code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : Any ): # noqa: E741
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = 0
_UpperCAmelCase = [0] * n
_UpperCAmelCase = [False] * n
_UpperCAmelCase = [False] * n
def dfs(_SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
if parent == root:
out_edge_count += 1
_UpperCAmelCase = True
_UpperCAmelCase = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
_UpperCAmelCase = dfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
_UpperCAmelCase = True
# AP found via cycle
if at == low[to]:
_UpperCAmelCase = True
else:
_UpperCAmelCase = min(low[at] , _SCREAMING_SNAKE_CASE )
return out_edge_count
for i in range(_SCREAMING_SNAKE_CASE ):
if not visited[i]:
_UpperCAmelCase = 0
_UpperCAmelCase = dfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , -1 , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = out_edge_count > 1
for x in range(len(_SCREAMING_SNAKE_CASE ) ):
if is_art[x] is True:
print(_SCREAMING_SNAKE_CASE )
# Adjacency list of graph
__A : Union[str, Any] = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ["PLBartTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
"PLBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"PLBartForCausalLM",
"PLBartForConditionalGeneration",
"PLBartForSequenceClassification",
"PLBartModel",
"PLBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__A : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 260 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = args.pruning_method
_UpperCAmelCase = args.threshold
_UpperCAmelCase = args.model_name_or_path.rstrip('''/''' )
_UpperCAmelCase = args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
_UpperCAmelCase = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
_UpperCAmelCase = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "bias" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
_UpperCAmelCase = MagnitudeBinarizer.apply(inputs=_SCREAMING_SNAKE_CASE , threshold=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = TopKBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase , _UpperCAmelCase = -0.1, 1.1
_UpperCAmelCase = torch.sigmoid(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = s * (r - l) + l
_UpperCAmelCase = s_bar.clamp(min=0.0 , max=1.0 )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
_UpperCAmelCase = os.path.join(
os.path.dirname(_SCREAMING_SNAKE_CASE ) , f'bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE )}' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
shutil.copytree(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f'\nCreated folder {target_model_path}' )
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__A : Optional[int] = parser.parse_args()
main(args)
| 260 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = LxmertTokenizer
UpperCamelCase__ = LxmertTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def lowercase__ ( self : List[Any] )->List[str]:
super().setUp()
_UpperCAmelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_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 lowercase__ ( self : Dict , __UpperCamelCase : List[Any] )->int:
_UpperCAmelCase = '''UNwant\u00E9d,running'''
_UpperCAmelCase = '''unwanted, running'''
return input_text, output_text
def lowercase__ ( self : Union[str, Any] )->int:
_UpperCAmelCase = self.tokenizer_class(self.vocab_file )
_UpperCAmelCase = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__UpperCamelCase , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , [7, 4, 5, 1_0, 8, 9] )
def lowercase__ ( self : Optional[Any] )->Union[str, Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = '''I was born in 92000, and this is falsé.'''
_UpperCAmelCase = tokenizer.tokenize(__UpperCamelCase )
_UpperCAmelCase = rust_tokenizer.tokenize(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
_UpperCAmelCase = rust_tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(__UpperCamelCase )
_UpperCAmelCase = rust_tokenizer.encode(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCAmelCase = arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE )]
# Reverse whole list
_UpperCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
__A : List[str] = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
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
__A : int = logging.getLogger(__name__)
# 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>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
UpperCamelCase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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()
# 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_audio_classification''' , _SCREAMING_SNAKE_CASE , _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()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_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}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.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 , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase = np.zeros_like(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
_UpperCAmelCase = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
_UpperCAmelCase = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
_UpperCAmelCase = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__A : str = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
__A : str = np.array(Image.open(lena_path))
# kernel to be applied
__A : List[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__A : Optional[Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__A : Optional[Any] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 260 | 1 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Any=False ):
'''simple docstring'''
_UpperCAmelCase = OmegaConf.load(_SCREAMING_SNAKE_CASE )
if display:
print(yaml.dump(OmegaConf.to_container(_SCREAMING_SNAKE_CASE ) ) )
return config
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[Any]=None , _SCREAMING_SNAKE_CASE : List[str]=None ):
'''simple docstring'''
if conf_path is None:
_UpperCAmelCase = '''./model_checkpoints/vqgan_only.yaml'''
_UpperCAmelCase = load_config(_SCREAMING_SNAKE_CASE , display=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = VQModel(**config.model.params )
if ckpt_path is None:
_UpperCAmelCase = '''./model_checkpoints/vqgan_only.pt'''
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location=_SCREAMING_SNAKE_CASE )
if ".ckpt" in ckpt_path:
_UpperCAmelCase = sd['''state_dict''']
model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
del sd
return model
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = model.encode(_SCREAMING_SNAKE_CASE )
print(f'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}' )
_UpperCAmelCase = model.decode(_SCREAMING_SNAKE_CASE )
return xrec
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=False ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = string.rsplit('''.''' , 1 )
if reload:
_UpperCAmelCase = importlib.import_module(_SCREAMING_SNAKE_CASE )
importlib.reload(_SCREAMING_SNAKE_CASE )
return getattr(importlib.import_module(_SCREAMING_SNAKE_CASE , package=_SCREAMING_SNAKE_CASE ) , cls )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
if "target" not in config:
raise KeyError('''Expected key `target` to instantiate.''' )
return get_obj_from_str(config['''target'''] )(**config.get('''params''' , {} ) )
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int=True , _SCREAMING_SNAKE_CASE : Optional[Any]=True ):
'''simple docstring'''
_UpperCAmelCase = instantiate_from_config(_SCREAMING_SNAKE_CASE )
if sd is not None:
model.load_state_dict(_SCREAMING_SNAKE_CASE )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
if ckpt:
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = pl_sd['''global_step''']
print(f'loaded model from global step {global_step}.' )
else:
_UpperCAmelCase = {'''state_dict''': None}
_UpperCAmelCase = None
_UpperCAmelCase = load_model_from_config(config.model , pl_sd['''state_dict'''] , gpu=_SCREAMING_SNAKE_CASE , eval_mode=_SCREAMING_SNAKE_CASE )['''model''']
return model, global_step
| 260 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """audio-spectrogram-transformer"""
def __init__( self : int , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : int=1_2 , __UpperCamelCase : List[Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Dict=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1e-12 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=1_0 , __UpperCamelCase : Optional[int]=1_0 , __UpperCamelCase : str=1_0_2_4 , __UpperCamelCase : Optional[Any]=1_2_8 , **__UpperCamelCase : Any , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = patch_size
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = frequency_stride
_UpperCAmelCase = time_stride
_UpperCAmelCase = max_length
_UpperCAmelCase = num_mel_bins
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCAmelCase = arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE )]
# Reverse whole list
_UpperCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
__A : List[str] = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 260 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
_UpperCAmelCase = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
if month > 12:
year += 1
_UpperCAmelCase = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
"InstructBlipVisionConfig",
],
"processing_instructblip": ["InstructBlipProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
"INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"InstructBlipQFormerModel",
"InstructBlipPreTrainedModel",
"InstructBlipForConditionalGeneration",
"InstructBlipVisionModel",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [n]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if len(str(_SCREAMING_SNAKE_CASE ) ) > 3:
if not is_prime(int(str(_SCREAMING_SNAKE_CASE )[-3:] ) ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE )[:3] ) ):
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(_SCREAMING_SNAKE_CASE ) != count:
if validate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_truncated_nums(_SCREAMING_SNAKE_CASE )
if all(is_prime(_SCREAMING_SNAKE_CASE ) for i in list_nums ):
list_truncated_primes.append(_SCREAMING_SNAKE_CASE )
num += 2
return list_truncated_primes
def lowercase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 260 | 1 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
random.seed(_SCREAMING_SNAKE_CASE )
np.random.seed(_SCREAMING_SNAKE_CASE )
torch.manual_seed(_SCREAMING_SNAKE_CASE )
torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE )
# ^^ safe to call this function even if cuda is not available
class _a :
"""simple docstring"""
def __init__( self : List[Any] , __UpperCamelCase : Iterable[torch.nn.Parameter] , __UpperCamelCase : float = 0.9_9_9_9 , __UpperCamelCase : float = 0.0 , __UpperCamelCase : int = 0 , __UpperCamelCase : bool = False , __UpperCamelCase : Union[float, int] = 1.0 , __UpperCamelCase : Union[float, int] = 2 / 3 , __UpperCamelCase : Optional[Any] = None , __UpperCamelCase : Dict[str, Any] = None , **__UpperCamelCase : Any , )->List[Any]:
if isinstance(__UpperCamelCase , torch.nn.Module ):
_UpperCAmelCase = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage`''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase , )
_UpperCAmelCase = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
_UpperCAmelCase = True
if kwargs.get('''max_value''' , __UpperCamelCase ) is not None:
_UpperCAmelCase = '''The `max_value` argument is deprecated. Please use `decay` instead.'''
deprecate('''max_value''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase )
_UpperCAmelCase = kwargs['''max_value''']
if kwargs.get('''min_value''' , __UpperCamelCase ) is not None:
_UpperCAmelCase = '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
deprecate('''min_value''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase )
_UpperCAmelCase = kwargs['''min_value''']
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = [p.clone().detach() for p in parameters]
if kwargs.get('''device''' , __UpperCamelCase ) is not None:
_UpperCAmelCase = '''The `device` argument is deprecated. Please use `to` instead.'''
deprecate('''device''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase )
self.to(device=kwargs['''device'''] )
_UpperCAmelCase = None
_UpperCAmelCase = decay
_UpperCAmelCase = min_decay
_UpperCAmelCase = update_after_step
_UpperCAmelCase = use_ema_warmup
_UpperCAmelCase = inv_gamma
_UpperCAmelCase = power
_UpperCAmelCase = 0
_UpperCAmelCase = None # set in `step()`
_UpperCAmelCase = model_cls
_UpperCAmelCase = model_config
@classmethod
def lowercase__ ( cls : int , __UpperCamelCase : Dict , __UpperCamelCase : int )->"EMAModel":
_UpperCAmelCase , _UpperCAmelCase = model_cls.load_config(__UpperCamelCase , return_unused_kwargs=__UpperCamelCase )
_UpperCAmelCase = model_cls.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = cls(model.parameters() , model_cls=__UpperCamelCase , model_config=model.config )
ema_model.load_state_dict(__UpperCamelCase )
return ema_model
def lowercase__ ( self : Any , __UpperCamelCase : Tuple )->List[str]:
if self.model_cls is None:
raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )
if self.model_config is None:
raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )
_UpperCAmelCase = self.model_cls.from_config(self.model_config )
_UpperCAmelCase = self.state_dict()
state_dict.pop('''shadow_params''' , __UpperCamelCase )
model.register_to_config(**__UpperCamelCase )
self.copy_to(model.parameters() )
model.save_pretrained(__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : int )->float:
_UpperCAmelCase = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
_UpperCAmelCase = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
_UpperCAmelCase = (1 + step) / (1_0 + step)
_UpperCAmelCase = min(__UpperCamelCase , self.decay )
# make sure decay is not smaller than min_decay
_UpperCAmelCase = max(__UpperCamelCase , self.min_decay )
return cur_decay_value
@torch.no_grad()
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Iterable[torch.nn.Parameter] )->int:
if isinstance(__UpperCamelCase , torch.nn.Module ):
_UpperCAmelCase = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase , )
_UpperCAmelCase = parameters.parameters()
_UpperCAmelCase = list(__UpperCamelCase )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
_UpperCAmelCase = self.get_decay(self.optimization_step )
_UpperCAmelCase = decay
_UpperCAmelCase = 1 - decay
_UpperCAmelCase = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , __UpperCamelCase ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
_UpperCAmelCase = deepspeed.zero.GatheredParameters(__UpperCamelCase , modifier_rank=__UpperCamelCase )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(__UpperCamelCase )
def lowercase__ ( self : Any , __UpperCamelCase : Iterable[torch.nn.Parameter] )->None:
_UpperCAmelCase = list(__UpperCamelCase )
for s_param, param in zip(self.shadow_params , __UpperCamelCase ):
param.data.copy_(s_param.to(param.device ).data )
def lowercase__ ( self : Dict , __UpperCamelCase : Any=None , __UpperCamelCase : Tuple=None )->None:
_UpperCAmelCase = [
p.to(device=__UpperCamelCase , dtype=__UpperCamelCase ) if p.is_floating_point() else p.to(device=__UpperCamelCase )
for p in self.shadow_params
]
def lowercase__ ( self : str )->dict:
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Iterable[torch.nn.Parameter] )->None:
_UpperCAmelCase = [param.detach().cpu().clone() for param in parameters]
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Iterable[torch.nn.Parameter] )->None:
if self.temp_stored_params is None:
raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
for c_param, param in zip(self.temp_stored_params , __UpperCamelCase ):
param.data.copy_(c_param.data )
# Better memory-wise.
_UpperCAmelCase = None
def lowercase__ ( self : Optional[int] , __UpperCamelCase : dict )->None:
_UpperCAmelCase = copy.deepcopy(__UpperCamelCase )
_UpperCAmelCase = state_dict.get('''decay''' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('''Decay must be between 0 and 1''' )
_UpperCAmelCase = state_dict.get('''min_decay''' , self.min_decay )
if not isinstance(self.min_decay , __UpperCamelCase ):
raise ValueError('''Invalid min_decay''' )
_UpperCAmelCase = state_dict.get('''optimization_step''' , self.optimization_step )
if not isinstance(self.optimization_step , __UpperCamelCase ):
raise ValueError('''Invalid optimization_step''' )
_UpperCAmelCase = state_dict.get('''update_after_step''' , self.update_after_step )
if not isinstance(self.update_after_step , __UpperCamelCase ):
raise ValueError('''Invalid update_after_step''' )
_UpperCAmelCase = state_dict.get('''use_ema_warmup''' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , __UpperCamelCase ):
raise ValueError('''Invalid use_ema_warmup''' )
_UpperCAmelCase = state_dict.get('''inv_gamma''' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('''Invalid inv_gamma''' )
_UpperCAmelCase = state_dict.get('''power''' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('''Invalid power''' )
_UpperCAmelCase = state_dict.get('''shadow_params''' , __UpperCamelCase )
if shadow_params is not None:
_UpperCAmelCase = shadow_params
if not isinstance(self.shadow_params , __UpperCamelCase ):
raise ValueError('''shadow_params must be a list''' )
if not all(isinstance(__UpperCamelCase , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('''shadow_params must all be Tensors''' )
| 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : str = sys.version_info >= (3, 10)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : Tuple )->Optional[int]:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : List[str] )->List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def lowercase__ ( self : int )->str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : int , __UpperCamelCase : argparse.ArgumentParser , __UpperCamelCase : argparse.ArgumentParser )->Dict:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , __UpperCamelCase ) and yy.get('''choices''' , __UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](__UpperCamelCase ) , yy['''type'''](__UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--bar''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--baz''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--flag''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(__UpperCamelCase , look_for_args_file=__UpperCamelCase )
self.assertFalse(example.flag )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=4_2 , type=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=__UpperCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 4_2] , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : List[str] )->List[str]:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 4_2) , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
def lowercase__ ( self : int )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=__UpperCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--bar''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , bar=__UpperCamelCase , baz=__UpperCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo=1_2 , bar=3.1_4 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--required_str''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
_UpperCAmelCase = parser.parse_dict(__UpperCamelCase )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 4_2,
}
self.assertRaises(__UpperCamelCase , parser.parse_dict , __UpperCamelCase , allow_extra_keys=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_json''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_yaml''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 260 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = RemBertConfig.from_json_file(_SCREAMING_SNAKE_CASE )
print('''Building PyTorch model from configuration: {}'''.format(str(_SCREAMING_SNAKE_CASE ) ) )
_UpperCAmelCase = RemBertModel(_SCREAMING_SNAKE_CASE )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Save pytorch-model
print('''Save PyTorch model to {}'''.format(_SCREAMING_SNAKE_CASE ) )
torch.save(model.state_dict() , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Optional[int] = 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(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__A : Union[str, Any] = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase = True
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_UpperCAmelCase = True
if a[i].islower():
_UpperCAmelCase = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__A : Any = logging.get_logger(__name__)
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Union[str, Any] , *__UpperCamelCase : Optional[Any] , **__UpperCamelCase : Optional[int] )->None:
warnings.warn(
'''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use BeitImageProcessor instead.''' , __UpperCamelCase , )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
| 260 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
quick_sort_random(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_SCREAMING_SNAKE_CASE , pivot_index + 1 , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCAmelCase = [int(_SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Any = {
"configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"],
"processing_mgp_str": ["MgpstrProcessor"],
"tokenization_mgp_str": ["MgpstrTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
"MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST",
"MgpstrModel",
"MgpstrPreTrainedModel",
"MgpstrForSceneTextRecognition",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
__A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"
__A : List[str] = "\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to 'cuda' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n >>> results = perplexity.compute(model_id='gpt2',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 78.22\n >>> print(round(results[\"perplexities\"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = datasets.load_dataset(\"wikitext\",\n ... \"wikitext-2-raw-v1\",\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!='']\n >>> results = perplexity.compute(model_id='gpt2',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 60.35\n >>> print(round(results[\"perplexities\"][0], 2))\n 81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int = 1_6 , __UpperCamelCase : bool = True , __UpperCamelCase : List[Any]=None )->Any:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = '''cuda'''
else:
_UpperCAmelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors='''pt''' , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_UpperCAmelCase = encodings['''input_ids''']
_UpperCAmelCase = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 260 | 1 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
__A : Union[str, Any] = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
__A : Optional[int] = 10
__A : Dict = 256
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) < MIN_NUM_TOKENS:
return None
_UpperCAmelCase = MinHash(num_perm=_SCREAMING_SNAKE_CASE )
for token in set(_SCREAMING_SNAKE_CASE ):
min_hash.update(token.encode() )
return min_hash
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return {t for t in NON_ALPHA.split(_SCREAMING_SNAKE_CASE ) if len(t.strip() ) > 0}
class _a :
"""simple docstring"""
def __init__( self : Tuple , *,
__UpperCamelCase : float = 0.8_5 , )->List[str]:
_UpperCAmelCase = duplication_jaccard_threshold
_UpperCAmelCase = NUM_PERM
_UpperCAmelCase = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_UpperCAmelCase = defaultdict(__UpperCamelCase )
def lowercase__ ( self : Any , __UpperCamelCase : Tuple , __UpperCamelCase : MinHash )->None:
_UpperCAmelCase = self._index.query(__UpperCamelCase )
if code_key in self._index.keys:
print(F'Duplicate key {code_key}' )
return
self._index.insert(__UpperCamelCase , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCamelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCamelCase )
def lowercase__ ( self : str )->List[List[Dict]]:
_UpperCAmelCase = []
for base, duplicates in self._duplicate_clusters.items():
_UpperCAmelCase = [base] + list(__UpperCamelCase )
# reformat the cluster to be a list of dict
_UpperCAmelCase = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__UpperCamelCase )
return duplicate_clusters
def lowercase__ ( self : Tuple , __UpperCamelCase : Union[str, Any] )->None:
_UpperCAmelCase = self.get_duplicate_clusters()
with open(__UpperCamelCase , '''w''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = element
_UpperCAmelCase = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowercase ( _SCREAMING_SNAKE_CASE : Type[Dataset] ):
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(_SCREAMING_SNAKE_CASE , max_queue_size=1_0000 ) , chunksize=100 , ):
if data is not None:
yield data
def lowercase ( _SCREAMING_SNAKE_CASE : Type[Dataset] , _SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''
_UpperCAmelCase = DuplicationIndex(duplication_jaccard_threshold=_SCREAMING_SNAKE_CASE )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_SCREAMING_SNAKE_CASE ) ) , max_queue_size=100 ) ):
di.add(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = get_tokens(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = get_tokens(_SCREAMING_SNAKE_CASE )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
__A : Tuple = None
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = []
for elementa in cluster:
_UpperCAmelCase = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_UpperCAmelCase = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_UpperCAmelCase = 1
extremes.append(_SCREAMING_SNAKE_CASE )
return extremes
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
global _shared_dataset
_UpperCAmelCase = dataset
_UpperCAmelCase = []
_UpperCAmelCase = partial(_find_cluster_extremes_shared , jaccard_threshold=_SCREAMING_SNAKE_CASE )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) , total=len(_SCREAMING_SNAKE_CASE ) , ):
extremes_list.append(_SCREAMING_SNAKE_CASE )
return extremes_list
def lowercase ( _SCREAMING_SNAKE_CASE : Type[Dataset] , _SCREAMING_SNAKE_CASE : float = 0.85 ):
'''simple docstring'''
_UpperCAmelCase = make_duplicate_clusters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_UpperCAmelCase = {}
_UpperCAmelCase = find_extremes(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for extremes in extremes_clusters:
for element in extremes:
_UpperCAmelCase = element
_UpperCAmelCase = duplicate_indices - set(extreme_dict.keys() )
_UpperCAmelCase = dataset.filter(lambda _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : idx not in remove_indices , with_indices=_SCREAMING_SNAKE_CASE )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_UpperCAmelCase = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_UpperCAmelCase = extreme_dict[element['''base_index''']]['''copies''']
print(f'Original dataset size: {len(_SCREAMING_SNAKE_CASE )}' )
print(f'Number of duplicate clusters: {len(_SCREAMING_SNAKE_CASE )}' )
print(f'Files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE )}' )
print(f'Unique files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE )}' )
print(f'Filtered dataset size: {len(_SCREAMING_SNAKE_CASE )}' )
return ds_filter, duplicate_clusters
| 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.getbasetemp() / '''cache'''
_UpperCAmelCase = test_hf_cache_home / '''datasets'''
_UpperCAmelCase = test_hf_cache_home / '''metrics'''
_UpperCAmelCase = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE , scope='''session''' )
def lowercase ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _SCREAMING_SNAKE_CASE )
| 260 | 1 |
"""simple docstring"""
__A : Any = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__A : str = ["a", "b", "c", "d", "e"]
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
_UpperCAmelCase = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
_UpperCAmelCase = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
_UpperCAmelCase = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__A : Union[str, Any] = topological_sort("a", [], [])
print(sort)
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase , _UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__A : Dict = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
"GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GitForCausalLM",
"GitModel",
"GitPreTrainedModel",
"GitVisionModel",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
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
__A : int = logging.getLogger(__name__)
# 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>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
UpperCamelCase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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()
# 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_audio_classification''' , _SCREAMING_SNAKE_CASE , _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()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_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}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.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 , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A : Union[str, Any] = logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""")
@require_torch
@require_tf
@slow
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : Path , __UpperCamelCase : Union[str, None] = None , __UpperCamelCase : Union[List[str], None] = None , __UpperCamelCase : Union[str, List[str], None] = None , __UpperCamelCase : bool = True , )->Any:
_UpperCAmelCase = [file for file in os.listdir(__UpperCamelCase ) if os.path.isfile(os.path.join(__UpperCamelCase , __UpperCamelCase ) )]
if identifier is not None:
_UpperCAmelCase = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
for n_ in n_identifier:
_UpperCAmelCase = [file for file in files if n_ not in file]
else:
_UpperCAmelCase = [file for file in files if n_identifier not in file]
_UpperCAmelCase = ignore_files or []
ignore_files.append('''__init__.py''' )
