:
pass
@unittest.skip("Esm does not support embedding resizing" )
def a_ (self ) -> int:
pass
@unittest.skip("Esm does not support embedding resizing" )
def a_ (self ) -> Dict:
pass
@unittest.skip("ESMFold does not support passing input embeds!" )
def a_ (self ) -> str:
pass
@unittest.skip("ESMFold does not support head pruning." )
def a_ (self ) -> int:
pass
@unittest.skip("ESMFold does not support head pruning." )
def a_ (self ) -> Optional[Any]:
pass
@unittest.skip("ESMFold does not support head pruning." )
def a_ (self ) -> Tuple:
pass
@unittest.skip("ESMFold does not support head pruning." )
def a_ (self ) -> Any:
pass
@unittest.skip("ESMFold does not support head pruning." )
def a_ (self ) -> List[str]:
pass
@unittest.skip("ESMFold does not output hidden states in the normal way." )
def a_ (self ) -> str:
pass
@unittest.skip("ESMfold does not output hidden states in the normal way." )
def a_ (self ) -> str:
pass
@unittest.skip("ESMFold only has one output format." )
def a_ (self ) -> List[Any]:
pass
@unittest.skip("This test doesn't work for ESMFold and doesn't test core functionality" )
def a_ (self ) -> Tuple:
pass
@unittest.skip("ESMFold does not support input chunking." )
def a_ (self ) -> int:
pass
@unittest.skip("ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments." )
def a_ (self ) -> Any:
pass
@unittest.skip("ESMFold doesn't support torchscript compilation." )
def a_ (self ) -> Dict:
pass
@unittest.skip("ESMFold doesn't support torchscript compilation." )
def a_ (self ) -> int:
pass
@unittest.skip("ESMFold doesn't support torchscript compilation." )
def a_ (self ) -> List[Any]:
pass
@unittest.skip("ESMFold doesn't support data parallel." )
def a_ (self ) -> int:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def a_ (self ) -> Optional[Any]:
pass
@require_torch
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@slow
def a_ (self ) -> Optional[int]:
__UpperCamelCase : Tuple = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1" ).float()
model.eval()
__UpperCamelCase : Dict = torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
__UpperCamelCase : Optional[int] = model(_UpperCAmelCase )["positions"]
__UpperCamelCase : Optional[Any] = torch.tensor([2.5_828, 0.7_993, -10.9_334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , _UpperCAmelCase , atol=1E-4 ) )
| 298 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCAmelCase = abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __lowerCAmelCase ( snake_case__ ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__ )
def __lowerCAmelCase ( snake_case__ ):
from transformers.testing_utils import pytest_terminal_summary_main
__UpperCamelCase : int = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__ )
| 298 | 1 |
'''simple docstring'''
from math import isqrt, loga
def __magic_name__ ( __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
snake_case_ = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, __UpperCAmelCase, __UpperCAmelCase ):
snake_case_ = False
return [i for i in range(2, __UpperCAmelCase ) if is_prime[i]]
def __magic_name__ ( __UpperCAmelCase = 80_0800, __UpperCAmelCase = 80_0800 ) -> int:
'''simple docstring'''
snake_case_ = degree * loga(__UpperCAmelCase )
snake_case_ = int(__UpperCAmelCase )
snake_case_ = calculate_prime_numbers(__UpperCAmelCase )
snake_case_ = 0
snake_case_ = 0
snake_case_ = len(__UpperCAmelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f'''{solution() = }''')
| 72 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, n + 1 ):
snake_case_ = max(
__UpperCAmelCase, prices[i - 1] + naive_cut_rod_recursive(n - i, __UpperCAmelCase ) )
return max_revue
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
snake_case_ = [float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
snake_case_ = float('''-inf''' )
for i in range(1, n + 1 ):
snake_case_ = max(
__UpperCAmelCase, prices[i - 1] + _top_down_cut_rod_recursive(n - i, __UpperCAmelCase, __UpperCAmelCase ), )
snake_case_ = max_revenue
return max_rev[n]
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
snake_case_ = [float('''-inf''' ) for _ in range(n + 1 )]
snake_case_ = 0