_UpperCAmelCase = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print('''Testing''' , __UpperCamelCase )
if only_modules:
_UpperCAmelCase = file.split('''.''' )[0]
try:
_UpperCAmelCase = getattr(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = doctest.DocTestSuite(__UpperCamelCase )
_UpperCAmelCase = unittest.TextTestRunner().run(__UpperCamelCase )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(F'{module_identifier} is not a module.' )
else:
_UpperCAmelCase = doctest.testfile(str('''..''' / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def lowercase__ ( self : Any )->Dict:
_UpperCAmelCase = Path('''src/transformers''' )
_UpperCAmelCase = '''modeling'''
_UpperCAmelCase = [
'''modeling_ctrl.py''',
'''modeling_tf_ctrl.py''',
]
self.analyze_directory(__UpperCamelCase , identifier=__UpperCamelCase , ignore_files=__UpperCamelCase )
def lowercase__ ( self : Dict )->Any:
_UpperCAmelCase = Path('''src/transformers''' )
_UpperCAmelCase = '''tokenization'''
self.analyze_directory(__UpperCamelCase , identifier=__UpperCamelCase )
def lowercase__ ( self : Tuple )->Optional[Any]:
_UpperCAmelCase = Path('''src/transformers''' )
_UpperCAmelCase = '''configuration'''
self.analyze_directory(__UpperCamelCase , identifier=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->int:
_UpperCAmelCase = Path('''src/transformers''' )
_UpperCAmelCase = ['''configuration''', '''modeling''', '''tokenization''']
self.analyze_directory(__UpperCamelCase , n_identifier=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->str:
_UpperCAmelCase = Path('''docs/source''' )
_UpperCAmelCase = ['''favicon.ico''']
self.analyze_directory(__UpperCamelCase , ignore_files=__UpperCamelCase , only_modules=__UpperCamelCase )
| 260 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DPMSolverSinglestepScheduler,)
UpperCamelCase__ = (("""num_inference_steps""", 25),)
def lowercase__ ( self : Tuple , **__UpperCamelCase : Tuple )->Any:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf''' ),
'''variance_type''': None,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : Dict , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Any )->Union[str, Any]:
pass
def lowercase__ ( self : str , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : int , __UpperCamelCase : List[str]=None , **__UpperCamelCase : Optional[int] )->List[Any]:
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def lowercase__ ( self : List[Any] )->Dict:
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = 5_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_5_7_4 ) < 1e-3
def lowercase__ ( self : Dict )->Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : str )->Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->int:
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , algorithm_type='''dpmsolver++''' , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def lowercase__ ( self : str )->str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict )->List[str]:
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def lowercase__ ( self : Dict )->str:
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowercase__ ( self : List[str] )->int:
self.check_over_configs(variance_type=__UpperCamelCase )
self.check_over_configs(variance_type='''learned_range''' )
def lowercase__ ( self : List[str] )->Union[str, Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : List[str] )->List[str]:
_UpperCAmelCase = self.full_loop(use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_2_4_8 ) < 1e-3
def lowercase__ ( self : int )->List[Any]:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_4_5_3 ) < 1e-3
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_6_4_9 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 260 | 1 |
"""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
__A : Tuple = [
{"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 lowercase ( _SCREAMING_SNAKE_CASE : str=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=lowerCAmelCase))
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = None
def lowercase__ ( self : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str] )->List[Any]:
with TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = dataset_module_factory(__UpperCamelCase , cache_dir=__UpperCamelCase )
_UpperCAmelCase = import_main_class(dataset_module.module_path , dataset=__UpperCamelCase )
_UpperCAmelCase = builder_cls(
cache_dir=__UpperCamelCase , config_name=__UpperCamelCase , hash=dataset_module.hash , )
_UpperCAmelCase = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=__UpperCamelCase ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
_UpperCAmelCase = cached_path(__UpperCamelCase , cache_dir=__UpperCamelCase )
self.assertTrue(os.path.exists(__UpperCamelCase ) )
@pytest.mark.integration
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
_UpperCAmelCase = dataset_module_factory('''wikipedia''' , cache_dir=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = import_main_class(dataset_module.module_path )
_UpperCAmelCase = builder_cls(
cache_dir=_SCREAMING_SNAKE_CASE , 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 lowercase ( _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = dataset_module_factory('''wikipedia''' , 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='''20220301.frr''' , hash=dataset_module.hash , )
_UpperCAmelCase = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert "train" in ds
assert isinstance(ds['''train'''] , _SCREAMING_SNAKE_CASE )
assert next(iter(ds['''train'''] ) )
| 260 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : float )->float:
return 0.0
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_UpperCAmelCase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.abs(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = 20 * np.logaa(_SCREAMING_SNAKE_CASE )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
_UpperCAmelCase = get_bounds(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(_SCREAMING_SNAKE_CASE )
plt.show()
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.angle(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(_SCREAMING_SNAKE_CASE , -2 * pi ) )
plt.show()
| 260 | 1 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
__A : Tuple = open # noqa: we just need to have a builtin inside this module to test it properly
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """camembert"""
def __init__( self : List[str] , __UpperCamelCase : Union[str, Any]=3_0_5_2_2 , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : Optional[int]=1_2 , __UpperCamelCase : Union[str, Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Dict="gelu" , __UpperCamelCase : Tuple=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : int=5_1_2 , __UpperCamelCase : Dict=2 , __UpperCamelCase : int=0.0_2 , __UpperCamelCase : int=1e-12 , __UpperCamelCase : Optional[Any]=1 , __UpperCamelCase : Dict=0 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : Any="absolute" , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : str=None , **__UpperCamelCase : Optional[Any] , )->str:
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class _a ( lowerCAmelCase):
"""simple docstring"""
@property
def lowercase__ ( self : int )->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 50 ):
'''simple docstring'''
_UpperCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """poolformer"""
def __init__( self : List[str] , __UpperCamelCase : int=3 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : str=1_6 , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : int=4.0 , __UpperCamelCase : str=[2, 2, 6, 2] , __UpperCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __UpperCamelCase : int=[7, 3, 3, 3] , __UpperCamelCase : str=[4, 2, 2, 2] , __UpperCamelCase : Union[str, Any]=[2, 1, 1, 1] , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=0.0 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : List[str]=True , __UpperCamelCase : Union[str, Any]=1e-5 , __UpperCamelCase : str=0.0_2 , **__UpperCamelCase : List[Any] , )->Dict:
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = stride
_UpperCAmelCase = padding
_UpperCAmelCase = pool_size
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = depths
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_layer_scale
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = initializer_range
super().__init__(**__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = version.parse("""1.11""")
@property
def lowercase__ ( self : Union[str, Any] )->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase__ ( self : Tuple )->float:
return 2e-3
| 260 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : float )->float:
return 0.0
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_UpperCAmelCase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.abs(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = 20 * np.logaa(_SCREAMING_SNAKE_CASE )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
_UpperCAmelCase = get_bounds(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(_SCREAMING_SNAKE_CASE )
plt.show()
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.angle(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(_SCREAMING_SNAKE_CASE , -2 * pi ) )
plt.show()
| 260 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Union[str, Any] = 16
__A : Optional[Any] = 32
def lowercase ( _SCREAMING_SNAKE_CASE : Accelerator , _SCREAMING_SNAKE_CASE : int = 16 ):
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_UpperCAmelCase = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_SCREAMING_SNAKE_CASE : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase = datasets.map(
_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_SCREAMING_SNAKE_CASE : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase = 8
else:
_UpperCAmelCase = None
return tokenizer.pad(
_SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : Optional[int] = mocked_dataloaders # noqa: F811
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _SCREAMING_SNAKE_CASE ) == "1":
_UpperCAmelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
_UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config['''lr''']
_UpperCAmelCase = int(config['''num_epochs'''] )
_UpperCAmelCase = int(config['''seed'''] )
_UpperCAmelCase = int(config['''batch_size'''] )
set_seed(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = get_dataloaders(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = AdamW(params=model.parameters() , lr=_SCREAMING_SNAKE_CASE )
# Instantiate scheduler
_UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=_SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(_SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_UpperCAmelCase = os.path.split(_SCREAMING_SNAKE_CASE )[-1].split('''.''' )[0]
accelerator.init_trackers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(_SCREAMING_SNAKE_CASE ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_UpperCAmelCase = 0
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(_SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_SCREAMING_SNAKE_CASE , references=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , _SCREAMING_SNAKE_CASE )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(_SCREAMING_SNAKE_CASE ),
'''epoch''': epoch,
} , step=_SCREAMING_SNAKE_CASE , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=_SCREAMING_SNAKE_CASE , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' , type=_SCREAMING_SNAKE_CASE , default=1 , help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' , type=_SCREAMING_SNAKE_CASE , help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) , )
# rest from the training program
parser.add_argument('''training_script_args''' , nargs=_SCREAMING_SNAKE_CASE )
return parser.parse_args()
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = parse_args()
# Import training_script as a module.
_UpperCAmelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_UpperCAmelCase = script_fpath.stem
_UpperCAmelCase = importlib.import_module(_SCREAMING_SNAKE_CASE )
# Patch sys.argv
_UpperCAmelCase = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCAmelCase = 0
count += depth_first_search(_SCREAMING_SNAKE_CASE , row + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , row - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col + 1 , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col - 1 , _SCREAMING_SNAKE_CASE )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
_UpperCAmelCase = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
_UpperCAmelCase = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
_UpperCAmelCase = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
_UpperCAmelCase = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
_UpperCAmelCase = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
_UpperCAmelCase = key.replace('''image_encoder.module''' , '''flava.image_model''' )
_UpperCAmelCase = key.replace('''text_encoder.module''' , '''flava.text_model''' )
_UpperCAmelCase = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
_UpperCAmelCase = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
_UpperCAmelCase = key.replace('''text_projection''' , '''flava.text_projection''' )
_UpperCAmelCase = key.replace('''image_projection''' , '''flava.image_projection''' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 260 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : Union[str, Any] = {}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """llama"""
UpperCamelCase__ = ["""past_key_values"""]
def __init__( self : Dict , __UpperCamelCase : int=3_2_0_0_0 , __UpperCamelCase : List[str]=4_0_9_6 , __UpperCamelCase : Optional[int]=1_1_0_0_8 , __UpperCamelCase : Optional[Any]=3_2 , __UpperCamelCase : Tuple=3_2 , __UpperCamelCase : List[str]=None , __UpperCamelCase : Dict="silu" , __UpperCamelCase : Tuple=2_0_4_8 , __UpperCamelCase : List[str]=0.0_2 , __UpperCamelCase : int=1e-6 , __UpperCamelCase : Any=True , __UpperCamelCase : Union[str, Any]=0 , __UpperCamelCase : int=1 , __UpperCamelCase : List[str]=2 , __UpperCamelCase : Optional[int]=1 , __UpperCamelCase : List[Any]=False , __UpperCamelCase : Any=None , **__UpperCamelCase : Optional[int] , )->List[Any]:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = num_key_value_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = rms_norm_eps
_UpperCAmelCase = pretraining_tp
_UpperCAmelCase = use_cache
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , tie_word_embeddings=__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : Tuple )->Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __UpperCamelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
F'got {self.rope_scaling}' )
_UpperCAmelCase = self.rope_scaling.get('''type''' , __UpperCamelCase )
_UpperCAmelCase = self.rope_scaling.get('''factor''' , __UpperCamelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(__UpperCamelCase , __UpperCamelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 260 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowercase ( _SCREAMING_SNAKE_CASE : Features ):
'''simple docstring'''
_UpperCAmelCase = np.inf
def set_batch_size(_SCREAMING_SNAKE_CASE : FeatureType ) -> None:
nonlocal batch_size
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and feature.dtype == "binary":
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return None if batch_size is np.inf else batch_size
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCamelCase : NestedDataStructureLike[PathLike] , __UpperCamelCase : Optional[NamedSplit] = None , __UpperCamelCase : Optional[Features] = None , __UpperCamelCase : str = None , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : int , )->Union[str, Any]:
super().__init__(
__UpperCamelCase , split=__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase , streaming=__UpperCamelCase , num_proc=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = path_or_paths if isinstance(__UpperCamelCase , __UpperCamelCase ) else {self.split: path_or_paths}
_UpperCAmelCase = _PACKAGED_DATASETS_MODULES['''parquet'''][1]
_UpperCAmelCase = Parquet(
cache_dir=__UpperCamelCase , data_files=__UpperCamelCase , features=__UpperCamelCase , hash=__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : Union[str, Any] )->Dict:
# Build iterable dataset
if self.streaming:
_UpperCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=__UpperCamelCase , download_mode=__UpperCamelCase , verification_mode=__UpperCamelCase , base_path=__UpperCamelCase , num_proc=self.num_proc , )
_UpperCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=__UpperCamelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , __UpperCamelCase : Dataset , __UpperCamelCase : Union[PathLike, BinaryIO] , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : Tuple , )->Optional[int]:
_UpperCAmelCase = dataset
_UpperCAmelCase = path_or_buf
_UpperCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_UpperCAmelCase = parquet_writer_kwargs
def lowercase__ ( self : Optional[int] )->int:
_UpperCAmelCase = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , '''wb+''' ) as buffer:
_UpperCAmelCase = self._write(file_obj=__UpperCamelCase , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
else:
_UpperCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
return written
def lowercase__ ( self : int , __UpperCamelCase : BinaryIO , __UpperCamelCase : int , **__UpperCamelCase : int )->int:
_UpperCAmelCase = 0
_UpperCAmelCase = parquet_writer_kwargs.pop('''path_or_buf''' , __UpperCamelCase )
_UpperCAmelCase = self.dataset.features.arrow_schema
_UpperCAmelCase = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase , **__UpperCamelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , __UpperCamelCase ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating parquet from Arrow format''' , ):
_UpperCAmelCase = query_table(
table=self.dataset._data , key=slice(__UpperCamelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(__UpperCamelCase )
written += batch.nbytes
writer.close()
return written
| 260 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DDPMScheduler,)
def lowercase__ ( self : Any , **__UpperCamelCase : Tuple )->Dict:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : List[Any] )->Union[str, Any]:
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : Tuple )->List[Any]:
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=__UpperCamelCase , beta_end=__UpperCamelCase )
def lowercase__ ( self : Dict )->int:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__UpperCamelCase )
def lowercase__ ( self : Tuple )->List[Any]:
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Optional[int]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__UpperCamelCase )
def lowercase__ ( self : Tuple )->Optional[Any]:
self.check_over_configs(thresholding=__UpperCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , )
def lowercase__ ( self : Optional[int] )->List[str]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : int )->int:
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=__UpperCamelCase )
def lowercase__ ( self : List[str] )->Tuple:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.0_2 ) ) < 1e-5
def lowercase__ ( self : int )->Dict:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
_UpperCAmelCase = torch.manual_seed(0 )
for t in reversed(range(__UpperCamelCase ) ):
# 1. predict noise residual
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase = pred_prev_sample
_UpperCAmelCase = torch.sum(torch.abs(__UpperCamelCase ) )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def lowercase__ ( self : List[Any] )->List[Any]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(prediction_type='''v_prediction''' )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
_UpperCAmelCase = torch.manual_seed(0 )
for t in reversed(range(__UpperCamelCase ) ):
# 1. predict noise residual
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase = pred_prev_sample
_UpperCAmelCase = torch.sum(torch.abs(__UpperCamelCase ) )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def lowercase__ ( self : Dict )->Tuple:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=__UpperCamelCase )
_UpperCAmelCase = scheduler.timesteps
for i, timestep in enumerate(__UpperCamelCase ):
if i == len(__UpperCamelCase ) - 1:
_UpperCAmelCase = -1
else:
_UpperCAmelCase = timesteps[i + 1]
_UpperCAmelCase = scheduler.previous_timestep(__UpperCamelCase )
_UpperCAmelCase = prev_t.item()
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[int] )->Optional[Any]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(__UpperCamelCase , msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__UpperCamelCase )
def lowercase__ ( self : Optional[int] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = [1_0_0, 8_7, 5_0, 1, 0]
_UpperCAmelCase = len(__UpperCamelCase )
with self.assertRaises(__UpperCamelCase , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__UpperCamelCase , timesteps=__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->int:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__UpperCamelCase , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__UpperCamelCase )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase ( _SCREAMING_SNAKE_CASE : Sequence[int] | None = None ):
'''simple docstring'''
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
_UpperCAmelCase = nums[0]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
_UpperCAmelCase = nums[i]
_UpperCAmelCase = max(_SCREAMING_SNAKE_CASE , ans + num , _SCREAMING_SNAKE_CASE )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
__A : List[Any] = int(input("Enter number of elements : ").strip())
__A : List[Any] = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 260 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = args.pruning_method
_UpperCAmelCase = args.threshold
_UpperCAmelCase = args.model_name_or_path.rstrip('''/''' )
_UpperCAmelCase = args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
_UpperCAmelCase = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
_UpperCAmelCase = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "bias" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
_UpperCAmelCase = MagnitudeBinarizer.apply(inputs=_SCREAMING_SNAKE_CASE , threshold=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = TopKBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase , _UpperCAmelCase = -0.1, 1.1
_UpperCAmelCase = torch.sigmoid(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = s * (r - l) + l
_UpperCAmelCase = s_bar.clamp(min=0.0 , max=1.0 )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
_UpperCAmelCase = os.path.join(
os.path.dirname(_SCREAMING_SNAKE_CASE ) , f'bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE )}' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
shutil.copytree(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f'\nCreated folder {target_model_path}' )
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__A : Optional[int] = parser.parse_args()
main(args)
| 260 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class _a :
"""simple docstring"""
# setable values
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None # sigma(t_i)
@classmethod
def lowercase__ ( cls : Tuple )->Union[str, Any]:
return cls()
@dataclass
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
class _a ( lowerCAmelCase , lowerCAmelCase):
"""simple docstring"""
@property
def lowercase__ ( self : int )->List[Any]:
return True
@register_to_config
def __init__( self : Optional[Any] , __UpperCamelCase : float = 0.0_2 , __UpperCamelCase : float = 1_0_0 , __UpperCamelCase : float = 1.0_0_7 , __UpperCamelCase : float = 8_0 , __UpperCamelCase : float = 0.0_5 , __UpperCamelCase : float = 5_0 , )->Optional[Any]:
pass
def lowercase__ ( self : List[str] )->int:
return KarrasVeSchedulerState.create()
def lowercase__ ( self : str , __UpperCamelCase : KarrasVeSchedulerState , __UpperCamelCase : int , __UpperCamelCase : Tuple = () )->KarrasVeSchedulerState:
_UpperCAmelCase = jnp.arange(0 , __UpperCamelCase )[::-1].copy()
_UpperCAmelCase = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__UpperCamelCase , schedule=jnp.array(__UpperCamelCase , dtype=jnp.floataa ) , timesteps=__UpperCamelCase , )
def lowercase__ ( self : str , __UpperCamelCase : KarrasVeSchedulerState , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : float , __UpperCamelCase : random.KeyArray , )->Tuple[jnp.ndarray, float]:
if self.config.s_min <= sigma <= self.config.s_max:
_UpperCAmelCase = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
_UpperCAmelCase = 0
# sample eps ~ N(0, S_noise^2 * I)
_UpperCAmelCase = random.split(__UpperCamelCase , num=1 )
_UpperCAmelCase = self.config.s_noise * random.normal(key=__UpperCamelCase , shape=sample.shape )
_UpperCAmelCase = sigma + gamma * sigma
_UpperCAmelCase = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def lowercase__ ( self : List[str] , __UpperCamelCase : KarrasVeSchedulerState , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : bool = True , )->Union[FlaxKarrasVeOutput, Tuple]:
_UpperCAmelCase = sample_hat + sigma_hat * model_output
_UpperCAmelCase = (sample_hat - pred_original_sample) / sigma_hat
_UpperCAmelCase = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase , derivative=__UpperCamelCase , state=__UpperCamelCase )
def lowercase__ ( self : Dict , __UpperCamelCase : KarrasVeSchedulerState , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : jnp.ndarray , __UpperCamelCase : bool = True , )->Union[FlaxKarrasVeOutput, Tuple]:
_UpperCAmelCase = sample_prev + sigma_prev * model_output
_UpperCAmelCase = (sample_prev - pred_original_sample) / sigma_prev
_UpperCAmelCase = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase , derivative=__UpperCamelCase , state=__UpperCamelCase )
def lowercase__ ( self : List[Any] , __UpperCamelCase : KarrasVeSchedulerState , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] )->Union[str, Any]:
raise NotImplementedError()
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCAmelCase = arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE )]
# Reverse whole list
_UpperCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
__A : List[str] = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _a :
"""simple docstring"""
def __init__( self : int , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any]=1_3 , __UpperCamelCase : Any=3_0 , __UpperCamelCase : Optional[int]=2 , __UpperCamelCase : Union[str, Any]=3 , __UpperCamelCase : str=True , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Dict=3_2 , __UpperCamelCase : Optional[int]=5 , __UpperCamelCase : List[Any]=4 , __UpperCamelCase : str=3_7 , __UpperCamelCase : Union[str, Any]="gelu" , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : Optional[int]=0.1 , __UpperCamelCase : int=1_0 , __UpperCamelCase : str=0.0_2 , __UpperCamelCase : Dict=None , )->Dict:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase = (image_size // patch_size) ** 2
_UpperCAmelCase = num_patches + 1
def lowercase__ ( self : Dict )->int:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self : Any )->Optional[int]:
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowercase__ ( self : Any , __UpperCamelCase : Dict , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] )->List[Any]:
_UpperCAmelCase = ViTMSNModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Any , __UpperCamelCase : int , __UpperCamelCase : str , __UpperCamelCase : int )->Any:
_UpperCAmelCase = self.type_sequence_label_size
_UpperCAmelCase = ViTMSNForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , labels=__UpperCamelCase )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_UpperCAmelCase = 1
_UpperCAmelCase = ViTMSNForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase__ ( self : int )->Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _a ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
UpperCamelCase__ = (
{"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def lowercase__ ( self : Dict )->Tuple:
_UpperCAmelCase = ViTMSNModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=3_7 )
def lowercase__ ( self : Dict )->Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowercase__ ( self : Any )->List[str]:
pass
def lowercase__ ( self : Optional[int] )->Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def lowercase__ ( self : Optional[Any] )->List[str]:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(__UpperCamelCase )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def lowercase__ ( self : Tuple )->str:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowercase__ ( self : Optional[int] )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def lowercase__ ( self : List[Any] )->List[str]:
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = ViTMSNModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _a ( unittest.TestCase):
"""simple docstring"""
@cached_property
def lowercase__ ( self : Any )->Union[str, Any]:
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowercase__ ( self : List[str] )->int:
torch.manual_seed(2 )
_UpperCAmelCase = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__UpperCamelCase )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=__UpperCamelCase , return_tensors='''pt''' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**__UpperCamelCase )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
_UpperCAmelCase = torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1e-4 ) )
| 260 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase = np.zeros_like(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
_UpperCAmelCase = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
_UpperCAmelCase = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
_UpperCAmelCase = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__A : str = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
__A : str = np.array(Image.open(lena_path))
# kernel to be applied
__A : List[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__A : Optional[Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__A : Optional[Any] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 260 | 1 |
"""simple docstring"""
__A : str = 256
# Modulus to hash a string
__A : int = 1000003
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
if p_len > t_len:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# Calculating the hash of pattern and substring of text
for i in range(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_UpperCAmelCase = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_UpperCAmelCase = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_UpperCAmelCase = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = '''abc1abc12'''
_UpperCAmelCase = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_UpperCAmelCase = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and not rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 2)
_UpperCAmelCase = '''ABABX'''
_UpperCAmelCase = '''ABABZABABYABABX'''
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 3)
_UpperCAmelCase = '''AAAB'''
_UpperCAmelCase = '''ABAAAAAB'''
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 4)
_UpperCAmelCase = '''abcdabcy'''
_UpperCAmelCase = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 5)
_UpperCAmelCase = '''Lü'''
_UpperCAmelCase = '''Lüsai'''
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = '''Lue'''
assert not rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 260 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """audio-spectrogram-transformer"""
def __init__( self : int , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : int=1_2 , __UpperCamelCase : List[Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Dict=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1e-12 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=1_0 , __UpperCamelCase : Optional[int]=1_0 , __UpperCamelCase : str=1_0_2_4 , __UpperCamelCase : Optional[Any]=1_2_8 , **__UpperCamelCase : Any , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = patch_size
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = frequency_stride
_UpperCAmelCase = time_stride
_UpperCAmelCase = max_length
_UpperCAmelCase = num_mel_bins
| 260 | 1 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
_UpperCAmelCase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw ).convert('''RGB''' )
_UpperCAmelCase = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073) , (0.26862954, 0.26130258, 0.27577711) ),
] )
_UpperCAmelCase = transform(_SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(_SCREAMING_SNAKE_CASE )
return image
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
if "visual_encoder" in key:
_UpperCAmelCase = re.sub('''visual_encoder*''' , '''vision_model.encoder''' , _SCREAMING_SNAKE_CASE )
if "blocks" in key:
_UpperCAmelCase = re.sub(r'''blocks''' , '''layers''' , _SCREAMING_SNAKE_CASE )
if "attn" in key:
_UpperCAmelCase = re.sub(r'''attn''' , '''self_attn''' , _SCREAMING_SNAKE_CASE )
if "norm1" in key:
_UpperCAmelCase = re.sub(r'''norm1''' , '''layer_norm1''' , _SCREAMING_SNAKE_CASE )
if "norm2" in key:
_UpperCAmelCase = re.sub(r'''norm2''' , '''layer_norm2''' , _SCREAMING_SNAKE_CASE )
if "encoder.norm" in key:
_UpperCAmelCase = re.sub(r'''encoder.norm''' , '''post_layernorm''' , _SCREAMING_SNAKE_CASE )
if "encoder.patch_embed.proj" in key:
_UpperCAmelCase = re.sub(r'''encoder.patch_embed.proj''' , '''embeddings.patch_embedding''' , _SCREAMING_SNAKE_CASE )
if "encoder.pos_embed" in key:
_UpperCAmelCase = re.sub(r'''encoder.pos_embed''' , '''embeddings.position_embedding''' , _SCREAMING_SNAKE_CASE )
if "encoder.cls_token" in key:
_UpperCAmelCase = re.sub(r'''encoder.cls_token''' , '''embeddings.class_embedding''' , _SCREAMING_SNAKE_CASE )
if "self_attn" in key:
_UpperCAmelCase = re.sub(r'''self_attn.proj''' , '''self_attn.projection''' , _SCREAMING_SNAKE_CASE )
return key
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Any=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = BlipConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = BlipConfig(projection_dim=512 , text_config={} , vision_config={} )
_UpperCAmelCase = BlipForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''
_UpperCAmelCase = blip_decoder(pretrained=_SCREAMING_SNAKE_CASE , image_size=384 , vit='''base''' )
_UpperCAmelCase = pt_model.eval()
_UpperCAmelCase = pt_model.state_dict()
for key in modified_state_dict.copy():
_UpperCAmelCase = modified_state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rename_key(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = value
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = 384
_UpperCAmelCase = load_demo_image(image_size=_SCREAMING_SNAKE_CASE , device='''cpu''' )
_UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
_UpperCAmelCase = tokenizer(['''a picture of'''] ).input_ids
_UpperCAmelCase = hf_model.generate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
_UpperCAmelCase = hf_model.generate(_SCREAMING_SNAKE_CASE )
assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
_UpperCAmelCase = (
'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
)
_UpperCAmelCase = blip_vqa(pretrained=_SCREAMING_SNAKE_CASE , image_size=_SCREAMING_SNAKE_CASE , vit='''base''' )
vqa_model.eval()
_UpperCAmelCase = vqa_model.state_dict()
for key in modified_state_dict.copy():
_UpperCAmelCase = modified_state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rename_key(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = value
_UpperCAmelCase = BlipForQuestionAnswering(_SCREAMING_SNAKE_CASE )
hf_vqa_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = ['''How many dogs are in this image?''']
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids
_UpperCAmelCase = hf_vqa_model.generate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa''' )
_UpperCAmelCase = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''
_UpperCAmelCase = blip_itm(pretrained=_SCREAMING_SNAKE_CASE , image_size=_SCREAMING_SNAKE_CASE , vit='''base''' )
itm_model.eval()
_UpperCAmelCase = itm_model.state_dict()
for key in modified_state_dict.copy():
_UpperCAmelCase = modified_state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rename_key(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = value
_UpperCAmelCase = BlipForImageTextRetrieval(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = ['''A picture of a woman with a dog sitting in a beach''']
_UpperCAmelCase = tokenizer(
_SCREAMING_SNAKE_CASE , return_tensors='''pt''' , padding='''max_length''' , truncation=_SCREAMING_SNAKE_CASE , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(_SCREAMING_SNAKE_CASE )
hf_itm_model.eval()
_UpperCAmelCase = hf_itm_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , use_itm_head=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_itm_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , use_itm_head=_SCREAMING_SNAKE_CASE )
assert out[0].item() == 0.2110687494277954
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.45698845386505127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm''' )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : List[str] = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 260 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
_UpperCAmelCase = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
if month > 12:
year += 1
_UpperCAmelCase = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 260 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
__A : Dict = logging.getLogger(__name__)
__A : List[str] = "pytorch_model.bin"
@dataclasses.dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = dataclasses.field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""})
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""} , )
@dataclasses.dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""})
UpperCamelCase__ = dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""})
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """A csv or a json file containing the validation data."""})
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """The name of the task to train on."""} , )
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """The list of labels for the task."""})
@dataclasses.dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = dataclasses.field(
metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""})
UpperCamelCase__ = dataclasses.field(
default="""accuracy""" , metadata={"""help""": """The evaluation metric used for the task."""})
UpperCamelCase__ = dataclasses.field(
default="""no""" , metadata={
"""help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
} , )
UpperCamelCase__ = dataclasses.field(
default=10 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
UpperCamelCase__ = dataclasses.field(
default=0.0 , metadata={
"""help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
} , )
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""} , )
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""} , )
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""} , )
UpperCamelCase__ = dataclasses.field(
default=0.0 , metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""} , )
UpperCamelCase__ = dataclasses.field(
default=100 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
UpperCamelCase__ = dataclasses.field(
default=lowerCAmelCase , metadata={"""help""": """Random seed for initialization."""} , )
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_UpperCAmelCase = dataset.filter(lambda _SCREAMING_SNAKE_CASE : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_UpperCAmelCase = int(eval_result * len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = dataset.sort('''probability''' , reverse=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = dataset.select(range(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = dataset.remove_columns(['''label''', '''probability'''] )
_UpperCAmelCase = dataset.rename_column('''prediction''' , '''label''' )
_UpperCAmelCase = dataset.map(lambda _SCREAMING_SNAKE_CASE : {"label": idalabel[example["label"]]} )
_UpperCAmelCase = dataset.shuffle(seed=args.seed )
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , f'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(_SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE )
else:
dataset.to_json(_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Union[str, Any] , **_SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
_UpperCAmelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_UpperCAmelCase = STModelArguments(model_name_or_path=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = STDataArguments(train_file=_SCREAMING_SNAKE_CASE , infer_file=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = STTrainingArguments(output_dir=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_SCREAMING_SNAKE_CASE ).items():
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for key, value in kwargs.items():
if hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Sanity checks
_UpperCAmelCase = {}
_UpperCAmelCase = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_UpperCAmelCase = args.train_file
_UpperCAmelCase = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_UpperCAmelCase = args.eval_file
for key in data_files:
_UpperCAmelCase = data_files[key].split('''.''' )[-1]
assert extension in ["csv", "json"], f'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
_UpperCAmelCase = extension
else:
assert extension == args.data_file_extension, f'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), f'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('''Creating the initial data directory for self-training...''' )
_UpperCAmelCase = f'{args.output_dir}/self-train_iter-{{}}'.format
_UpperCAmelCase = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_SCREAMING_SNAKE_CASE )
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = 0
_UpperCAmelCase = False
# Show the progress bar
_UpperCAmelCase = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_UpperCAmelCase = data_dir_format(_SCREAMING_SNAKE_CASE )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''stage-1''' )
_UpperCAmelCase = {
'''accelerator''': accelerator,
'''model_name_or_path''': args.model_name_or_path,
'''cache_dir''': args.cache_dir,
'''do_train''': True,
'''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
'''do_eval''': True if args.eval_file is not None else False,
'''eval_file''': data_files['''eval'''],
'''do_predict''': True,
'''infer_file''': data_files['''infer'''],
'''task_name''': args.task_name,
'''label_list''': args.label_list,
'''output_dir''': current_output_dir,
'''eval_metric''': args.eval_metric,
'''evaluation_strategy''': args.evaluation_strategy,
'''early_stopping_patience''': args.early_stopping_patience,
'''early_stopping_threshold''': args.early_stopping_threshold,
'''seed''': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
arguments_dict.update({key: value} )
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''best-checkpoint''' , _SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''' , _SCREAMING_SNAKE_CASE )
finetune(**_SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
assert os.path.exists(_SCREAMING_SNAKE_CASE )
logger.info('''Self-training job completed: iteration: %d, stage: 1.''' , _SCREAMING_SNAKE_CASE )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''best-checkpoint''' )
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''stage-2''' )
# Update arguments_dict
_UpperCAmelCase = model_path
_UpperCAmelCase = data_files['''train''']
_UpperCAmelCase = current_output_dir
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''best-checkpoint''' , _SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''' , _SCREAMING_SNAKE_CASE )
finetune(**_SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
assert os.path.exists(_SCREAMING_SNAKE_CASE )
logger.info('''Self-training job completed: iteration: %d, stage: 2.''' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = iteration
_UpperCAmelCase = data_dir_format(iteration + 1 )
_UpperCAmelCase = AutoConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , '''best-checkpoint''' ) )
_UpperCAmelCase = config.idalabel
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''eval_results_best-checkpoint.json''' )
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''test_results_best-checkpoint.json''' )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , '''r''' ) as f:
_UpperCAmelCase = float(json.load(_SCREAMING_SNAKE_CASE )[args.eval_metric] )
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , '''infer_output_best-checkpoint.csv''' )
assert os.path.exists(_SCREAMING_SNAKE_CASE )
# Loading the dataset from local csv or json files.
_UpperCAmelCase = load_dataset(args.data_file_extension , data_files={'''data''': data_files['''infer''']} )['''data''']
_UpperCAmelCase = load_dataset('''csv''' , data_files={'''data''': infer_output_file} )['''data''']
if accelerator.is_main_process:
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
shutil.copy(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , f'eval_results_iter-{iteration}.json' ) )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
shutil.copy(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , f'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , f'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_UpperCAmelCase = eval_result
if best_iteration is None:
_UpperCAmelCase = new_iteration
_UpperCAmelCase = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_UpperCAmelCase = new_iteration
_UpperCAmelCase = new_eval_result
_UpperCAmelCase = 0
else:
if new_eval_result == best_eval_result:
_UpperCAmelCase = new_iteration
_UpperCAmelCase = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_UpperCAmelCase = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('''Best iteration: %d''' , _SCREAMING_SNAKE_CASE )
logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , _SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_SCREAMING_SNAKE_CASE , f'eval_results_iter-{iteration}.json' ) , os.path.join(_SCREAMING_SNAKE_CASE , '''eval_results_best-iteration.json''' ) , )
else:
# Assume that the last iteration is the best
logger.info('''Best iteration: %d''' , args.max_selftrain_iterations - 1 )
logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , _SCREAMING_SNAKE_CASE )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_SCREAMING_SNAKE_CASE , f'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(_SCREAMING_SNAKE_CASE , '''eval_results_best-iteration.json''' ) , )
| 260 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [n]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if len(str(_SCREAMING_SNAKE_CASE ) ) > 3:
if not is_prime(int(str(_SCREAMING_SNAKE_CASE )[-3:] ) ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE )[:3] ) ):
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(_SCREAMING_SNAKE_CASE ) != count:
if validate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_truncated_nums(_SCREAMING_SNAKE_CASE )
if all(is_prime(_SCREAMING_SNAKE_CASE ) for i in list_nums ):
list_truncated_primes.append(_SCREAMING_SNAKE_CASE )
num += 2
return list_truncated_primes
def lowercase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 260 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class _a ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : List[str] )->str:
_UpperCAmelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def lowercase__ ( self : Any , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] , *__UpperCamelCase : Any , **__UpperCamelCase : str )->str:
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCAmelCase = kwargs.pop('''main_process_only''' , __UpperCamelCase )
_UpperCAmelCase = kwargs.pop('''in_order''' , __UpperCamelCase )
if self.isEnabledFor(__UpperCamelCase ):
if self._should_log(__UpperCamelCase ):
_UpperCAmelCase , _UpperCAmelCase = self.process(__UpperCamelCase , __UpperCamelCase )
self.logger.log(__UpperCamelCase , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
elif in_order:
_UpperCAmelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCAmelCase , _UpperCAmelCase = self.process(__UpperCamelCase , __UpperCamelCase )
self.logger.log(__UpperCamelCase , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
state.wait_for_everyone()
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = None ):
'''simple docstring'''
if log_level is None:
_UpperCAmelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = logging.getLogger(_SCREAMING_SNAKE_CASE )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_SCREAMING_SNAKE_CASE , {} )
| 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : str = sys.version_info >= (3, 10)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : Tuple )->Optional[int]:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : List[str] )->List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def lowercase__ ( self : int )->str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : int , __UpperCamelCase : argparse.ArgumentParser , __UpperCamelCase : argparse.ArgumentParser )->Dict:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , __UpperCamelCase ) and yy.get('''choices''' , __UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](__UpperCamelCase ) , yy['''type'''](__UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--bar''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--baz''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--flag''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(__UpperCamelCase , look_for_args_file=__UpperCamelCase )
self.assertFalse(example.flag )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=4_2 , type=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=__UpperCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 4_2] , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : List[str] )->List[str]:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 4_2) , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
def lowercase__ ( self : int )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=__UpperCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--bar''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , bar=__UpperCamelCase , baz=__UpperCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo=1_2 , bar=3.1_4 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--required_str''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
_UpperCAmelCase = parser.parse_dict(__UpperCamelCase )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 4_2,
}
self.assertRaises(__UpperCamelCase , parser.parse_dict , __UpperCamelCase , allow_extra_keys=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_json''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_yaml''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 260 | 1 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any]=1_3 , __UpperCamelCase : Optional[int]=7 , __UpperCamelCase : str=True , __UpperCamelCase : str=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : List[str]=True , __UpperCamelCase : Optional[Any]=9_9 , __UpperCamelCase : Optional[int]=1_6 , __UpperCamelCase : Dict=3_6 , __UpperCamelCase : Tuple=6 , __UpperCamelCase : Optional[int]=6 , __UpperCamelCase : List[Any]=6 , __UpperCamelCase : int=3_7 , __UpperCamelCase : List[Any]="gelu" , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : List[Any]=5_1_2 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : int=2 , __UpperCamelCase : int=0.0_2 , __UpperCamelCase : Union[str, Any]=3 , __UpperCamelCase : Dict=4 , __UpperCamelCase : Dict=None , )->Union[str, Any]:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = embedding_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_hidden_groups
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
def lowercase__ ( self : List[Any] )->str:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase__ ( self : Optional[int] )->str:
return AlbertConfig(
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 , num_hidden_groups=self.num_hidden_groups , )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] )->str:
_UpperCAmelCase = AlbertModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase , token_type_ids=__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase__ ( self : Dict , __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : int , __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[Any] )->Union[str, Any]:
_UpperCAmelCase = AlbertForPreTraining(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase , sentence_order_label=__UpperCamelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def lowercase__ ( self : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] )->int:
_UpperCAmelCase = AlbertForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] )->Union[str, Any]:
_UpperCAmelCase = AlbertForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase__ ( self : int , __UpperCamelCase : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] )->Tuple:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = AlbertForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : List[str] , __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : List[str] , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict )->Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = AlbertForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase__ ( self : Any , __UpperCamelCase : str , __UpperCamelCase : str , __UpperCamelCase : Dict , __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] )->Optional[Any]:
_UpperCAmelCase = self.num_choices
_UpperCAmelCase = AlbertForMultipleChoice(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase__ ( self : str )->str:
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
"""feature-extraction""": AlbertModel,
"""fill-mask""": AlbertForMaskedLM,
"""question-answering""": AlbertForQuestionAnswering,
"""text-classification""": AlbertForSequenceClassification,
"""token-classification""": AlbertForTokenClassification,
"""zero-shot""": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
def lowercase__ ( self : str , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : str=False )->Union[str, Any]:
_UpperCAmelCase = super()._prepare_for_class(__UpperCamelCase , __UpperCamelCase , return_labels=__UpperCamelCase )
if return_labels:
if model_class in get_values(__UpperCamelCase ):
_UpperCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__UpperCamelCase )
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCamelCase )
return inputs_dict
def lowercase__ ( self : Optional[Any] )->int:
_UpperCAmelCase = AlbertModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=3_7 )
def lowercase__ ( self : Tuple )->Dict:
self.config_tester.run_common_tests()
def lowercase__ ( self : Union[str, Any] )->Optional[Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def lowercase__ ( self : Dict )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->List[Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def lowercase__ ( self : Tuple )->Dict:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Dict:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )
def lowercase__ ( self : Tuple )->Dict:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
@slow
def lowercase__ ( self : str )->str:
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = AlbertModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@require_torch
class _a ( unittest.TestCase):
"""simple docstring"""
@slow
def lowercase__ ( self : List[str] )->Optional[int]:
_UpperCAmelCase = AlbertModel.from_pretrained('''albert-base-v2''' )
_UpperCAmelCase = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_UpperCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
_UpperCAmelCase = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , __UpperCamelCase )
_UpperCAmelCase = torch.tensor(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __UpperCamelCase , atol=1e-4 ) )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase = True
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_UpperCAmelCase = True
if a[i].islower():
_UpperCAmelCase = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = [False] * len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = []
queue.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = True
while queue:
_UpperCAmelCase = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = True
_UpperCAmelCase = u
return visited[t]
def lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = [-1] * (len(_SCREAMING_SNAKE_CASE ))
_UpperCAmelCase = 0
while bfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = float('''Inf''' )
_UpperCAmelCase = sink
while s != source:
# Find the minimum value in select path
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , graph[parent[s]][s] )
_UpperCAmelCase = parent[s]
max_flow += path_flow
_UpperCAmelCase = sink
while v != source:
_UpperCAmelCase = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_UpperCAmelCase = parent[v]
return max_flow
__A : Union[str, Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__A , __A : List[str] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 260 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
quick_sort_random(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_SCREAMING_SNAKE_CASE , pivot_index + 1 , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCAmelCase = [int(_SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A : Union[str, Any] = logging.get_logger(__name__)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
if isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_SCREAMING_SNAKE_CASE ):
return [[videos]]
raise ValueError(f'Could not make batched video from {videos}' )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = ["""pixel_values"""]
def __init__( self : List[str] , __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_5_5 , __UpperCamelCase : bool = True , __UpperCamelCase : Optional[Union[float, List[float]]] = None , __UpperCamelCase : Optional[Union[float, List[float]]] = None , **__UpperCamelCase : Dict , )->None:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = size if size is not None else {'''shortest_edge''': 2_2_4}
_UpperCAmelCase = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
_UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
_UpperCAmelCase = get_size_dict(__UpperCamelCase , param_name='''crop_size''' )
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = resample
_UpperCAmelCase = do_rescale
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : np.ndarray , __UpperCamelCase : Dict[str, int] , __UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , __UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCamelCase : Dict , )->np.ndarray:
_UpperCAmelCase = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
_UpperCAmelCase = get_resize_output_image_size(__UpperCamelCase , size['''shortest_edge'''] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
_UpperCAmelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : Any , __UpperCamelCase : np.ndarray , __UpperCamelCase : Dict[str, int] , __UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCamelCase : Union[str, Any] , )->np.ndarray:
_UpperCAmelCase = 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 lowercase__ ( self : str , __UpperCamelCase : np.ndarray , __UpperCamelCase : Union[int, float] , __UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCamelCase : Optional[Any] , )->int:
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : Tuple , __UpperCamelCase : np.ndarray , __UpperCamelCase : Union[float, List[float]] , __UpperCamelCase : Union[float, List[float]] , __UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCamelCase : Tuple , )->np.ndarray:
return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : Dict , __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 : Optional[Union[float, List[float]]] = None , __UpperCamelCase : Optional[Union[float, List[float]]] = None , __UpperCamelCase : Optional[ChannelDimension] = ChannelDimension.FIRST , )->np.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_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.''' )