for i in range(1, n + 1 ):
snake_case_ = max_rev[i]
for j in range(1, i + 1 ):
snake_case_ = max(__UpperCAmelCase, prices[j - 1] + max_rev[i - j] )
snake_case_ = max_revenue_i
return max_rev[n]
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if n < 0:
snake_case_ = F"n must be greater than or equal to 0. Got n = {n}"
raise ValueError(__UpperCAmelCase )
if n > len(__UpperCAmelCase ):
snake_case_ = (
'''Each integral piece of rod must have a corresponding price. '''
F"Got n = {n} but length of prices = {len(__UpperCAmelCase )}"
)
raise ValueError(__UpperCAmelCase )
def __magic_name__ ( ) -> Optional[int]:
'''simple docstring'''
snake_case_ = [6, 10, 12, 15, 20, 23]
snake_case_ = len(__UpperCAmelCase )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
snake_case_ = 36
snake_case_ = top_down_cut_rod(__UpperCAmelCase, __UpperCAmelCase )
snake_case_ = bottom_up_cut_rod(__UpperCAmelCase, __UpperCAmelCase )
snake_case_ = naive_cut_rod_recursive(__UpperCAmelCase, __UpperCAmelCase )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 72 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__snake_case = logging.get_logger(__name__)
__snake_case = TypeVar("""DatasetType""", Dataset, IterableDataset)
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = "first_exhausted" , ) -> DatasetType:
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(UpperCamelCase_ )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_ ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_ ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , stopping_strategy=UpperCamelCase_ )
else:
return _interleave_iterable_datasets(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , stopping_strategy=UpperCamelCase_ )
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = 0 , ) -> DatasetType:
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(UpperCamelCase_ )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_ ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_ ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , axis=UpperCamelCase_ )
else:
return _concatenate_iterable_datasets(UpperCamelCase_ , info=UpperCamelCase_ , split=UpperCamelCase_ , axis=UpperCamelCase_ )
| 176 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase__ ( _UpperCAmelCase ):
A__ : Union[str, Any] ="""Wav2Vec2FeatureExtractor"""
A__ : Any ="""AutoTokenizer"""
def __init__( self : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.feature_extractor
SCREAMING_SNAKE_CASE__ = False
@classmethod
def A_ ( cls : Union[str, Any] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[Any] ):
try:
return super().from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
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: ' , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE__ = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
return cls(feature_extractor=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ )
def __call__( self : Any , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*UpperCAmelCase_ , **UpperCAmelCase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
SCREAMING_SNAKE_CASE__ = kwargs.pop('raw_speech' )
else:
SCREAMING_SNAKE_CASE__ = kwargs.pop('audio' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('sampling_rate' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('text' , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE__ = args[0]
SCREAMING_SNAKE_CASE__ = 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:
SCREAMING_SNAKE_CASE__ = self.feature_extractor(UpperCAmelCase_ , *UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None:
SCREAMING_SNAKE_CASE__ = self.tokenizer(UpperCAmelCase_ , **UpperCAmelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
SCREAMING_SNAKE_CASE__ = encodings['input_ids']
return inputs
def A_ ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('input_features' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('labels' , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE__ = args[0]
SCREAMING_SNAKE_CASE__ = args[1:]
if input_features is not None:
SCREAMING_SNAKE_CASE__ = self.feature_extractor.pad(UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is not None:
SCREAMING_SNAKE_CASE__ = self.tokenizer.pad(UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
SCREAMING_SNAKE_CASE__ = labels['input_ids']
return input_features
def A_ ( self : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[str] ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Tuple , *UpperCAmelCase_ : int , **UpperCAmelCase_ : List[str] ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@contextmanager
def A_ ( self : str ):
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.' )
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = self.tokenizer
yield
SCREAMING_SNAKE_CASE__ = self.feature_extractor
SCREAMING_SNAKE_CASE__ = False
| 176 | 1 |
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( UpperCAmelCase_ ):
"""simple docstring"""
a : torch.FloatTensor
class _lowerCAmelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
"""simple docstring"""
@register_to_config
def __init__( self , _lowerCamelCase = 3 , _lowerCamelCase = 3 , _lowerCamelCase = ("DownEncoderBlock2D",) , _lowerCamelCase = ("UpDecoderBlock2D",) , _lowerCamelCase = (6_4,) , _lowerCamelCase = 1 , _lowerCamelCase = "silu" , _lowerCamelCase = 3 , _lowerCamelCase = 3_2 , _lowerCamelCase = 2_5_6 , _lowerCamelCase = 3_2 , _lowerCamelCase = None , _lowerCamelCase = 0.1_8_2_1_5 , _lowerCamelCase = "group" , ):
super().__init__()
# pass init params to Encoder
UpperCamelCase_: int = 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 , )
UpperCamelCase_: Any = vq_embed_dim if vq_embed_dim is not None else latent_channels
UpperCamelCase_: List[str] = nn.Convad(_lowerCamelCase , _lowerCamelCase , 1 )
UpperCamelCase_: Union[str, Any] = VectorQuantizer(_lowerCamelCase , _lowerCamelCase , beta=0.2_5 , remap=_lowerCamelCase , sane_index_shape=_lowerCamelCase )
UpperCamelCase_: List[Any] = nn.Convad(_lowerCamelCase , _lowerCamelCase , 1 )
# pass init params to Decoder
UpperCamelCase_: str = 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 _a ( self , _lowerCamelCase , _lowerCamelCase = True ):
UpperCamelCase_: int = self.encoder(_lowerCamelCase )
UpperCamelCase_: Tuple = self.quant_conv(_lowerCamelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_lowerCamelCase )
@apply_forward_hook
def _a ( self , _lowerCamelCase , _lowerCamelCase = False , _lowerCamelCase = True ):
# also go through quantization layer
if not force_not_quantize:
UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_: Optional[int] = self.quantize(_lowerCamelCase )
else:
UpperCamelCase_: str = h
UpperCamelCase_: List[str] = self.post_quant_conv(_lowerCamelCase )
UpperCamelCase_: Tuple = self.decoder(_lowerCamelCase , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCamelCase )
def _a ( self , _lowerCamelCase , _lowerCamelCase = True ):
UpperCamelCase_: int = sample
UpperCamelCase_: int = self.encode(_lowerCamelCase ).latents
UpperCamelCase_: Dict = self.decode(_lowerCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCamelCase ) | 292 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case (UpperCAmelCase__ ) -> tuple:
return (data["data"], data["target"])
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> np.ndarray:
UpperCamelCase_: Dict = XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(UpperCAmelCase__ , UpperCAmelCase__ )
# Predict target for test data
UpperCamelCase_: int = xgb.predict(UpperCAmelCase__ )
UpperCamelCase_: Any = predictions.reshape(len(UpperCAmelCase__ ) , 1 )
return predictions
def snake_case () -> None:
UpperCamelCase_: Union[str, Any] = fetch_california_housing()
UpperCamelCase_ ,UpperCamelCase_: Tuple = data_handling(UpperCAmelCase__ )
UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_: Optional[Any] = train_test_split(
UpperCAmelCase__ , UpperCAmelCase__ , test_size=0.25 , random_state=1 )
UpperCamelCase_: Union[str, Any] = xgboost(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__ , UpperCAmelCase__ )}''' )
print(F'''Mean Square Error : {mean_squared_error(UpperCAmelCase__ , UpperCAmelCase__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main() | 292 | 1 |
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