# All transformations expect numpy arrays.
_UpperCAmelCase = to_numpy_array(__UpperCamelCase )
if do_resize:
_UpperCAmelCase = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
_UpperCAmelCase = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
_UpperCAmelCase = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase )
if do_normalize:
_UpperCAmelCase = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
_UpperCAmelCase = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
def lowercase__ ( 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 : Optional[Union[float, List[float]]] = None , __UpperCamelCase : Optional[Union[float, List[float]]] = None , __UpperCamelCase : Optional[Union[str, TensorType]] = None , __UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **__UpperCamelCase : Any , )->PIL.Image.Image:
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
_UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase = 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.''' )
_UpperCAmelCase = make_batched(__UpperCamelCase )
_UpperCAmelCase = [
[
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 , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
_UpperCAmelCase = {'''pixel_values''': videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 260 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"
__A : List[str] = "\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to 'cuda' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n >>> results = perplexity.compute(model_id='gpt2',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 78.22\n >>> print(round(results[\"perplexities\"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = datasets.load_dataset(\"wikitext\",\n ... \"wikitext-2-raw-v1\",\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!='']\n >>> results = perplexity.compute(model_id='gpt2',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 60.35\n >>> print(round(results[\"perplexities\"][0], 2))\n 81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int = 1_6 , __UpperCamelCase : bool = True , __UpperCamelCase : List[Any]=None )->Any:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = '''cuda'''
else:
_UpperCAmelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors='''pt''' , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_UpperCAmelCase = encodings['''input_ids''']
_UpperCAmelCase = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 260 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
_UpperCAmelCase = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
_UpperCAmelCase = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
_UpperCAmelCase = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
_UpperCAmelCase = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
_UpperCAmelCase = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
_UpperCAmelCase = key.replace('''image_encoder.module''' , '''flava.image_model''' )
_UpperCAmelCase = key.replace('''text_encoder.module''' , '''flava.text_model''' )
_UpperCAmelCase = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
_UpperCAmelCase = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
_UpperCAmelCase = key.replace('''text_projection''' , '''flava.text_projection''' )
_UpperCAmelCase = key.replace('''image_projection''' , '''flava.image_projection''' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.getbasetemp() / '''cache'''
_UpperCAmelCase = test_hf_cache_home / '''datasets'''
_UpperCAmelCase = test_hf_cache_home / '''metrics'''
_UpperCAmelCase = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE , scope='''session''' )
def lowercase ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _SCREAMING_SNAKE_CASE )
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
_UpperCAmelCase , _UpperCAmelCase = head.next, head
while fast and fast.next:
_UpperCAmelCase = fast.next.next
_UpperCAmelCase = slow.next
_UpperCAmelCase = slow.next
_UpperCAmelCase = None # Don't forget here! But forget still works!
# reverse the second part
_UpperCAmelCase = None
while second:
_UpperCAmelCase = second.next
_UpperCAmelCase = node
_UpperCAmelCase = second
_UpperCAmelCase = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
_UpperCAmelCase = node.next
_UpperCAmelCase = head.next
return True
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
_UpperCAmelCase = _UpperCAmelCase = _UpperCAmelCase = head
while fast and fast.next:
_UpperCAmelCase , _UpperCAmelCase = fast.next.next, slow.next
# 2. Push the second half into the stack
_UpperCAmelCase = [slow.val]
while slow.next:
_UpperCAmelCase = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
_UpperCAmelCase = cur.next
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if not head or not head.next:
return True
_UpperCAmelCase = {}
_UpperCAmelCase = 0
while head:
if head.val in d:
d[head.val].append(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = [pos]
_UpperCAmelCase = head.next
pos += 1
_UpperCAmelCase = pos - 1
_UpperCAmelCase = 0
for v in d.values():
if len(_SCREAMING_SNAKE_CASE ) % 2 != 0:
middle += 1
else:
_UpperCAmelCase = 0
for i in range(0 , len(_SCREAMING_SNAKE_CASE ) ):
if v[i] + v[len(_SCREAMING_SNAKE_CASE ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase , _UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__A : Dict = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """poolformer"""
def __init__( self : List[str] , __UpperCamelCase : int=3 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : str=1_6 , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : int=4.0 , __UpperCamelCase : str=[2, 2, 6, 2] , __UpperCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __UpperCamelCase : int=[7, 3, 3, 3] , __UpperCamelCase : str=[4, 2, 2, 2] , __UpperCamelCase : Union[str, Any]=[2, 1, 1, 1] , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=0.0 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : List[str]=True , __UpperCamelCase : Union[str, Any]=1e-5 , __UpperCamelCase : str=0.0_2 , **__UpperCamelCase : List[Any] , )->Dict:
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = stride
_UpperCAmelCase = padding
_UpperCAmelCase = pool_size
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = depths
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_layer_scale
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = initializer_range
super().__init__(**__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = version.parse("""1.11""")
@property
def lowercase__ ( self : Union[str, Any] )->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase__ ( self : Tuple )->float:
return 2e-3
| 260 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
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
__A : int = logging.getLogger(__name__)
# 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>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
UpperCamelCase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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()
# 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_audio_classification''' , _SCREAMING_SNAKE_CASE , _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()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_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}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.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 , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__A : Union[str, Any] = "scheduler_config.json"
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 1
UpperCamelCase__ = 2
UpperCamelCase__ = 3
UpperCamelCase__ = 4
UpperCamelCase__ = 5
@dataclass
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a :
"""simple docstring"""
UpperCamelCase__ = SCHEDULER_CONFIG_NAME
UpperCamelCase__ = ["""dtype"""]
UpperCamelCase__ = []
UpperCamelCase__ = True
@classmethod
def lowercase__ ( cls : Any , __UpperCamelCase : Dict[str, Any] = None , __UpperCamelCase : Optional[str] = None , __UpperCamelCase : Tuple=False , **__UpperCamelCase : List[Any] , )->List[str]:
_UpperCAmelCase , _UpperCAmelCase = cls.load_config(
pretrained_model_name_or_path=__UpperCamelCase , subfolder=__UpperCamelCase , return_unused_kwargs=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase , _UpperCAmelCase = cls.from_config(__UpperCamelCase , return_unused_kwargs=__UpperCamelCase , **__UpperCamelCase )
if hasattr(__UpperCamelCase , '''create_state''' ) and getattr(__UpperCamelCase , '''has_state''' , __UpperCamelCase ):
_UpperCAmelCase = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, os.PathLike] , __UpperCamelCase : bool = False , **__UpperCamelCase : int )->Tuple:
self.save_config(save_directory=__UpperCamelCase , push_to_hub=__UpperCamelCase , **__UpperCamelCase )
@property
def lowercase__ ( self : Any )->int:
return self._get_compatibles()
@classmethod
def lowercase__ ( cls : Optional[int] )->Optional[int]:
_UpperCAmelCase = list(set([cls.__name__] + cls._compatibles ) )
_UpperCAmelCase = importlib.import_module(__name__.split('''.''' )[0] )
_UpperCAmelCase = [
getattr(__UpperCamelCase , __UpperCamelCase ) for c in compatible_classes_str if hasattr(__UpperCamelCase , __UpperCamelCase )
]
return compatible_classes
def lowercase ( _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : Tuple[int] ):
'''simple docstring'''
assert len(_SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_SCREAMING_SNAKE_CASE ) - x.ndim) ) , _SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Any=0.999 , _SCREAMING_SNAKE_CASE : Union[str, Any]=jnp.floataa ):
'''simple docstring'''
def alpha_bar(_SCREAMING_SNAKE_CASE : List[Any] ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
_UpperCAmelCase = []
for i in range(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = i / num_diffusion_timesteps
_UpperCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(_SCREAMING_SNAKE_CASE ) / alpha_bar(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) )
return jnp.array(_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@classmethod
def lowercase__ ( cls : Tuple , __UpperCamelCase : Optional[Any] )->List[str]:
_UpperCAmelCase = scheduler.config
if config.trained_betas is not None:
_UpperCAmelCase = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
_UpperCAmelCase = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCAmelCase = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
_UpperCAmelCase = 1.0 - betas
_UpperCAmelCase = jnp.cumprod(__UpperCamelCase , axis=0 )
return cls(
alphas=__UpperCamelCase , betas=__UpperCamelCase , alphas_cumprod=__UpperCamelCase , )
def lowercase ( _SCREAMING_SNAKE_CASE : CommonSchedulerState , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray ):
'''simple docstring'''
_UpperCAmelCase = state.alphas_cumprod
_UpperCAmelCase = alphas_cumprod[timesteps] ** 0.5
_UpperCAmelCase = sqrt_alpha_prod.flatten()
_UpperCAmelCase = broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE , original_samples.shape )
_UpperCAmelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
_UpperCAmelCase = sqrt_one_minus_alpha_prod.flatten()
_UpperCAmelCase = broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def lowercase ( _SCREAMING_SNAKE_CASE : CommonSchedulerState , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def lowercase ( _SCREAMING_SNAKE_CASE : CommonSchedulerState , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 260 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DPMSolverSinglestepScheduler,)
UpperCamelCase__ = (("""num_inference_steps""", 25),)
def lowercase__ ( self : Tuple , **__UpperCamelCase : Tuple )->Any:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf''' ),
'''variance_type''': None,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : Dict , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Any )->Union[str, Any]:
pass
def lowercase__ ( self : str , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : int , __UpperCamelCase : List[str]=None , **__UpperCamelCase : Optional[int] )->List[Any]:
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def lowercase__ ( self : List[Any] )->Dict:
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = 5_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_5_7_4 ) < 1e-3
def lowercase__ ( self : Dict )->Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : str )->Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->int:
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , algorithm_type='''dpmsolver++''' , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def lowercase__ ( self : str )->str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict )->List[str]:
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def lowercase__ ( self : Dict )->str:
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowercase__ ( self : List[str] )->int:
self.check_over_configs(variance_type=__UpperCamelCase )
self.check_over_configs(variance_type='''learned_range''' )
def lowercase__ ( self : List[str] )->Union[str, Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : List[str] )->List[str]:
_UpperCAmelCase = self.full_loop(use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_2_4_8 ) < 1e-3
def lowercase__ ( self : int )->List[Any]:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_4_5_3 ) < 1e-3
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_6_4_9 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 260 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
__A : Optional[Any] = pytest.mark.integration
__A : List[str] = {"comet"}
__A : List[Any] = importlib.util.find_spec("fairseq") is not None
__A : Optional[int] = {"code_eval"}
__A : Optional[int] = os.name == "nt"
__A : Optional[Any] = {"bertscore", "frugalscore", "perplexity"}
__A : List[str] = importlib.util.find_spec("transformers") is not None
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
@wraps(_SCREAMING_SNAKE_CASE )
def wrapper(self : Tuple , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('''"test requires Fairseq"''' )
else:
test_case(self , _SCREAMING_SNAKE_CASE )
return wrapper
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
@wraps(_SCREAMING_SNAKE_CASE )
def wrapper(self : int , _SCREAMING_SNAKE_CASE : str ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('''"test requires transformers"''' )
else:
test_case(self , _SCREAMING_SNAKE_CASE )
return wrapper
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
@wraps(_SCREAMING_SNAKE_CASE )
def wrapper(self : Optional[Any] , _SCREAMING_SNAKE_CASE : List[Any] ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('''"test not supported on Windows"''' )
else:
test_case(self , _SCREAMING_SNAKE_CASE )
return wrapper
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('''./metrics/*/''' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names())
@for_all_test_methods(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
@local
class _a ( parameterized.TestCase):
"""simple docstring"""
UpperCamelCase__ = {}
UpperCamelCase__ = None
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''' )
def lowercase__ ( self : int , __UpperCamelCase : Optional[int] )->Dict:
_UpperCAmelCase = '''[...]'''
_UpperCAmelCase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('''metrics''' , __UpperCamelCase ) ).module_path )
_UpperCAmelCase = datasets.load.import_main_class(metric_module.__name__ , dataset=__UpperCamelCase )
# check parameters
_UpperCAmelCase = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(__UpperCamelCase , metric_module.__name__ ):
with self.use_local_metrics():
try:
_UpperCAmelCase = doctest.testmod(__UpperCamelCase , verbose=__UpperCamelCase , raise_on_error=__UpperCamelCase )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Optional[Any] )->str:
_UpperCAmelCase = '''[...]'''
_UpperCAmelCase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('''metrics''' , __UpperCamelCase ) ).module_path )
# run doctest
with self.use_local_metrics():
_UpperCAmelCase = doctest.testmod(__UpperCamelCase , verbose=__UpperCamelCase , raise_on_error=__UpperCamelCase )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def lowercase__ ( self : str , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] )->Tuple:
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCamelCase ):
yield
else:
yield
@contextmanager
def lowercase__ ( self : Optional[Any] )->List[str]:
def load_local_metric(__UpperCamelCase : Optional[Any] , *__UpperCamelCase : Union[str, Any] , **__UpperCamelCase : Any ):
return load_metric(os.path.join('''metrics''' , __UpperCamelCase ) , *__UpperCamelCase , **__UpperCamelCase )
with patch('''datasets.load_metric''' ) as mock_load_metric:
_UpperCAmelCase = load_local_metric
yield
@classmethod
def lowercase__ ( cls : Optional[int] , __UpperCamelCase : Any )->Dict:
def wrapper(__UpperCamelCase : Optional[int] ):
_UpperCAmelCase = contextmanager(__UpperCamelCase )
_UpperCAmelCase = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('''bleurt''' )
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('''sv''' , '''''' , '''''' ) # handle pytest cli flags
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : str , __UpperCamelCase : List[Any] )->Optional[int]:
assert len(input_dict['''input_ids'''] ) == 2
return np.array([1.0_3, 1.0_4] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('''bleurt.score._create_predictor''' ) as mock_create_predictor:
_UpperCAmelCase = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('''bertscore''' )
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
import torch
def bert_cos_score_idf(_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] , *_SCREAMING_SNAKE_CASE : Dict , **_SCREAMING_SNAKE_CASE : int ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(_SCREAMING_SNAKE_CASE ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('''bert_score.scorer.get_model''' ), patch(
'''bert_score.scorer.bert_cos_score_idf''' ) as mock_bert_cos_score_idf:
_UpperCAmelCase = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('''comet''' )
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
def load_from_checkpoint(_SCREAMING_SNAKE_CASE : Union[str, Any] ):
class _a :
"""simple docstring"""
def lowercase__ ( self : Optional[int] , __UpperCamelCase : List[Any] , *__UpperCamelCase : Dict , **__UpperCamelCase : List[str] )->Any:
assert len(__UpperCamelCase ) == 2
_UpperCAmelCase = [0.1_9, 0.9_2]
return scores, sum(__UpperCamelCase ) / len(__UpperCamelCase )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('''comet.download_model''' ) as mock_download_model:
_UpperCAmelCase = None
with patch('''comet.load_from_checkpoint''' ) as mock_load_from_checkpoint:
_UpperCAmelCase = load_from_checkpoint
yield
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = load_metric(os.path.join('''metrics''' , '''seqeval''' ) )
_UpperCAmelCase = '''ERROR'''
_UpperCAmelCase = f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
with pytest.raises(_SCREAMING_SNAKE_CASE , match=re.escape(_SCREAMING_SNAKE_CASE ) ):
metric.compute(predictions=[] , references=[] , scheme=_SCREAMING_SNAKE_CASE )
| 260 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : float )->float:
return 0.0
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_UpperCAmelCase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.abs(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = 20 * np.logaa(_SCREAMING_SNAKE_CASE )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
_UpperCAmelCase = get_bounds(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(_SCREAMING_SNAKE_CASE )
plt.show()
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.angle(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(_SCREAMING_SNAKE_CASE , -2 * pi ) )
plt.show()
| 260 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__A : Optional[Any] = logging.get_logger(__name__)
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Union[str, Any] , *__UpperCamelCase : int , **__UpperCamelCase : Dict )->None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __UpperCamelCase , )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """camembert"""
def __init__( self : List[str] , __UpperCamelCase : Union[str, Any]=3_0_5_2_2 , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : Optional[int]=1_2 , __UpperCamelCase : Union[str, Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Dict="gelu" , __UpperCamelCase : Tuple=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : int=5_1_2 , __UpperCamelCase : Dict=2 , __UpperCamelCase : int=0.0_2 , __UpperCamelCase : int=1e-12 , __UpperCamelCase : Optional[Any]=1 , __UpperCamelCase : Dict=0 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : Any="absolute" , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : str=None , **__UpperCamelCase : Optional[Any] , )->str:
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class _a ( lowerCAmelCase):
"""simple docstring"""
@property
def lowercase__ ( self : int )->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 260 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """t5"""
UpperCamelCase__ = ["""past_key_values"""]
UpperCamelCase__ = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self : List[str] , __UpperCamelCase : int=3_2_1_2_8 , __UpperCamelCase : Optional[int]=5_1_2 , __UpperCamelCase : str=6_4 , __UpperCamelCase : Tuple=2_0_4_8 , __UpperCamelCase : List[str]=6 , __UpperCamelCase : str=None , __UpperCamelCase : Dict=8 , __UpperCamelCase : Any=3_2 , __UpperCamelCase : Optional[Any]=1_2_8 , __UpperCamelCase : List[Any]=0.1 , __UpperCamelCase : Tuple=1e-6 , __UpperCamelCase : Optional[int]=1.0 , __UpperCamelCase : Union[str, Any]="relu" , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Dict=True , __UpperCamelCase : Union[str, Any]=0 , __UpperCamelCase : List[Any]=1 , **__UpperCamelCase : Any , )->Optional[int]:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = d_model
_UpperCAmelCase = d_kv
_UpperCAmelCase = d_ff
_UpperCAmelCase = num_layers
_UpperCAmelCase = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_UpperCAmelCase = num_heads
_UpperCAmelCase = relative_attention_num_buckets
_UpperCAmelCase = relative_attention_max_distance
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_factor
_UpperCAmelCase = feed_forward_proj
_UpperCAmelCase = use_cache
_UpperCAmelCase = self.feed_forward_proj.split('''-''' )
_UpperCAmelCase = act_info[-1]
_UpperCAmelCase = act_info[0] == '''gated'''
if len(__UpperCamelCase ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
_UpperCAmelCase = '''gelu_new'''
super().__init__(
pad_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase , )
class _a ( lowerCAmelCase):
"""simple docstring"""
@property
def lowercase__ ( self : Dict )->Mapping[str, Mapping[int, str]]:
_UpperCAmelCase = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
_UpperCAmelCase = '''past_encoder_sequence + sequence'''
_UpperCAmelCase = {0: '''batch'''}
_UpperCAmelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
_UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__UpperCamelCase , direction='''inputs''' )
return common_inputs
@property
def lowercase__ ( self : int )->int:
return 1_3
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """poolformer"""
def __init__( self : List[str] , __UpperCamelCase : int=3 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : str=1_6 , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : int=4.0 , __UpperCamelCase : str=[2, 2, 6, 2] , __UpperCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __UpperCamelCase : int=[7, 3, 3, 3] , __UpperCamelCase : str=[4, 2, 2, 2] , __UpperCamelCase : Union[str, Any]=[2, 1, 1, 1] , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=0.0 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : List[str]=True , __UpperCamelCase : Union[str, Any]=1e-5 , __UpperCamelCase : str=0.0_2 , **__UpperCamelCase : List[Any] , )->Dict:
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = stride
_UpperCAmelCase = padding
_UpperCAmelCase = pool_size
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = depths
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_layer_scale
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = initializer_range
super().__init__(**__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = version.parse("""1.11""")
@property
def lowercase__ ( self : Union[str, Any] )->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase__ ( self : Tuple )->float:
return 2e-3
| 260 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [n]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if len(str(_SCREAMING_SNAKE_CASE ) ) > 3:
if not is_prime(int(str(_SCREAMING_SNAKE_CASE )[-3:] ) ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE )[:3] ) ):
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(_SCREAMING_SNAKE_CASE ) != count:
if validate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_truncated_nums(_SCREAMING_SNAKE_CASE )
if all(is_prime(_SCREAMING_SNAKE_CASE ) for i in list_nums ):
list_truncated_primes.append(_SCREAMING_SNAKE_CASE )
num += 2
return list_truncated_primes
def lowercase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 260 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Union[str, Any] = 16
__A : Optional[Any] = 32
def lowercase ( _SCREAMING_SNAKE_CASE : Accelerator , _SCREAMING_SNAKE_CASE : int = 16 ):
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_UpperCAmelCase = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_SCREAMING_SNAKE_CASE : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase = datasets.map(
_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_SCREAMING_SNAKE_CASE : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase = 8
else:
_UpperCAmelCase = None
return tokenizer.pad(
_SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : Optional[int] = mocked_dataloaders # noqa: F811
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _SCREAMING_SNAKE_CASE ) == "1":
_UpperCAmelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
_UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config['''lr''']
_UpperCAmelCase = int(config['''num_epochs'''] )
_UpperCAmelCase = int(config['''seed'''] )
_UpperCAmelCase = int(config['''batch_size'''] )
set_seed(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = get_dataloaders(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = AdamW(params=model.parameters() , lr=_SCREAMING_SNAKE_CASE )
# Instantiate scheduler
_UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=_SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(_SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_UpperCAmelCase = os.path.split(_SCREAMING_SNAKE_CASE )[-1].split('''.''' )[0]
accelerator.init_trackers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(_SCREAMING_SNAKE_CASE ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_UpperCAmelCase = 0
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(_SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_SCREAMING_SNAKE_CASE , references=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , _SCREAMING_SNAKE_CASE )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(_SCREAMING_SNAKE_CASE ),
'''epoch''': epoch,
} , step=_SCREAMING_SNAKE_CASE , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=_SCREAMING_SNAKE_CASE , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase)
class _a ( lowerCAmelCase):
"""simple docstring"""
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
UpperCamelCase__ = field(default="""summarization""" , metadata={"""include_in_asdict_even_if_is_default""": True})
UpperCamelCase__ = Features({"""text""": Value("""string""")})
UpperCamelCase__ = Features({"""summary""": Value("""string""")})
UpperCamelCase__ = "text"
UpperCamelCase__ = "summary"
@property
def lowercase__ ( self : Any )->Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCAmelCase = 0
count += depth_first_search(_SCREAMING_SNAKE_CASE , row + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , row - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col + 1 , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col - 1 , _SCREAMING_SNAKE_CASE )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """rwkv"""
UpperCamelCase__ = {"""max_position_embeddings""": """context_length"""}
def __init__( self : List[str] , __UpperCamelCase : Any=5_0_2_7_7 , __UpperCamelCase : List[Any]=1_0_2_4 , __UpperCamelCase : int=4_0_9_6 , __UpperCamelCase : Any=3_2 , __UpperCamelCase : int=None , __UpperCamelCase : List[Any]=None , __UpperCamelCase : int=1e-5 , __UpperCamelCase : Optional[Any]=0 , __UpperCamelCase : Tuple=0 , __UpperCamelCase : Union[str, Any]=6 , __UpperCamelCase : int=False , __UpperCamelCase : Any=True , **__UpperCamelCase : Tuple , )->Optional[int]:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = context_length
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_UpperCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = rescale_every
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
| 260 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
_UpperCAmelCase = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
_UpperCAmelCase = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
_UpperCAmelCase = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
_UpperCAmelCase = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
_UpperCAmelCase = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
_UpperCAmelCase = key.replace('''image_encoder.module''' , '''flava.image_model''' )
_UpperCAmelCase = key.replace('''text_encoder.module''' , '''flava.text_model''' )
_UpperCAmelCase = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
_UpperCAmelCase = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
_UpperCAmelCase = key.replace('''text_projection''' , '''flava.text_projection''' )
_UpperCAmelCase = key.replace('''image_projection''' , '''flava.image_projection''' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 260 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(_SCREAMING_SNAKE_CASE ):
requests.request('''GET''' , '''https://huggingface.co''' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('''GET''' , '''https://huggingface.co''' , timeout=1.0 )
@pytest.mark.integration
def lowercase ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('''GET''' , '''https://huggingface.co''' )
def lowercase ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(_SCREAMING_SNAKE_CASE ):
http_head('''https://huggingface.co''' )
| 260 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowercase ( _SCREAMING_SNAKE_CASE : Features ):
'''simple docstring'''
_UpperCAmelCase = np.inf
def set_batch_size(_SCREAMING_SNAKE_CASE : FeatureType ) -> None:
nonlocal batch_size
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and feature.dtype == "binary":
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return None if batch_size is np.inf else batch_size
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCamelCase : NestedDataStructureLike[PathLike] , __UpperCamelCase : Optional[NamedSplit] = None , __UpperCamelCase : Optional[Features] = None , __UpperCamelCase : str = None , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : int , )->Union[str, Any]:
super().__init__(
__UpperCamelCase , split=__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase , streaming=__UpperCamelCase , num_proc=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = path_or_paths if isinstance(__UpperCamelCase , __UpperCamelCase ) else {self.split: path_or_paths}
_UpperCAmelCase = _PACKAGED_DATASETS_MODULES['''parquet'''][1]
_UpperCAmelCase = Parquet(
cache_dir=__UpperCamelCase , data_files=__UpperCamelCase , features=__UpperCamelCase , hash=__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : Union[str, Any] )->Dict:
# Build iterable dataset
if self.streaming:
_UpperCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=__UpperCamelCase , download_mode=__UpperCamelCase , verification_mode=__UpperCamelCase , base_path=__UpperCamelCase , num_proc=self.num_proc , )
_UpperCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=__UpperCamelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , __UpperCamelCase : Dataset , __UpperCamelCase : Union[PathLike, BinaryIO] , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : Tuple , )->Optional[int]:
_UpperCAmelCase = dataset
_UpperCAmelCase = path_or_buf
_UpperCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_UpperCAmelCase = parquet_writer_kwargs
def lowercase__ ( self : Optional[int] )->int:
_UpperCAmelCase = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , '''wb+''' ) as buffer:
_UpperCAmelCase = self._write(file_obj=__UpperCamelCase , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
else:
_UpperCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
return written
def lowercase__ ( self : int , __UpperCamelCase : BinaryIO , __UpperCamelCase : int , **__UpperCamelCase : int )->int:
_UpperCAmelCase = 0
_UpperCAmelCase = parquet_writer_kwargs.pop('''path_or_buf''' , __UpperCamelCase )
_UpperCAmelCase = self.dataset.features.arrow_schema
_UpperCAmelCase = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase , **__UpperCamelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , __UpperCamelCase ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating parquet from Arrow format''' , ):
_UpperCAmelCase = query_table(
table=self.dataset._data , key=slice(__UpperCamelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(__UpperCamelCase )
written += batch.nbytes
writer.close()
return written
| 260 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__A : Any = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : tuple , _SCREAMING_SNAKE_CASE : Path , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str]=False , ):
'''simple docstring'''
output_path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=_SCREAMING_SNAKE_CASE , output_names=_SCREAMING_SNAKE_CASE , dynamic_axes=_SCREAMING_SNAKE_CASE , do_constant_folding=_SCREAMING_SNAKE_CASE , use_external_data_format=_SCREAMING_SNAKE_CASE , enable_onnx_checker=_SCREAMING_SNAKE_CASE , opset_version=_SCREAMING_SNAKE_CASE , )
else:
export(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=_SCREAMING_SNAKE_CASE , output_names=_SCREAMING_SNAKE_CASE , dynamic_axes=_SCREAMING_SNAKE_CASE , do_constant_folding=_SCREAMING_SNAKE_CASE , opset_version=_SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
_UpperCAmelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
_UpperCAmelCase = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
_UpperCAmelCase = '''cpu'''
_UpperCAmelCase = Path(_SCREAMING_SNAKE_CASE )
# VAE DECODER
_UpperCAmelCase = AutoencoderKL.from_pretrained(model_path + '''/vae''' )
_UpperCAmelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
_UpperCAmelCase = vae_decoder.decode
onnx_export(
_SCREAMING_SNAKE_CASE , model_args=(
torch.randn(1 , _SCREAMING_SNAKE_CASE , 25 , 25 ).to(device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE ),
False,
) , output_path=output_path / '''vae_decoder''' / '''model.onnx''' , ordered_input_names=['''latent_sample''', '''return_dict'''] , output_names=['''sample'''] , dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=_SCREAMING_SNAKE_CASE , )
del vae_decoder
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
__A : Tuple = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX")
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase , _UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__A : Dict = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 260 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = args.pruning_method
_UpperCAmelCase = args.threshold
_UpperCAmelCase = args.model_name_or_path.rstrip('''/''' )
_UpperCAmelCase = args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
_UpperCAmelCase = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
_UpperCAmelCase = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "bias" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
_UpperCAmelCase = MagnitudeBinarizer.apply(inputs=_SCREAMING_SNAKE_CASE , threshold=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = TopKBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase , _UpperCAmelCase = -0.1, 1.1
_UpperCAmelCase = torch.sigmoid(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = s * (r - l) + l
_UpperCAmelCase = s_bar.clamp(min=0.0 , max=1.0 )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
_UpperCAmelCase = os.path.join(
os.path.dirname(_SCREAMING_SNAKE_CASE ) , f'bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE )}' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
shutil.copytree(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f'\nCreated folder {target_model_path}' )
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__A : Optional[int] = parser.parse_args()
main(args)
| 260 | 1 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__A : List[str] = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase)
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : int , *__UpperCamelCase : str , **__UpperCamelCase : List[Any] )->Optional[int]:
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
requires_backends(self , '''decord''' )
self.check_model_type(__UpperCamelCase )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : List[Any]=None , __UpperCamelCase : int=None , __UpperCamelCase : Optional[Any]=None )->Any:
_UpperCAmelCase = {}
if frame_sampling_rate is not None:
_UpperCAmelCase = frame_sampling_rate
if num_frames is not None:
_UpperCAmelCase = num_frames
_UpperCAmelCase = {}
if top_k is not None:
_UpperCAmelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : int , __UpperCamelCase : Union[str, List[str]] , **__UpperCamelCase : List[str] )->Union[str, Any]:
return super().__call__(__UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str]=None , __UpperCamelCase : List[Any]=1 )->Tuple:
if num_frames is None:
_UpperCAmelCase = self.model.config.num_frames
if video.startswith('''http://''' ) or video.startswith('''https://''' ):
_UpperCAmelCase = BytesIO(requests.get(__UpperCamelCase ).content )
_UpperCAmelCase = VideoReader(__UpperCamelCase )
videoreader.seek(0 )
_UpperCAmelCase = 0
_UpperCAmelCase = num_frames * frame_sampling_rate - 1
_UpperCAmelCase = np.linspace(__UpperCamelCase , __UpperCamelCase , num=__UpperCamelCase , dtype=np.intaa )
_UpperCAmelCase = videoreader.get_batch(__UpperCamelCase ).asnumpy()
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = self.image_processor(__UpperCamelCase , return_tensors=self.framework )
return model_inputs
def lowercase__ ( self : List[str] , __UpperCamelCase : str )->Optional[int]:
_UpperCAmelCase = self.model(**__UpperCamelCase )
return model_outputs
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : str , __UpperCamelCase : Optional[int]=5 )->int:
if top_k > self.model.config.num_labels:
_UpperCAmelCase = self.model.config.num_labels
if self.framework == "pt":
_UpperCAmelCase = model_outputs.logits.softmax(-1 )[0]
_UpperCAmelCase , _UpperCAmelCase = probs.topk(__UpperCamelCase )
else:
raise ValueError(F'Unsupported framework: {self.framework}' )
_UpperCAmelCase = scores.tolist()
_UpperCAmelCase = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCamelCase , __UpperCamelCase )]
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCAmelCase = arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE )]
# Reverse whole list
_UpperCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
__A : List[str] = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
'''helper utility that will spawn up '''
'''multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' , type=_SCREAMING_SNAKE_CASE , default=1 , help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' , type=_SCREAMING_SNAKE_CASE , help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) , )
# rest from the training program
parser.add_argument('''training_script_args''' , nargs=_SCREAMING_SNAKE_CASE )
return parser.parse_args()
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = parse_args()
# Import training_script as a module.
_UpperCAmelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_UpperCAmelCase = script_fpath.stem
_UpperCAmelCase = importlib.import_module(_SCREAMING_SNAKE_CASE )
# Patch sys.argv
_UpperCAmelCase = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase = np.zeros_like(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
_UpperCAmelCase = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
_UpperCAmelCase = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
_UpperCAmelCase = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__A : str = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
__A : str = np.array(Image.open(lena_path))
# kernel to be applied
__A : List[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__A : Optional[Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__A : Optional[Any] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
_UpperCAmelCase = (boundary[1] - boundary[0]) / steps
_UpperCAmelCase = boundary[0]
_UpperCAmelCase = boundary[1]
_UpperCAmelCase = make_points(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = 0.0
y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE )
for i in x_i:
# print(i)
y += h * f(_SCREAMING_SNAKE_CASE )
y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE )
return y
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = a + h
while x < (b - h):
yield x
_UpperCAmelCase = x + h
def lowercase ( _SCREAMING_SNAKE_CASE : Any ): # enter your function here
'''simple docstring'''
_UpperCAmelCase = (x - 0) * (x - 0)
return y
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = 0.0 # Lower bound of integration
_UpperCAmelCase = 1.0 # Upper bound of integration
_UpperCAmelCase = 10.0 # define number of steps or resolution
_UpperCAmelCase = [a, b] # define boundary of integration
_UpperCAmelCase = method_a(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f'y = {y}' )
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """audio-spectrogram-transformer"""
def __init__( self : int , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : int=1_2 , __UpperCamelCase : List[Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Dict=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1e-12 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=1_0 , __UpperCamelCase : Optional[int]=1_0 , __UpperCamelCase : str=1_0_2_4 , __UpperCamelCase : Optional[Any]=1_2_8 , **__UpperCamelCase : Any , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = patch_size
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = frequency_stride
_UpperCAmelCase = time_stride
_UpperCAmelCase = max_length
_UpperCAmelCase = num_mel_bins
| 260 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__A : Union[str, Any] = logging.get_logger(__name__)
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
_UpperCAmelCase = MaskFormerConfig(backbone_config=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
_UpperCAmelCase = 847
_UpperCAmelCase = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
_UpperCAmelCase = 150
_UpperCAmelCase = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
_UpperCAmelCase = 171
_UpperCAmelCase = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
_UpperCAmelCase = 133
_UpperCAmelCase = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
_UpperCAmelCase = 19
_UpperCAmelCase = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
_UpperCAmelCase = 65
_UpperCAmelCase = '''mapillary-vistas-id2label.json'''
_UpperCAmelCase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
_UpperCAmelCase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
return config
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight') )
rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = dct.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = val
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase = state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
_UpperCAmelCase = state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[:dim, :]
_UpperCAmelCase = in_proj_bias[: dim]
_UpperCAmelCase = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase = in_proj_weight[
-dim :, :
]
_UpperCAmelCase = in_proj_bias[-dim :]
# fmt: on
def lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase = state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
_UpperCAmelCase = state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[: hidden_size, :]
_UpperCAmelCase = in_proj_bias[:config.hidden_size]
_UpperCAmelCase = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase = state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
_UpperCAmelCase = state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[: hidden_size, :]
_UpperCAmelCase = in_proj_bias[:config.hidden_size]
_UpperCAmelCase = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase = in_proj_bias[-hidden_size :]
# fmt: on
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCAmelCase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
_UpperCAmelCase = get_maskformer_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
with open(_SCREAMING_SNAKE_CASE , '''rb''' ) as f:
_UpperCAmelCase = pickle.load(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_UpperCAmelCase = create_rename_keys(_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
read_in_swin_q_k_v(_SCREAMING_SNAKE_CASE , config.backbone_config )
read_in_decoder_q_k_v(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# update to torch tensors
for key, value in state_dict.items():
_UpperCAmelCase = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# load 🤗 model
_UpperCAmelCase = MaskFormerForInstanceSegmentation(_SCREAMING_SNAKE_CASE )
model.eval()
for name, param in model.named_parameters():
print(_SCREAMING_SNAKE_CASE , param.shape )
_UpperCAmelCase , _UpperCAmelCase = model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_SCREAMING_SNAKE_CASE ) == 0, f'Unexpected keys: {unexpected_keys}'
# verify results
_UpperCAmelCase = prepare_img()
if "vistas" in model_name:
_UpperCAmelCase = 65
elif "cityscapes" in model_name:
_UpperCAmelCase = 6_5535
else:
_UpperCAmelCase = 255
_UpperCAmelCase = True if '''ade''' in model_name else False
_UpperCAmelCase = MaskFormerImageProcessor(ignore_index=_SCREAMING_SNAKE_CASE , reduce_labels=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
print('''Logits:''' , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_UpperCAmelCase = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f'Saving 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 )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(f'nielsr/{model_name}' )
image_processor.push_to_hub(f'nielsr/{model_name}' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="maskformer-swin-tiny-ade",
type=str,
help=("Name of the MaskFormer model you'd like to convert",),
)
parser.add_argument(
"--checkpoint_path",
default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
type=str,
help="Path to the original state dict (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
__A : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 260 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
_UpperCAmelCase = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
if month > 12:
year += 1
_UpperCAmelCase = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 1000 ):
'''simple docstring'''
_UpperCAmelCase = 2**power
_UpperCAmelCase = 0
while n:
_UpperCAmelCase , _UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 260 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [n]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if len(str(_SCREAMING_SNAKE_CASE ) ) > 3:
if not is_prime(int(str(_SCREAMING_SNAKE_CASE )[-3:] ) ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE )[:3] ) ):
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(_SCREAMING_SNAKE_CASE ) != count:
if validate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_truncated_nums(_SCREAMING_SNAKE_CASE )
if all(is_prime(_SCREAMING_SNAKE_CASE ) for i in list_nums ):
list_truncated_primes.append(_SCREAMING_SNAKE_CASE )
num += 2
return list_truncated_primes
def lowercase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 260 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
__A : Union[str, Any] = list[tuple[int, int]]
__A : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__A : str = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class _a :
"""simple docstring"""
def __init__( self : Any , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : Node | None )->Any:
_UpperCAmelCase = pos_x
_UpperCAmelCase = pos_y
_UpperCAmelCase = (pos_y, pos_x)
_UpperCAmelCase = goal_x
_UpperCAmelCase = goal_y
_UpperCAmelCase = parent
class _a :
"""simple docstring"""
def __init__( self : List[str] , __UpperCamelCase : tuple[int, int] , __UpperCamelCase : tuple[int, int] )->str:
_UpperCAmelCase = Node(start[1] , start[0] , goal[1] , goal[0] , __UpperCamelCase )
_UpperCAmelCase = Node(goal[1] , goal[0] , goal[1] , goal[0] , __UpperCamelCase )
_UpperCAmelCase = [self.start]
_UpperCAmelCase = False
def lowercase__ ( self : str )->Path | None:
while self.node_queue:
_UpperCAmelCase = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_UpperCAmelCase = True
return self.retrace_path(__UpperCamelCase )
_UpperCAmelCase = self.get_successors(__UpperCamelCase )
for node in successors:
self.node_queue.append(__UpperCamelCase )
if not self.reached:
return [self.start.pos]
return None
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Node )->list[Node]:
_UpperCAmelCase = []
for action in delta:
_UpperCAmelCase = parent.pos_x + action[1]
_UpperCAmelCase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__UpperCamelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__UpperCamelCase , __UpperCamelCase , self.target.pos_y , self.target.pos_x , __UpperCamelCase ) )
return successors
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Node | None )->Path:
_UpperCAmelCase = node
_UpperCAmelCase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_UpperCAmelCase = current_node.parent
path.reverse()
return path
class _a :
"""simple docstring"""
def __init__( self : str , __UpperCamelCase : Tuple , __UpperCamelCase : Dict )->Dict:
_UpperCAmelCase = BreadthFirstSearch(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = BreadthFirstSearch(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = False
def lowercase__ ( self : Tuple )->Path | None:
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_UpperCAmelCase = self.fwd_bfs.node_queue.pop(0 )
_UpperCAmelCase = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_UpperCAmelCase = True
return self.retrace_bidirectional_path(
__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = current_bwd_node
_UpperCAmelCase = current_fwd_node
_UpperCAmelCase = {
self.fwd_bfs: self.fwd_bfs.get_successors(__UpperCamelCase ),
self.bwd_bfs: self.bwd_bfs.get_successors(__UpperCamelCase ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__UpperCamelCase )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def lowercase__ ( self : str , __UpperCamelCase : Node , __UpperCamelCase : Node )->Path:
_UpperCAmelCase = self.fwd_bfs.retrace_path(__UpperCamelCase )
_UpperCAmelCase = self.bwd_bfs.retrace_path(__UpperCamelCase )
bwd_path.pop()
bwd_path.reverse()
_UpperCAmelCase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
__A : Tuple = (0, 0)
__A : Union[str, Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__A : Optional[int] = time.time()
__A : Optional[int] = BreadthFirstSearch(init, goal)
__A : Tuple = bfs.search()
__A : Union[str, Any] = time.time() - start_bfs_time
print("Unidirectional BFS computation time : ", bfs_time)
__A : Any = time.time()
__A : Optional[int] = BidirectionalBreadthFirstSearch(init, goal)
__A : Union[str, Any] = bd_bfs.search()
__A : List[str] = time.time() - start_bd_bfs_time
print("Bidirectional BFS computation time : ", bd_bfs_time)
| 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : str = sys.version_info >= (3, 10)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : Tuple )->Optional[int]:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : List[str] )->List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def lowercase__ ( self : int )->str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : int , __UpperCamelCase : argparse.ArgumentParser , __UpperCamelCase : argparse.ArgumentParser )->Dict:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , __UpperCamelCase ) and yy.get('''choices''' , __UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](__UpperCamelCase ) , yy['''type'''](__UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--bar''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--baz''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--flag''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(__UpperCamelCase , look_for_args_file=__UpperCamelCase )
self.assertFalse(example.flag )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=4_2 , type=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=__UpperCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 4_2] , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : List[str] )->List[str]:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 4_2) , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
def lowercase__ ( self : int )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=__UpperCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--bar''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , bar=__UpperCamelCase , baz=__UpperCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo=1_2 , bar=3.1_4 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--required_str''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
_UpperCAmelCase = parser.parse_dict(__UpperCamelCase )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 4_2,
}
self.assertRaises(__UpperCamelCase , parser.parse_dict , __UpperCamelCase , allow_extra_keys=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_json''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_yaml''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowercase ( _SCREAMING_SNAKE_CASE : int = 5000 ):
'''simple docstring'''
_UpperCAmelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , _SCREAMING_SNAKE_CASE )]
for i, pentagonal_i in enumerate(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ):
_UpperCAmelCase = pentagonal_nums[j]
_UpperCAmelCase = pentagonal_i + pentagonal_j
_UpperCAmelCase = pentagonal_j - pentagonal_i
if is_pentagonal(_SCREAMING_SNAKE_CASE ) and is_pentagonal(_SCREAMING_SNAKE_CASE ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase = True
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_UpperCAmelCase = True
if a[i].islower():
_UpperCAmelCase = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
def merge(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return collection
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__A : int = input("Enter numbers separated by a comma:\n").strip()
__A : Any = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 260 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
quick_sort_random(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_SCREAMING_SNAKE_CASE , pivot_index + 1 , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCAmelCase = [int(_SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""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 _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : str )->Tuple:
_UpperCAmelCase = '''laion/clap-htsat-unfused'''
_UpperCAmelCase = tempfile.mkdtemp()
def lowercase__ ( self : List[Any] , **__UpperCamelCase : List[Any] )->str:
return RobertaTokenizer.from_pretrained(self.checkpoint , **__UpperCamelCase )
def lowercase__ ( self : int , **__UpperCamelCase : Dict )->Dict:
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__UpperCamelCase )
def lowercase__ ( self : Dict )->Optional[Any]:
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = ClapProcessor(tokenizer=__UpperCamelCase , feature_extractor=__UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCamelCase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __UpperCamelCase )
def lowercase__ ( self : List[str] )->Any:
_UpperCAmelCase = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_UpperCAmelCase = self.get_feature_extractor(do_normalize=__UpperCamelCase , padding_value=1.0 )
_UpperCAmelCase = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCamelCase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = ClapProcessor(tokenizer=__UpperCamelCase , feature_extractor=__UpperCamelCase )
_UpperCAmelCase = floats_list((3, 1_0_0_0) )
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors='''np''' )
_UpperCAmelCase = processor(audios=__UpperCamelCase , 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 lowercase__ ( self : Any )->Optional[int]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = ClapProcessor(tokenizer=__UpperCamelCase , feature_extractor=__UpperCamelCase )
_UpperCAmelCase = '''This is a test string'''
_UpperCAmelCase = processor(text=__UpperCamelCase )
_UpperCAmelCase = tokenizer(__UpperCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase__ ( self : Optional[Any] )->Optional[Any]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = ClapProcessor(tokenizer=__UpperCamelCase , feature_extractor=__UpperCamelCase )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(__UpperCamelCase )
_UpperCAmelCase = tokenizer.batch_decode(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[int] )->List[str]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = ClapProcessor(tokenizer=__UpperCamelCase , feature_extractor=__UpperCamelCase )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
| 260 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"
__A : List[str] = "\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to 'cuda' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n >>> results = perplexity.compute(model_id='gpt2',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 78.22\n >>> print(round(results[\"perplexities\"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = datasets.load_dataset(\"wikitext\",\n ... \"wikitext-2-raw-v1\",\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!='']\n >>> results = perplexity.compute(model_id='gpt2',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 60.35\n >>> print(round(results[\"perplexities\"][0], 2))\n 81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int = 1_6 , __UpperCamelCase : bool = True , __UpperCamelCase : List[Any]=None )->Any:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = '''cuda'''
else:
_UpperCAmelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors='''pt''' , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_UpperCAmelCase = encodings['''input_ids''']
_UpperCAmelCase = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[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 ):
_UpperCAmelCase = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
_UpperCAmelCase = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
_UpperCAmelCase = subset[i - 1][j]
if arr[i - 1] <= j:
_UpperCAmelCase = 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()
| 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.getbasetemp() / '''cache'''
_UpperCAmelCase = test_hf_cache_home / '''datasets'''
_UpperCAmelCase = test_hf_cache_home / '''metrics'''
_UpperCAmelCase = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE , scope='''session''' )
def lowercase ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _SCREAMING_SNAKE_CASE )
| 260 | 1 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str=1_3 , __UpperCamelCase : Optional[int]=7 , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : Any=True , __UpperCamelCase : Union[str, Any]=9_9 , __UpperCamelCase : Tuple=3_2 , __UpperCamelCase : int=5 , __UpperCamelCase : List[Any]=4 , __UpperCamelCase : Optional[Any]=6_4 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Dict=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : Union[str, Any]=5_1_2 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : Union[str, Any]=0.0_2 , __UpperCamelCase : str=3 , __UpperCamelCase : Union[str, Any]=4 , __UpperCamelCase : List[str]=None , __UpperCamelCase : Any=2 , __UpperCamelCase : Dict=2 , __UpperCamelCase : List[str]=2 , __UpperCamelCase : str=2 , __UpperCamelCase : str=4 , __UpperCamelCase : Dict=1 , )->Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
_UpperCAmelCase = q_groups
_UpperCAmelCase = k_groups
_UpperCAmelCase = v_groups
_UpperCAmelCase = post_attention_groups
_UpperCAmelCase = intermediate_groups
_UpperCAmelCase = output_groups
def lowercase__ ( self : Union[str, Any] )->List[Any]:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase__ ( self : Optional[Any] )->List[Any]:
return SqueezeBertConfig(
embedding_size=self.hidden_size , 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 , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : str , __UpperCamelCase : Any )->Optional[Any]:
_UpperCAmelCase = SqueezeBertModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : int , __UpperCamelCase : str , __UpperCamelCase : Tuple )->Tuple:
_UpperCAmelCase = SqueezeBertForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str , __UpperCamelCase : Tuple )->Optional[Any]:
_UpperCAmelCase = SqueezeBertForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase__ ( self : Any , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] )->Optional[int]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = SqueezeBertForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple )->Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = SqueezeBertForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase__ ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int )->Optional[Any]:
_UpperCAmelCase = self.num_choices
_UpperCAmelCase = SqueezeBertForMultipleChoice(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = self.prepare_config_and_inputs()
((_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase)) = config_and_inputs
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
UpperCamelCase__ = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = False
def lowercase__ ( self : Dict )->int:
_UpperCAmelCase = SqueezeBertModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , dim=3_7 )
def lowercase__ ( self : List[Any] )->List[Any]:
self.config_tester.run_common_tests()
def lowercase__ ( self : Optional[Any] )->Tuple:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*__UpperCamelCase )
def lowercase__ ( self : Tuple )->str:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*__UpperCamelCase )
def lowercase__ ( self : Dict )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*__UpperCamelCase )
def lowercase__ ( self : Any )->Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*__UpperCamelCase )
def lowercase__ ( self : List[str] )->int:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__UpperCamelCase )
@slow
def lowercase__ ( self : Optional[Any] )->Dict:
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = SqueezeBertModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@require_sentencepiece
@require_tokenizers
@require_torch
class _a ( unittest.TestCase):
"""simple docstring"""
@slow
def lowercase__ ( self : List[str] )->Optional[int]:
_UpperCAmelCase = SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli''' )
_UpperCAmelCase = torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]] )
_UpperCAmelCase = model(__UpperCamelCase )[0]
_UpperCAmelCase = torch.Size((1, 3) )
self.assertEqual(output.shape , __UpperCamelCase )
_UpperCAmelCase = torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]] )
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-4 ) )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase , _UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__A : Dict = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Any = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = ["VisionEncoderDecoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = ["TFVisionEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ["FlaxVisionEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
__A : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
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
__A : int = logging.getLogger(__name__)
# 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>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
UpperCamelCase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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()
# 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_audio_classification''' , _SCREAMING_SNAKE_CASE , _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()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_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}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.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 , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DPMSolverSinglestepScheduler,)
UpperCamelCase__ = (("""num_inference_steps""", 25),)
def lowercase__ ( self : Tuple , **__UpperCamelCase : Tuple )->Any:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf''' ),
'''variance_type''': None,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : Dict , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Any )->Union[str, Any]:
pass
def lowercase__ ( self : str , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : int , __UpperCamelCase : List[str]=None , **__UpperCamelCase : Optional[int] )->List[Any]:
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def lowercase__ ( self : List[Any] )->Dict:
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = 5_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_5_7_4 ) < 1e-3
def lowercase__ ( self : Dict )->Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : str )->Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->int:
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , algorithm_type='''dpmsolver++''' , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def lowercase__ ( self : str )->str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict )->List[str]:
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def lowercase__ ( self : Dict )->str:
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowercase__ ( self : List[str] )->int:
self.check_over_configs(variance_type=__UpperCamelCase )
self.check_over_configs(variance_type='''learned_range''' )
def lowercase__ ( self : List[str] )->Union[str, Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : List[str] )->List[str]:
_UpperCAmelCase = self.full_loop(use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_2_4_8 ) < 1e-3
def lowercase__ ( self : int )->List[Any]:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_4_5_3 ) < 1e-3
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_6_4_9 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 260 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DPMSolverSinglestepScheduler,)
UpperCamelCase__ = (("""num_inference_steps""", 25),)
def lowercase__ ( self : Tuple , **__UpperCamelCase : Tuple )->Any:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf''' ),
'''variance_type''': None,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : Dict , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Any )->Union[str, Any]:
pass
def lowercase__ ( self : str , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : int , __UpperCamelCase : List[str]=None , **__UpperCamelCase : Optional[int] )->List[Any]:
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def lowercase__ ( self : List[Any] )->Dict:
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = 5_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_5_7_4 ) < 1e-3
def lowercase__ ( self : Dict )->Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : str )->Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->int:
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , algorithm_type='''dpmsolver++''' , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def lowercase__ ( self : str )->str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict )->List[str]:
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def lowercase__ ( self : Dict )->str:
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowercase__ ( self : List[str] )->int:
self.check_over_configs(variance_type=__UpperCamelCase )
self.check_over_configs(variance_type='''learned_range''' )
def lowercase__ ( self : List[str] )->Union[str, Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : List[str] )->List[str]:
_UpperCAmelCase = self.full_loop(use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_2_4_8 ) < 1e-3
def lowercase__ ( self : int )->List[Any]:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_4_5_3 ) < 1e-3
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_6_4_9 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 260 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowercase ( _SCREAMING_SNAKE_CASE : BertModel , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')
_UpperCAmelCase = (
('''layer.''', '''layer_'''),
('''word_embeddings.weight''', '''word_embeddings'''),
('''position_embeddings.weight''', '''position_embeddings'''),
('''token_type_embeddings.weight''', '''token_type_embeddings'''),
('''.''', '''/'''),
('''LayerNorm/weight''', '''LayerNorm/gamma'''),
('''LayerNorm/bias''', '''LayerNorm/beta'''),
('''weight''', '''kernel'''),
)
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.state_dict()
def to_tf_var_name(_SCREAMING_SNAKE_CASE : str ):
for patt, repl in iter(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = name.replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return f'bert/{name}'
def create_tf_var(_SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : tf.Session ):
_UpperCAmelCase = tf.dtypes.as_dtype(tensor.dtype )
_UpperCAmelCase = tf.get_variable(dtype=_SCREAMING_SNAKE_CASE , shape=tensor.shape , name=_SCREAMING_SNAKE_CASE , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(_SCREAMING_SNAKE_CASE )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
_UpperCAmelCase = to_tf_var_name(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
_UpperCAmelCase = torch_tensor.T
_UpperCAmelCase = create_tf_var(tensor=_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE , session=_SCREAMING_SNAKE_CASE )
tf.keras.backend.set_value(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = session.run(_SCREAMING_SNAKE_CASE )
print(f'Successfully created {tf_name}: {np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}' )
_UpperCAmelCase = tf.train.Saver(tf.trainable_variables() )
saver.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , model_name.replace('''-''' , '''_''' ) + '''.ckpt''' ) )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str]=None ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help='''model name e.g. bert-base-uncased''' )
parser.add_argument(
'''--cache_dir''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help='''Directory containing pytorch model''' )
parser.add_argument('''--pytorch_model_path''' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help='''/path/to/<pytorch-model-name>.bin''' )
parser.add_argument('''--tf_cache_dir''' , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help='''Directory in which to save tensorflow model''' )
_UpperCAmelCase = parser.parse_args(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=_SCREAMING_SNAKE_CASE , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : float )->float:
return 0.0
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_UpperCAmelCase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.abs(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = 20 * np.logaa(_SCREAMING_SNAKE_CASE )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
_UpperCAmelCase = get_bounds(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(_SCREAMING_SNAKE_CASE )
plt.show()
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.angle(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(_SCREAMING_SNAKE_CASE , -2 * pi ) )
plt.show()
| 260 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all LED models at https://huggingface.co/models?filter=LED
__A : List[Any] = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
__A : int = {
"allenai/led-base-16384": 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_UpperCAmelCase = bs[:]
_UpperCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
_UpperCAmelCase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCAmelCase = char
return pairs
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , __UpperCamelCase : Tuple , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any]="replace" , __UpperCamelCase : List[str]="<s>" , __UpperCamelCase : Union[str, Any]="</s>" , __UpperCamelCase : Optional[Any]="</s>" , __UpperCamelCase : List[Any]="<s>" , __UpperCamelCase : Any="<unk>" , __UpperCamelCase : List[Any]="<pad>" , __UpperCamelCase : Tuple="<mask>" , __UpperCamelCase : str=False , **__UpperCamelCase : Any , )->int:
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else bos_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else eos_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else sep_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else cls_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else unk_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token
super().__init__(
errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , **__UpperCamelCase , )
with open(__UpperCamelCase , encoding='''utf-8''' ) as vocab_handle:
_UpperCAmelCase = json.load(__UpperCamelCase )
_UpperCAmelCase = {v: k for k, v in self.encoder.items()}
_UpperCAmelCase = errors # how to handle errors in decoding
_UpperCAmelCase = bytes_to_unicode()
_UpperCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(__UpperCamelCase , encoding='''utf-8''' ) as merges_handle:
_UpperCAmelCase = merges_handle.read().split('''\n''' )[1:-1]
_UpperCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_UpperCAmelCase = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
_UpperCAmelCase = {}
_UpperCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_UpperCAmelCase = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def lowercase__ ( self : str )->Dict:
return len(self.encoder )
def lowercase__ ( self : List[str] )->Union[str, Any]:
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase__ ( self : Dict , __UpperCamelCase : int )->Optional[Any]:
if token in self.cache:
return self.cache[token]
_UpperCAmelCase = tuple(__UpperCamelCase )
_UpperCAmelCase = get_pairs(__UpperCamelCase )
if not pairs:
return token
while True:
_UpperCAmelCase = min(__UpperCamelCase , key=lambda __UpperCamelCase : self.bpe_ranks.get(__UpperCamelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCAmelCase , _UpperCAmelCase = bigram
_UpperCAmelCase = []
_UpperCAmelCase = 0
while i < len(__UpperCamelCase ):
try:
_UpperCAmelCase = word.index(__UpperCamelCase , __UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCAmelCase = j
if word[i] == first and i < len(__UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCAmelCase = tuple(__UpperCamelCase )
_UpperCAmelCase = new_word
if len(__UpperCamelCase ) == 1:
break
else:
_UpperCAmelCase = get_pairs(__UpperCamelCase )
_UpperCAmelCase = ''' '''.join(__UpperCamelCase )
_UpperCAmelCase = word
return word
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Tuple )->int:
_UpperCAmelCase = []
for token in re.findall(self.pat , __UpperCamelCase ):
_UpperCAmelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase ).split(''' ''' ) )
return bpe_tokens
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Tuple )->Optional[Any]:
return self.encoder.get(__UpperCamelCase , self.encoder.get(self.unk_token ) )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : int )->List[str]:
return self.decoder.get(__UpperCamelCase )
def lowercase__ ( self : Any , __UpperCamelCase : Dict )->List[Any]:
_UpperCAmelCase = ''''''.join(__UpperCamelCase )
_UpperCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def lowercase__ ( self : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None )->Tuple[str]:
if not os.path.isdir(__UpperCamelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
__UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_UpperCAmelCase = os.path.join(
__UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__UpperCamelCase , ensure_ascii=__UpperCamelCase ) + '''\n''' )
_UpperCAmelCase = 0
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __UpperCamelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
''' Please check that the tokenizer is not corrupted!''' )
_UpperCAmelCase = token_index
writer.write(''' '''.join(__UpperCamelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
def lowercase__ ( self : int , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None )->List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
_UpperCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase__ ( self : List[Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None , __UpperCamelCase : bool = False )->List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCamelCase )) + [1]
return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]
def lowercase__ ( self : Tuple , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None )->List[int]:
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Optional[Any]=False , **__UpperCamelCase : List[str] )->Dict:
_UpperCAmelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase ) > 0 and not text[0].isspace()):
_UpperCAmelCase = ''' ''' + text
return (text, kwargs)
def lowercase__ ( self : List[str] , __UpperCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : Optional[bool] = None , )->dict:
_UpperCAmelCase = super()._pad(
encoded_inputs=__UpperCamelCase , max_length=__UpperCamelCase , padding_strategy=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_attention_mask=__UpperCamelCase , )
# Load from model defaults
if return_attention_mask is None:
_UpperCAmelCase = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_UpperCAmelCase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_UpperCAmelCase = len(encoded_inputs['''global_attention_mask'''] ) != len(__UpperCamelCase )
if needs_to_be_padded:
_UpperCAmelCase = len(__UpperCamelCase ) - len(encoded_inputs['''global_attention_mask'''] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_UpperCAmelCase = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
_UpperCAmelCase = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return encoded_inputs
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """camembert"""
def __init__( self : List[str] , __UpperCamelCase : Union[str, Any]=3_0_5_2_2 , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : Optional[int]=1_2 , __UpperCamelCase : Union[str, Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Dict="gelu" , __UpperCamelCase : Tuple=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : int=5_1_2 , __UpperCamelCase : Dict=2 , __UpperCamelCase : int=0.0_2 , __UpperCamelCase : int=1e-12 , __UpperCamelCase : Optional[Any]=1 , __UpperCamelCase : Dict=0 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : Any="absolute" , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : str=None , **__UpperCamelCase : Optional[Any] , )->str:
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class _a ( lowerCAmelCase):
"""simple docstring"""
@property
def lowercase__ ( self : int )->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 260 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
__A : Optional[int] = logging.get_logger(__name__)
__A : int = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__A : Any = {
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
__A : Tuple = {"mobilebert-uncased": 512}
__A : int = {}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = MobileBertTokenizer
def __init__( self : str , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : str=None , __UpperCamelCase : Tuple=True , __UpperCamelCase : Optional[int]="[UNK]" , __UpperCamelCase : List[str]="[SEP]" , __UpperCamelCase : Optional[int]="[PAD]" , __UpperCamelCase : Any="[CLS]" , __UpperCamelCase : str="[MASK]" , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Tuple=None , **__UpperCamelCase : Union[str, Any] , )->Union[str, Any]:
super().__init__(
__UpperCamelCase , tokenizer_file=__UpperCamelCase , do_lower_case=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , pad_token=__UpperCamelCase , cls_token=__UpperCamelCase , mask_token=__UpperCamelCase , tokenize_chinese_chars=__UpperCamelCase , strip_accents=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCamelCase ) != tokenize_chinese_chars
):
_UpperCAmelCase = getattr(__UpperCamelCase , normalizer_state.pop('''type''' ) )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = strip_accents
_UpperCAmelCase = tokenize_chinese_chars
_UpperCAmelCase = normalizer_class(**__UpperCamelCase )
_UpperCAmelCase = do_lower_case
def lowercase__ ( self : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Any=None )->Any:
_UpperCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : Tuple , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None )->List[int]:
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None )->Tuple[str]:
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """poolformer"""
def __init__( self : List[str] , __UpperCamelCase : int=3 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : str=1_6 , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : int=4.0 , __UpperCamelCase : str=[2, 2, 6, 2] , __UpperCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __UpperCamelCase : int=[7, 3, 3, 3] , __UpperCamelCase : str=[4, 2, 2, 2] , __UpperCamelCase : Union[str, Any]=[2, 1, 1, 1] , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=0.0 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : List[str]=True , __UpperCamelCase : Union[str, Any]=1e-5 , __UpperCamelCase : str=0.0_2 , **__UpperCamelCase : List[Any] , )->Dict:
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = stride
_UpperCAmelCase = padding
_UpperCAmelCase = pool_size
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = depths
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_layer_scale
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = initializer_range
super().__init__(**__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = version.parse("""1.11""")
@property
def lowercase__ ( self : Union[str, Any] )->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase__ ( self : Tuple )->float:
return 2e-3
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
_UpperCAmelCase = hex_num[0] == '''-'''
if is_negative:
_UpperCAmelCase = hex_num[1:]
try:
_UpperCAmelCase = int(_SCREAMING_SNAKE_CASE , 16 )
except ValueError:
raise ValueError('''Invalid value was passed to the function''' )
_UpperCAmelCase = ''''''
while int_num > 0:
_UpperCAmelCase = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('''-''' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Union[str, Any] = 16
__A : Optional[Any] = 32
def lowercase ( _SCREAMING_SNAKE_CASE : Accelerator , _SCREAMING_SNAKE_CASE : int = 16 ):
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_UpperCAmelCase = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_SCREAMING_SNAKE_CASE : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase = datasets.map(
_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_SCREAMING_SNAKE_CASE : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase = 8
else:
_UpperCAmelCase = None
return tokenizer.pad(
_SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : Optional[int] = mocked_dataloaders # noqa: F811
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _SCREAMING_SNAKE_CASE ) == "1":
_UpperCAmelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
_UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config['''lr''']
_UpperCAmelCase = int(config['''num_epochs'''] )
_UpperCAmelCase = int(config['''seed'''] )
_UpperCAmelCase = int(config['''batch_size'''] )
set_seed(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = get_dataloaders(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = AdamW(params=model.parameters() , lr=_SCREAMING_SNAKE_CASE )
# Instantiate scheduler
_UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=_SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(_SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_UpperCAmelCase = os.path.split(_SCREAMING_SNAKE_CASE )[-1].split('''.''' )[0]
accelerator.init_trackers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(_SCREAMING_SNAKE_CASE ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_UpperCAmelCase = 0
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(_SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_SCREAMING_SNAKE_CASE , references=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , _SCREAMING_SNAKE_CASE )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(_SCREAMING_SNAKE_CASE ),
'''epoch''': epoch,
} , step=_SCREAMING_SNAKE_CASE , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=_SCREAMING_SNAKE_CASE , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
from collections import defaultdict
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 1
_UpperCAmelCase = True
for v in tree[start]:
if v not in visited:
ret += dfs(_SCREAMING_SNAKE_CASE )
if ret % 2 == 0:
cuts.append(_SCREAMING_SNAKE_CASE )
return ret
def lowercase ( ):
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
__A , __A : Dict = 10, 9
__A : int = defaultdict(list)
__A : dict[int, bool] = {}
__A : list[int] = []
__A : Optional[Any] = 0
__A : Union[str, Any] = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCAmelCase = 0
count += depth_first_search(_SCREAMING_SNAKE_CASE , row + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , row - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col + 1 , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col - 1 , _SCREAMING_SNAKE_CASE )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.getbasetemp() / '''cache'''
_UpperCAmelCase = test_hf_cache_home / '''datasets'''
_UpperCAmelCase = test_hf_cache_home / '''metrics'''
_UpperCAmelCase = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE , scope='''session''' )
def lowercase ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _SCREAMING_SNAKE_CASE )
| 260 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
_UpperCAmelCase = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
_UpperCAmelCase = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
_UpperCAmelCase = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
_UpperCAmelCase = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
_UpperCAmelCase = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
_UpperCAmelCase = key.replace('''image_encoder.module''' , '''flava.image_model''' )
_UpperCAmelCase = key.replace('''text_encoder.module''' , '''flava.text_model''' )
_UpperCAmelCase = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
_UpperCAmelCase = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
_UpperCAmelCase = key.replace('''text_projection''' , '''flava.text_projection''' )
_UpperCAmelCase = key.replace('''image_projection''' , '''flava.image_projection''' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : Any = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
"CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"CTRLForSequenceClassification",
"CTRLLMHeadModel",
"CTRLModel",
"CTRLPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
"TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCTRLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowercase ( _SCREAMING_SNAKE_CASE : Features ):
'''simple docstring'''
_UpperCAmelCase = np.inf
def set_batch_size(_SCREAMING_SNAKE_CASE : FeatureType ) -> None:
nonlocal batch_size
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and feature.dtype == "binary":
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return None if batch_size is np.inf else batch_size
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCamelCase : NestedDataStructureLike[PathLike] , __UpperCamelCase : Optional[NamedSplit] = None , __UpperCamelCase : Optional[Features] = None , __UpperCamelCase : str = None , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : int , )->Union[str, Any]:
super().__init__(
__UpperCamelCase , split=__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase , streaming=__UpperCamelCase , num_proc=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = path_or_paths if isinstance(__UpperCamelCase , __UpperCamelCase ) else {self.split: path_or_paths}
_UpperCAmelCase = _PACKAGED_DATASETS_MODULES['''parquet'''][1]
_UpperCAmelCase = Parquet(
cache_dir=__UpperCamelCase , data_files=__UpperCamelCase , features=__UpperCamelCase , hash=__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : Union[str, Any] )->Dict:
# Build iterable dataset
if self.streaming:
_UpperCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=__UpperCamelCase , download_mode=__UpperCamelCase , verification_mode=__UpperCamelCase , base_path=__UpperCamelCase , num_proc=self.num_proc , )
_UpperCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=__UpperCamelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , __UpperCamelCase : Dataset , __UpperCamelCase : Union[PathLike, BinaryIO] , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : Tuple , )->Optional[int]:
_UpperCAmelCase = dataset
_UpperCAmelCase = path_or_buf
_UpperCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_UpperCAmelCase = parquet_writer_kwargs
def lowercase__ ( self : Optional[int] )->int:
_UpperCAmelCase = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , '''wb+''' ) as buffer:
_UpperCAmelCase = self._write(file_obj=__UpperCamelCase , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
else:
_UpperCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
return written
def lowercase__ ( self : int , __UpperCamelCase : BinaryIO , __UpperCamelCase : int , **__UpperCamelCase : int )->int:
_UpperCAmelCase = 0
_UpperCAmelCase = parquet_writer_kwargs.pop('''path_or_buf''' , __UpperCamelCase )
_UpperCAmelCase = self.dataset.features.arrow_schema
_UpperCAmelCase = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase , **__UpperCamelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , __UpperCamelCase ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating parquet from Arrow format''' , ):
_UpperCAmelCase = query_table(
table=self.dataset._data , key=slice(__UpperCamelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(__UpperCamelCase )
written += batch.nbytes
writer.close()
return written
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
__A : List[Any] = logging.get_logger(__name__)
@dataclass
class _a :
"""simple docstring"""
def __init__( self : List[str] , __UpperCamelCase : int=False , __UpperCamelCase : Dict=False , __UpperCamelCase : Optional[Any]=6.0 , __UpperCamelCase : Tuple=None , __UpperCamelCase : List[Any]=False , __UpperCamelCase : List[str]=False , __UpperCamelCase : List[str]=None , __UpperCamelCase : Tuple="fp4" , __UpperCamelCase : Optional[Any]=False , **__UpperCamelCase : List[str] , )->Optional[int]:
_UpperCAmelCase = load_in_abit
_UpperCAmelCase = load_in_abit
_UpperCAmelCase = llm_inta_threshold
_UpperCAmelCase = llm_inta_skip_modules
_UpperCAmelCase = llm_inta_enable_fpaa_cpu_offload
_UpperCAmelCase = llm_inta_has_fpaa_weight
_UpperCAmelCase = bnb_abit_quant_type
_UpperCAmelCase = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
_UpperCAmelCase = torch.floataa
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCAmelCase = getattr(__UpperCamelCase , __UpperCamelCase )
elif isinstance(__UpperCamelCase , torch.dtype ):
_UpperCAmelCase = bnb_abit_compute_dtype
else:
raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype''' )
self.post_init()
def lowercase__ ( self : str )->List[Any]:
if not isinstance(self.llm_inta_threshold , __UpperCamelCase ):
raise ValueError('''llm_int8_threshold must be a float''' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , __UpperCamelCase ):
raise ValueError('''llm_int8_skip_modules must be a list of strings''' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , __UpperCamelCase ):
raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean''' )
if not isinstance(self.llm_inta_has_fpaa_weight , __UpperCamelCase ):
raise ValueError('''llm_int8_has_fp16_weight must be a boolean''' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype''' )
if not isinstance(self.bnb_abit_quant_type , __UpperCamelCase ):
raise ValueError('''bnb_4bit_quant_type must be a string''' )
if not isinstance(self.bnb_abit_use_double_quant , __UpperCamelCase ):
raise ValueError('''bnb_4bit_use_double_quant must be a boolean''' )
if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes''' ) ) >= version.parse(
'''0.39.0''' ):
raise ValueError(
'''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version''' )
def lowercase__ ( self : List[Any] )->Optional[int]:
return self.load_in_abit or self.load_in_abit
def lowercase__ ( self : Dict )->Optional[int]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def lowercase__ ( cls : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = cls(**__UpperCamelCase )
_UpperCAmelCase = []
for key, value in kwargs.items():
if hasattr(__UpperCamelCase , __UpperCamelCase ):
setattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
to_remove.append(__UpperCamelCase )
for key in to_remove:
kwargs.pop(__UpperCamelCase , __UpperCamelCase )
if return_unused_kwargs:
return config, kwargs
else:
return config
def lowercase__ ( self : int , __UpperCamelCase : Union[str, os.PathLike] )->Tuple:
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as writer:
_UpperCAmelCase = self.to_dict()
_UpperCAmelCase = json.dumps(__UpperCamelCase , indent=2 , sort_keys=__UpperCamelCase ) + '''\n'''
writer.write(__UpperCamelCase )
def lowercase__ ( self : Any )->Dict[str, Any]:
_UpperCAmelCase = copy.deepcopy(self.__dict__ )
_UpperCAmelCase = str(output['''bnb_4bit_compute_dtype'''] ).split('''.''' )[1]
return output
def __repr__( self : Dict )->List[Any]:
return F'{self.__class__.__name__} {self.to_json_string()}'
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : bool = True )->str:
if use_diff is True:
_UpperCAmelCase = self.to_diff_dict()
else:
_UpperCAmelCase = self.to_dict()
return json.dumps(__UpperCamelCase , indent=2 , sort_keys=__UpperCamelCase ) + "\n"
def lowercase__ ( self : str )->Dict[str, Any]:
_UpperCAmelCase = self.to_dict()
# get the default config dict
_UpperCAmelCase = BitsAndBytesConfig().to_dict()
_UpperCAmelCase = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
_UpperCAmelCase = value
return serializable_config_dict
| 260 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = args.pruning_method
_UpperCAmelCase = args.threshold
_UpperCAmelCase = args.model_name_or_path.rstrip('''/''' )
_UpperCAmelCase = args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
_UpperCAmelCase = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
_UpperCAmelCase = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "bias" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
_UpperCAmelCase = MagnitudeBinarizer.apply(inputs=_SCREAMING_SNAKE_CASE , threshold=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = TopKBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase , _UpperCAmelCase = -0.1, 1.1
_UpperCAmelCase = torch.sigmoid(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = s * (r - l) + l
_UpperCAmelCase = s_bar.clamp(min=0.0 , max=1.0 )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
_UpperCAmelCase = os.path.join(
os.path.dirname(_SCREAMING_SNAKE_CASE ) , f'bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE )}' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
shutil.copytree(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f'\nCreated folder {target_model_path}' )
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__A : Optional[int] = parser.parse_args()
main(args)
| 260 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = inspect.getfile(accelerate.test_utils)
UpperCamelCase__ = os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_cli.py"""])
UpperCamelCase__ = ["""accelerate""", """launch"""]
UpperCamelCase__ = Path.home() / """.cache/huggingface/accelerate"""
UpperCamelCase__ = """default_config.yaml"""
UpperCamelCase__ = config_folder / config_file
UpperCamelCase__ = config_folder / """_default_config.yaml"""
UpperCamelCase__ = Path("""tests/test_configs""")
@classmethod
def lowercase__ ( cls : Any )->Dict:
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowercase__ ( cls : int )->List[str]:
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowercase__ ( self : str )->Any:
_UpperCAmelCase = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowercase__ ( self : int )->Optional[Any]:
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__UpperCamelCase ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__UpperCamelCase ), self.test_file_path] , env=os.environ.copy() )
def lowercase__ ( self : int )->str:
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class _a ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = """test-tpu"""
UpperCamelCase__ = """us-central1-a"""
UpperCamelCase__ = """ls"""
UpperCamelCase__ = ["""accelerate""", """tpu-config"""]
UpperCamelCase__ = """cd /usr/share"""
UpperCamelCase__ = """tests/test_samples/test_command_file.sh"""
UpperCamelCase__ = """Running gcloud compute tpus tpu-vm ssh"""
def lowercase__ ( self : Any )->Dict:
_UpperCAmelCase = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , __UpperCamelCase , )
def lowercase__ ( self : Optional[int] )->Optional[int]:
_UpperCAmelCase = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , __UpperCamelCase , )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__UpperCamelCase )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , __UpperCamelCase , )
def lowercase__ ( self : List[str] )->int:
_UpperCAmelCase = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , __UpperCamelCase , )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all' , __UpperCamelCase , )
def lowercase__ ( self : List[str] )->Optional[Any]:
_UpperCAmelCase = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , __UpperCamelCase , )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , __UpperCamelCase , )
def lowercase__ ( self : Any )->str:
_UpperCAmelCase = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all' , __UpperCamelCase , )
def lowercase__ ( self : Optional[Any] )->List[Any]:
_UpperCAmelCase = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__UpperCamelCase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all' , __UpperCamelCase , )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCAmelCase = arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE )]
# Reverse whole list
_UpperCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
__A : List[str] = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase = np.zeros_like(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
_UpperCAmelCase = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
_UpperCAmelCase = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
_UpperCAmelCase = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__A : str = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
__A : str = np.array(Image.open(lena_path))
# kernel to be applied
__A : List[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__A : Optional[Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__A : Optional[Any] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 260 | 1 |
"""simple docstring"""
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] )->Dict:
return F'gaussian_noise_s={seed}_shape={"_".join([str(__UpperCamelCase ) for s in shape] )}.npy'
def lowercase__ ( self : Any )->Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowercase__ ( self : List[Any] , __UpperCamelCase : List[str]=0 , __UpperCamelCase : List[Any]=(4, 4, 6_4, 6_4) , __UpperCamelCase : List[str]=False )->Any:
_UpperCAmelCase = jnp.bfloataa if fpaa else jnp.floataa
_UpperCAmelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCamelCase , __UpperCamelCase ) ) , dtype=__UpperCamelCase )
return image
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Optional[Any]=False , __UpperCamelCase : Any="CompVis/stable-diffusion-v1-4" )->List[str]:
_UpperCAmelCase = jnp.bfloataa if fpaa else jnp.floataa
_UpperCAmelCase = '''bf16''' if fpaa else None
_UpperCAmelCase , _UpperCAmelCase = FlaxUNetaDConditionModel.from_pretrained(
__UpperCamelCase , subfolder='''unet''' , dtype=__UpperCamelCase , revision=__UpperCamelCase )
return model, params
def lowercase__ ( self : Any , __UpperCamelCase : List[Any]=0 , __UpperCamelCase : List[str]=(4, 7_7, 7_6_8) , __UpperCamelCase : int=False )->Tuple:
_UpperCAmelCase = jnp.bfloataa if fpaa else jnp.floataa
_UpperCAmelCase = jnp.array(load_hf_numpy(self.get_file_format(__UpperCamelCase , __UpperCamelCase ) ) , dtype=__UpperCamelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[8_3, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
[1_7, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
[8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
[3, 1_0_0_0, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
# fmt: on
] )
def lowercase__ ( self : Dict , __UpperCamelCase : Any , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] )->Union[str, Any]:
_UpperCAmelCase , _UpperCAmelCase = self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4''' , fpaa=__UpperCamelCase )
_UpperCAmelCase = self.get_latents(__UpperCamelCase , fpaa=__UpperCamelCase )
_UpperCAmelCase = self.get_encoder_hidden_states(__UpperCamelCase , fpaa=__UpperCamelCase )
_UpperCAmelCase = model.apply(
{'''params''': params} , __UpperCamelCase , jnp.array(__UpperCamelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCamelCase , ).sample
assert sample.shape == latents.shape
_UpperCAmelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
_UpperCAmelCase = jnp.array(__UpperCamelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[8_3, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
[1_7, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
[8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
[3, 1_0_0_0, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
# fmt: on
] )
def lowercase__ ( self : Tuple , __UpperCamelCase : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] )->List[str]:
_UpperCAmelCase , _UpperCAmelCase = self.get_unet_model(model_id='''stabilityai/stable-diffusion-2''' , fpaa=__UpperCamelCase )
_UpperCAmelCase = self.get_latents(__UpperCamelCase , shape=(4, 4, 9_6, 9_6) , fpaa=__UpperCamelCase )
_UpperCAmelCase = self.get_encoder_hidden_states(__UpperCamelCase , shape=(4, 7_7, 1_0_2_4) , fpaa=__UpperCamelCase )
_UpperCAmelCase = model.apply(
{'''params''': params} , __UpperCamelCase , jnp.array(__UpperCamelCase , dtype=jnp.intaa ) , encoder_hidden_states=__UpperCamelCase , ).sample
assert sample.shape == latents.shape
_UpperCAmelCase = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
_UpperCAmelCase = jnp.array(__UpperCamelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-2 )
| 260 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """audio-spectrogram-transformer"""
def __init__( self : int , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : int=1_2 , __UpperCamelCase : List[Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Dict=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1e-12 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=1_0 , __UpperCamelCase : Optional[int]=1_0 , __UpperCamelCase : str=1_0_2_4 , __UpperCamelCase : Optional[Any]=1_2_8 , **__UpperCamelCase : Any , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = patch_size
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = frequency_stride
_UpperCAmelCase = time_stride
_UpperCAmelCase = max_length
_UpperCAmelCase = num_mel_bins
| 260 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : List[Any] , __UpperCamelCase : Union[str, "sqlalchemy.sql.Selectable"] , __UpperCamelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __UpperCamelCase : Optional[Features] = None , __UpperCamelCase : str = None , __UpperCamelCase : bool = False , **__UpperCamelCase : Optional[int] , )->Any:
super().__init__(features=__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = Sql(
cache_dir=__UpperCamelCase , features=__UpperCamelCase , sql=__UpperCamelCase , con=__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : Dict )->str:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=__UpperCamelCase , download_mode=__UpperCamelCase , verification_mode=__UpperCamelCase , base_path=__UpperCamelCase , )
# Build dataset for splits
_UpperCAmelCase = self.builder.as_dataset(
split='''train''' , verification_mode=__UpperCamelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : Dataset , __UpperCamelCase : str , __UpperCamelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : List[str] , )->Optional[Any]:
if num_proc is not None and num_proc <= 0:
raise ValueError(F'num_proc {num_proc} must be an integer > 0.' )
_UpperCAmelCase = dataset
_UpperCAmelCase = name
_UpperCAmelCase = con
_UpperCAmelCase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_UpperCAmelCase = num_proc
_UpperCAmelCase = to_sql_kwargs
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = self.to_sql_kwargs.pop('''sql''' , __UpperCamelCase )
_UpperCAmelCase = self.to_sql_kwargs.pop('''con''' , __UpperCamelCase )
_UpperCAmelCase = self.to_sql_kwargs.pop('''index''' , __UpperCamelCase )
_UpperCAmelCase = self._write(index=__UpperCamelCase , **self.to_sql_kwargs )
return written
def lowercase__ ( self : int , __UpperCamelCase : Tuple )->List[Any]:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = args
_UpperCAmelCase = {**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
_UpperCAmelCase = query_table(
table=self.dataset.data , key=slice(__UpperCamelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
_UpperCAmelCase = batch.to_pandas()
_UpperCAmelCase = df.to_sql(self.name , self.con , index=__UpperCamelCase , **__UpperCamelCase )
return num_rows or len(__UpperCamelCase )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : int , **__UpperCamelCase : int )->int:
_UpperCAmelCase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
_UpperCAmelCase , _UpperCAmelCase = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , __UpperCamelCase , __UpperCamelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += num_rows
return written
| 260 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
_UpperCAmelCase = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
if month > 12:
year += 1
_UpperCAmelCase = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 260 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Any = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
__A : Any = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
__A : List[str] = {
"allenai/longformer-base-4096": 4096,
"allenai/longformer-large-4096": 4096,
"allenai/longformer-large-4096-finetuned-triviaqa": 4096,
"allenai/longformer-base-4096-extra.pos.embd.only": 4096,
"allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_UpperCAmelCase = bs[:]
_UpperCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
_UpperCAmelCase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCAmelCase = char
return pairs
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : Tuple , __UpperCamelCase : str , __UpperCamelCase : int , __UpperCamelCase : Tuple="replace" , __UpperCamelCase : int="<s>" , __UpperCamelCase : Optional[Any]="</s>" , __UpperCamelCase : Optional[Any]="</s>" , __UpperCamelCase : Dict="<s>" , __UpperCamelCase : List[str]="<unk>" , __UpperCamelCase : Tuple="<pad>" , __UpperCamelCase : Any="<mask>" , __UpperCamelCase : List[Any]=False , **__UpperCamelCase : Optional[Any] , )->str:
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else bos_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else eos_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else sep_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else cls_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else unk_token
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token
super().__init__(
errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , **__UpperCamelCase , )
with open(__UpperCamelCase , encoding='''utf-8''' ) as vocab_handle:
_UpperCAmelCase = json.load(__UpperCamelCase )
_UpperCAmelCase = {v: k for k, v in self.encoder.items()}
_UpperCAmelCase = errors # how to handle errors in decoding
_UpperCAmelCase = bytes_to_unicode()
_UpperCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(__UpperCamelCase , encoding='''utf-8''' ) as merges_handle:
_UpperCAmelCase = merges_handle.read().split('''\n''' )[1:-1]
_UpperCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_UpperCAmelCase = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
_UpperCAmelCase = {}
_UpperCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_UpperCAmelCase = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def lowercase__ ( self : Any )->List[Any]:
return len(self.encoder )
def lowercase__ ( self : str )->Optional[int]:
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase__ ( self : Any , __UpperCamelCase : str )->Optional[int]:
if token in self.cache:
return self.cache[token]
_UpperCAmelCase = tuple(__UpperCamelCase )
_UpperCAmelCase = get_pairs(__UpperCamelCase )
if not pairs:
return token
while True:
_UpperCAmelCase = min(__UpperCamelCase , key=lambda __UpperCamelCase : self.bpe_ranks.get(__UpperCamelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCAmelCase , _UpperCAmelCase = bigram
_UpperCAmelCase = []
_UpperCAmelCase = 0
while i < len(__UpperCamelCase ):
try:
_UpperCAmelCase = word.index(__UpperCamelCase , __UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCAmelCase = j
if word[i] == first and i < len(__UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCAmelCase = tuple(__UpperCamelCase )
_UpperCAmelCase = new_word
if len(__UpperCamelCase ) == 1:
break
else:
_UpperCAmelCase = get_pairs(__UpperCamelCase )
_UpperCAmelCase = ''' '''.join(__UpperCamelCase )
_UpperCAmelCase = word
return word
def lowercase__ ( self : List[Any] , __UpperCamelCase : Union[str, Any] )->Union[str, Any]:
_UpperCAmelCase = []
for token in re.findall(self.pat , __UpperCamelCase ):
_UpperCAmelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase ).split(''' ''' ) )
return bpe_tokens
def lowercase__ ( self : Tuple , __UpperCamelCase : Dict )->str:
return self.encoder.get(__UpperCamelCase , self.encoder.get(self.unk_token ) )
def lowercase__ ( self : List[str] , __UpperCamelCase : List[Any] )->Any:
return self.decoder.get(__UpperCamelCase )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : int )->Dict:
_UpperCAmelCase = ''''''.join(__UpperCamelCase )
_UpperCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None )->Tuple[str]:
if not os.path.isdir(__UpperCamelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
__UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_UpperCAmelCase = os.path.join(
__UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__UpperCamelCase , ensure_ascii=__UpperCamelCase ) + '''\n''' )
_UpperCAmelCase = 0
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __UpperCamelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
''' Please check that the tokenizer is not corrupted!''' )
_UpperCAmelCase = token_index
writer.write(''' '''.join(__UpperCamelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
def lowercase__ ( self : Dict , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None )->List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
_UpperCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase__ ( self : Tuple , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None , __UpperCamelCase : bool = False )->List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCamelCase )) + [1]
return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]
def lowercase__ ( self : str , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None )->List[int]:
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase__ ( self : int , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict=False , **__UpperCamelCase : Dict )->Union[str, Any]:
_UpperCAmelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase ) > 0 and not text[0].isspace()):
_UpperCAmelCase = ''' ''' + text
return (text, kwargs)
| 260 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [n]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if len(str(_SCREAMING_SNAKE_CASE ) ) > 3:
if not is_prime(int(str(_SCREAMING_SNAKE_CASE )[-3:] ) ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE )[:3] ) ):
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(_SCREAMING_SNAKE_CASE ) != count:
if validate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_truncated_nums(_SCREAMING_SNAKE_CASE )
if all(is_prime(_SCREAMING_SNAKE_CASE ) for i in list_nums ):
list_truncated_primes.append(_SCREAMING_SNAKE_CASE )
num += 2
return list_truncated_primes
def lowercase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 260 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : Tuple = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """wav2vec2"""
def __init__( self : Tuple , __UpperCamelCase : List[Any]=3_2 , __UpperCamelCase : int=7_6_8 , __UpperCamelCase : Optional[Any]=1_2 , __UpperCamelCase : Optional[int]=1_2 , __UpperCamelCase : Optional[Any]=3_0_7_2 , __UpperCamelCase : List[str]="gelu" , __UpperCamelCase : Dict=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : str=0.1 , __UpperCamelCase : Optional[Any]=0.0 , __UpperCamelCase : Any=0.0 , __UpperCamelCase : Dict=0.1 , __UpperCamelCase : Any=0.1 , __UpperCamelCase : List[str]=0.0_2 , __UpperCamelCase : Dict=1e-5 , __UpperCamelCase : int="group" , __UpperCamelCase : Union[str, Any]="gelu" , __UpperCamelCase : Optional[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , __UpperCamelCase : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase : Union[str, Any]=(1_0, 3, 3, 3, 3, 2, 2) , __UpperCamelCase : Tuple=False , __UpperCamelCase : str=1_2_8 , __UpperCamelCase : Dict=1_6 , __UpperCamelCase : Optional[Any]=False , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=0.0_5 , __UpperCamelCase : List[Any]=1_0 , __UpperCamelCase : str=2 , __UpperCamelCase : str=0.0 , __UpperCamelCase : Union[str, Any]=1_0 , __UpperCamelCase : Tuple=0 , __UpperCamelCase : Tuple=3_2_0 , __UpperCamelCase : int=2 , __UpperCamelCase : Dict=0.1 , __UpperCamelCase : Tuple=1_0_0 , __UpperCamelCase : Tuple=2_5_6 , __UpperCamelCase : List[Any]=2_5_6 , __UpperCamelCase : int=0.1 , __UpperCamelCase : Dict="sum" , __UpperCamelCase : Tuple=False , __UpperCamelCase : Tuple=False , __UpperCamelCase : int=2_5_6 , __UpperCamelCase : Optional[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , __UpperCamelCase : Any=(5, 3, 3, 1, 1) , __UpperCamelCase : Dict=(1, 2, 3, 1, 1) , __UpperCamelCase : Any=5_1_2 , __UpperCamelCase : List[str]=0 , __UpperCamelCase : int=1 , __UpperCamelCase : Optional[int]=2 , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : int=3 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : Any=3 , __UpperCamelCase : Tuple=None , __UpperCamelCase : List[Any]=None , **__UpperCamelCase : Optional[Any] , )->int:
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = feat_extract_norm
_UpperCAmelCase = feat_extract_activation
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = conv_bias
_UpperCAmelCase = num_conv_pos_embeddings
_UpperCAmelCase = num_conv_pos_embedding_groups
_UpperCAmelCase = len(self.conv_dim )
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = feat_proj_dropout
_UpperCAmelCase = final_dropout
_UpperCAmelCase = layerdrop
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = vocab_size
_UpperCAmelCase = do_stable_layer_norm
_UpperCAmelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCAmelCase = apply_spec_augment
_UpperCAmelCase = mask_time_prob
_UpperCAmelCase = mask_time_length
_UpperCAmelCase = mask_time_min_masks
_UpperCAmelCase = mask_feature_prob
_UpperCAmelCase = mask_feature_length
_UpperCAmelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_UpperCAmelCase = num_codevectors_per_group
_UpperCAmelCase = num_codevector_groups
_UpperCAmelCase = contrastive_logits_temperature
_UpperCAmelCase = feat_quantizer_dropout
_UpperCAmelCase = num_negatives
_UpperCAmelCase = codevector_dim
_UpperCAmelCase = proj_codevector_dim
_UpperCAmelCase = diversity_loss_weight
# ctc loss
_UpperCAmelCase = ctc_loss_reduction
_UpperCAmelCase = ctc_zero_infinity
# adapter
_UpperCAmelCase = add_adapter
_UpperCAmelCase = adapter_kernel_size
_UpperCAmelCase = adapter_stride
_UpperCAmelCase = num_adapter_layers
_UpperCAmelCase = output_hidden_size or hidden_size
_UpperCAmelCase = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCAmelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = list(__UpperCamelCase )
_UpperCAmelCase = xvector_output_dim
@property
def lowercase__ ( self : Tuple )->List[str]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : str = sys.version_info >= (3, 10)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : Tuple )->Optional[int]:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : List[str] )->List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def lowercase__ ( self : int )->str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : int , __UpperCamelCase : argparse.ArgumentParser , __UpperCamelCase : argparse.ArgumentParser )->Dict:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , __UpperCamelCase ) and yy.get('''choices''' , __UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](__UpperCamelCase ) , yy['''type'''](__UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--bar''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--baz''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--flag''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(__UpperCamelCase , look_for_args_file=__UpperCamelCase )
self.assertFalse(example.flag )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=4_2 , type=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=__UpperCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 4_2] , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : List[str] )->List[str]:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 4_2) , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
def lowercase__ ( self : int )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=__UpperCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--bar''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , bar=__UpperCamelCase , baz=__UpperCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo=1_2 , bar=3.1_4 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--required_str''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
_UpperCAmelCase = parser.parse_dict(__UpperCamelCase )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 4_2,
}
self.assertRaises(__UpperCamelCase , parser.parse_dict , __UpperCamelCase , allow_extra_keys=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_json''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_yaml''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 260 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = MobileBertConfig.from_json_file(_SCREAMING_SNAKE_CASE )
print(f'Building PyTorch model from configuration: {config}' )
_UpperCAmelCase = MobileBertForPreTraining(_SCREAMING_SNAKE_CASE )
# Load weights from tf checkpoint
_UpperCAmelCase = load_tf_weights_in_mobilebert(_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__":
__A : List[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(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__A : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase = True
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_UpperCAmelCase = True
if a[i].islower():
_UpperCAmelCase = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[Any] = {
"configuration_xlm_roberta": [
"XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaConfig",
"XLMRobertaOnnxConfig",
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = ["XLMRobertaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ["XLMRobertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = [
"XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMRobertaForCausalLM",
"XLMRobertaForMaskedLM",
"XLMRobertaForMultipleChoice",
"XLMRobertaForQuestionAnswering",
"XLMRobertaForSequenceClassification",
"XLMRobertaForTokenClassification",
"XLMRobertaModel",
"XLMRobertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
"TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForMultipleChoice",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForTokenClassification",
"TFXLMRobertaModel",
"TFXLMRobertaPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
"FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxXLMRobertaForMaskedLM",
"FlaxXLMRobertaForCausalLM",
"FlaxXLMRobertaForMultipleChoice",
"FlaxXLMRobertaForQuestionAnswering",
"FlaxXLMRobertaForSequenceClassification",
"FlaxXLMRobertaForTokenClassification",
"FlaxXLMRobertaModel",
"FlaxXLMRobertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
quick_sort_random(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_SCREAMING_SNAKE_CASE , pivot_index + 1 , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCAmelCase = [int(_SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int | None = None , _SCREAMING_SNAKE_CASE : int | None = None ):
'''simple docstring'''
if start is None:
_UpperCAmelCase = 0
if end is None:
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ) - 1
if start >= end:
return
_UpperCAmelCase = (start + end) // 2
slowsort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
slowsort(_SCREAMING_SNAKE_CASE , mid + 1 , _SCREAMING_SNAKE_CASE )
if sequence[end] < sequence[mid]:
_UpperCAmelCase , _UpperCAmelCase = sequence[mid], sequence[end]
slowsort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"
__A : List[str] = "\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to 'cuda' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n >>> results = perplexity.compute(model_id='gpt2',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 78.22\n >>> print(round(results[\"perplexities\"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = datasets.load_dataset(\"wikitext\",\n ... \"wikitext-2-raw-v1\",\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!='']\n >>> results = perplexity.compute(model_id='gpt2',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 60.35\n >>> print(round(results[\"perplexities\"][0], 2))\n 81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int = 1_6 , __UpperCamelCase : bool = True , __UpperCamelCase : List[Any]=None )->Any:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = '''cuda'''
else:
_UpperCAmelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors='''pt''' , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_UpperCAmelCase = encodings['''input_ids''']
_UpperCAmelCase = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 260 | 1 |
"""simple docstring"""
import operator as op
__A : Any = "scaler.pt"
__A : Union[str, Any] = "pytorch_model"
__A : Dict = "random_states"
__A : List[Any] = "optimizer"
__A : List[str] = "scheduler"
__A : Union[str, Any] = "pytorch_model.bin"
__A : List[Any] = "pytorch_model.bin.index.json"
__A : List[Any] = "model.safetensors"
__A : int = "model.safetensors.index.json"
__A : Optional[int] = "1.10.2"
__A : Dict = "py38"
__A : str = "4.17.0"
__A : Union[str, Any] = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__A : List[Any] = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__A : Union[str, Any] = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__A : Tuple = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__A : List[str] = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__A : List[Any] = "2.0.1"
__A : Dict = ["pdsh", "standard", "openmpi", "mvapich"]
__A : Any = ["default", "reduce-overhead", "max-autotune"]
__A : Any = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
__A : Optional[int] = [
"nnodes",
"nproc_per_node",
"rdzv_backend",
"rdzv_endpoint",
"rdzv_id",
"rdzv_conf",
"standalone",
"max_restarts",
"monitor_interval",
"start_method",
"role",
"module",
"m",
"no_python",
"run_path",
"log_dir",
"r",
"redirects",
"t",
"tee",
"node_rank",
"master_addr",
"master_port",
]
__A : str = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__A : Any = ["DEEPSPEED", "MULTI_XPU", "FSDP"]
| 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.getbasetemp() / '''cache'''
_UpperCAmelCase = test_hf_cache_home / '''datasets'''
_UpperCAmelCase = test_hf_cache_home / '''metrics'''
_UpperCAmelCase = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE , scope='''session''' )
def lowercase ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _SCREAMING_SNAKE_CASE )
| 260 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
__A : Any = logging.get_logger(__name__)
__A : Optional[int] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__A : Any = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
__A : Union[str, Any] = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
__A : Optional[int] = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = RealmTokenizer
def __init__( self : Union[str, Any] , __UpperCamelCase : Tuple=None , __UpperCamelCase : Tuple=None , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int="[UNK]" , __UpperCamelCase : List[str]="[SEP]" , __UpperCamelCase : Optional[int]="[PAD]" , __UpperCamelCase : Any="[CLS]" , __UpperCamelCase : str="[MASK]" , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Any=None , **__UpperCamelCase : Dict , )->Any:
super().__init__(
__UpperCamelCase , tokenizer_file=__UpperCamelCase , do_lower_case=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , pad_token=__UpperCamelCase , cls_token=__UpperCamelCase , mask_token=__UpperCamelCase , tokenize_chinese_chars=__UpperCamelCase , strip_accents=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCamelCase ) != tokenize_chinese_chars
):
_UpperCAmelCase = getattr(__UpperCamelCase , normalizer_state.pop('''type''' ) )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = strip_accents
_UpperCAmelCase = tokenize_chinese_chars
_UpperCAmelCase = normalizer_class(**__UpperCamelCase )
_UpperCAmelCase = do_lower_case
def lowercase__ ( self : Optional[int] , __UpperCamelCase : List[Any] , **__UpperCamelCase : Union[str, Any] )->Tuple:
_UpperCAmelCase = PaddingStrategy.MAX_LENGTH
_UpperCAmelCase = text
_UpperCAmelCase = kwargs.pop('''text_pair''' , __UpperCamelCase )
_UpperCAmelCase = kwargs.pop('''return_tensors''' , __UpperCamelCase )
_UpperCAmelCase = {
'''input_ids''': [],
'''attention_mask''': [],
'''token_type_ids''': [],
}
for idx, candidate_text in enumerate(__UpperCamelCase ):
if batch_text_pair is not None:
_UpperCAmelCase = batch_text_pair[idx]
else:
_UpperCAmelCase = None
_UpperCAmelCase = super().__call__(__UpperCamelCase , __UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = encoded_candidates.get('''input_ids''' )
_UpperCAmelCase = encoded_candidates.get('''attention_mask''' )
_UpperCAmelCase = encoded_candidates.get('''token_type_ids''' )
if encoded_input_ids is not None:
output_data["input_ids"].append(__UpperCamelCase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(__UpperCamelCase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(__UpperCamelCase )
_UpperCAmelCase = {key: item for key, item in output_data.items() if len(__UpperCamelCase ) != 0}
return BatchEncoding(__UpperCamelCase , tensor_type=__UpperCamelCase )
def lowercase__ ( self : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : Tuple=None )->Optional[Any]:
_UpperCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : List[str] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None )->List[int]:
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : List[str] , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None )->Tuple[str]:
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase , _UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__A : Dict = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
"Pix2StructVisionConfig",
],
"processing_pix2struct": ["Pix2StructProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = ["Pix2StructImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = [
"PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Pix2StructPreTrainedModel",
"Pix2StructForConditionalGeneration",
"Pix2StructVisionModel",
"Pix2StructTextModel",
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 260 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
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
__A : int = logging.getLogger(__name__)
# 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>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
UpperCamelCase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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()
# 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_audio_classification''' , _SCREAMING_SNAKE_CASE , _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()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_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}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.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 , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class _a ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any]=1_3 , __UpperCamelCase : List[str]=7 , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Tuple=True , __UpperCamelCase : Optional[int]=9_9 , __UpperCamelCase : List[Any]=3_2 , __UpperCamelCase : Any=5 , __UpperCamelCase : List[Any]=4 , __UpperCamelCase : Dict=3_7 , __UpperCamelCase : int="gelu" , __UpperCamelCase : Optional[Any]=0.1 , __UpperCamelCase : Optional[Any]=0.1 , __UpperCamelCase : List[str]=5_1_2 , __UpperCamelCase : Optional[int]=1_6 , __UpperCamelCase : Union[str, Any]=2 , __UpperCamelCase : Union[str, Any]=0.0_2 , __UpperCamelCase : Dict=4 , )->Optional[Any]:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_choices
def lowercase__ ( self : Dict )->Tuple:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=__UpperCamelCase , )
return config, input_ids, attention_mask
def lowercase__ ( self : Optional[Any] )->Tuple:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase__ ( self : str )->Tuple:
_UpperCAmelCase = FlaxDistilBertModelTester(self )
@slow
def lowercase__ ( self : Union[str, Any] )->List[str]:
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('''distilbert-base-uncased''' )
_UpperCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(__UpperCamelCase )
@require_flax
class _a ( unittest.TestCase):
"""simple docstring"""
@slow
def lowercase__ ( self : str )->List[str]:
_UpperCAmelCase = FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''' )
_UpperCAmelCase = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_UpperCAmelCase = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
_UpperCAmelCase = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , __UpperCamelCase )
_UpperCAmelCase = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __UpperCamelCase , atol=1e-4 ) )
| 260 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DPMSolverSinglestepScheduler,)
UpperCamelCase__ = (("""num_inference_steps""", 25),)
def lowercase__ ( self : Tuple , **__UpperCamelCase : Tuple )->Any:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf''' ),
'''variance_type''': None,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : Dict , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Any )->Union[str, Any]:
pass
def lowercase__ ( self : str , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : int , __UpperCamelCase : List[str]=None , **__UpperCamelCase : Optional[int] )->List[Any]:
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def lowercase__ ( self : List[Any] )->Dict:
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = 5_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_5_7_4 ) < 1e-3
def lowercase__ ( self : Dict )->Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : str )->Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->int:
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , algorithm_type='''dpmsolver++''' , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def lowercase__ ( self : str )->str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict )->List[str]:
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def lowercase__ ( self : Dict )->str:
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowercase__ ( self : List[str] )->int:
self.check_over_configs(variance_type=__UpperCamelCase )
self.check_over_configs(variance_type='''learned_range''' )
def lowercase__ ( self : List[str] )->Union[str, Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : List[str] )->List[str]:
_UpperCAmelCase = self.full_loop(use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_2_4_8 ) < 1e-3
def lowercase__ ( self : int )->List[Any]:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_4_5_3 ) < 1e-3
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_6_4_9 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 260 | 1 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
quick_sort_random(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_SCREAMING_SNAKE_CASE , pivot_index + 1 , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCAmelCase = [int(_SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : float )->float:
return 0.0
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_UpperCAmelCase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.abs(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = 20 * np.logaa(_SCREAMING_SNAKE_CASE )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
_UpperCAmelCase = get_bounds(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(_SCREAMING_SNAKE_CASE )
plt.show()
def lowercase ( _SCREAMING_SNAKE_CASE : FilterType , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 512
_UpperCAmelCase = [1] + [0] * (size - 1)
_UpperCAmelCase = [filter_type.process(_SCREAMING_SNAKE_CASE ) for item in inputs]
_UpperCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCAmelCase = np.angle(np.fft.fft(_SCREAMING_SNAKE_CASE ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(_SCREAMING_SNAKE_CASE , -2 * pi ) )
plt.show()
| 260 | 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 lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def lowercase ( ):
'''simple docstring'''
with parallel_backend('''spark''' ):
assert ParallelBackendConfig.backend_name == "spark"
_UpperCAmelCase = [1, 2, 3]
with pytest.raises(_SCREAMING_SNAKE_CASE ):
with parallel_backend('''unsupported backend''' ):
map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=2 )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
with parallel_backend('''unsupported backend''' ):
map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc''' , [2, -1] )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
_UpperCAmelCase = [1, 2]
_UpperCAmelCase = {'''a''': 1, '''b''': 2}
_UpperCAmelCase = {'''a''': [1, 2], '''b''': [3, 4]}
_UpperCAmelCase = {'''a''': {'''1''': 1}, '''b''': 2}
_UpperCAmelCase = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
_UpperCAmelCase = [2, 3]
_UpperCAmelCase = {'''a''': 2, '''b''': 3}
_UpperCAmelCase = {'''a''': [2, 3], '''b''': [4, 5]}
_UpperCAmelCase = {'''a''': {'''1''': 2}, '''b''': 3}
_UpperCAmelCase = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
with parallel_backend('''spark''' ):
assert map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) == expected_map_nested_sa
assert map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) == expected_map_nested_sa
assert map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) == expected_map_nested_sa
assert map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) == expected_map_nested_sa
assert map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) == expected_map_nested_sa
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """camembert"""
def __init__( self : List[str] , __UpperCamelCase : Union[str, Any]=3_0_5_2_2 , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : Optional[int]=1_2 , __UpperCamelCase : Union[str, Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Dict="gelu" , __UpperCamelCase : Tuple=0.1 , __UpperCamelCase : int=0.1 , __UpperCamelCase : int=5_1_2 , __UpperCamelCase : Dict=2 , __UpperCamelCase : int=0.0_2 , __UpperCamelCase : int=1e-12 , __UpperCamelCase : Optional[Any]=1 , __UpperCamelCase : Dict=0 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : Any="absolute" , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : str=None , **__UpperCamelCase : Optional[Any] , )->str:
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class _a ( lowerCAmelCase):
"""simple docstring"""
@property
def lowercase__ ( self : int )->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 260 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = int(number**0.5 )
return number == sq * sq
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
_UpperCAmelCase = x_den * y_den * z_den
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
top //= hcf
bottom //= hcf
return top, bottom
def lowercase ( _SCREAMING_SNAKE_CASE : int = 35 ):
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = 42
_UpperCAmelCase = Fraction(0 )
_UpperCAmelCase = 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
_UpperCAmelCase = x_num * y_den + x_den * y_num
_UpperCAmelCase = x_den * y_den
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=2
_UpperCAmelCase = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
_UpperCAmelCase = x_den * x_den * y_den * y_den
if is_sq(_SCREAMING_SNAKE_CASE ) and is_sq(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=-1
_UpperCAmelCase = x_num * y_num
_UpperCAmelCase = x_den * y_num + x_num * y_den
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=2
_UpperCAmelCase = x_num * x_num * y_num * y_num
_UpperCAmelCase = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_SCREAMING_SNAKE_CASE ) and is_sq(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
for num, den in unique_s:
total += Fraction(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 260 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
"sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """poolformer"""
def __init__( self : List[str] , __UpperCamelCase : int=3 , __UpperCamelCase : List[Any]=1_6 , __UpperCamelCase : str=1_6 , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : int=4.0 , __UpperCamelCase : str=[2, 2, 6, 2] , __UpperCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __UpperCamelCase : int=[7, 3, 3, 3] , __UpperCamelCase : str=[4, 2, 2, 2] , __UpperCamelCase : Union[str, Any]=[2, 1, 1, 1] , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=0.0 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : List[str]=True , __UpperCamelCase : Union[str, Any]=1e-5 , __UpperCamelCase : str=0.0_2 , **__UpperCamelCase : List[Any] , )->Dict:
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = stride
_UpperCAmelCase = padding
_UpperCAmelCase = pool_size
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = depths
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_layer_scale
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = initializer_range
super().__init__(**__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = version.parse("""1.11""")
@property
def lowercase__ ( self : Union[str, Any] )->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase__ ( self : Tuple )->float:
return 2e-3
| 260 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A : List[Any] = logging.getLogger(__name__)
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(
description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' )
parser.add_argument(
'''--dataset_name''' , type=_SCREAMING_SNAKE_CASE , default='''wikitext''' , help='''Name of the training. Explore datasets at: hf.co/datasets.''' , )
parser.add_argument(
'''--dataset_config''' , type=_SCREAMING_SNAKE_CASE , default='''wikitext-103-raw-v1''' , help='''Configuration name of the dataset.''' )
parser.add_argument(
'''--tokenizer_name_or_path''' , type=_SCREAMING_SNAKE_CASE , default='''sayakpaul/unigram-tokenizer-wikitext''' , help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' , )
parser.add_argument(
'''--shard_size''' , type=_SCREAMING_SNAKE_CASE , default=1000 , help='''Number of entries to go in a single shard.''' , )
parser.add_argument('''--split''' , type=_SCREAMING_SNAKE_CASE , default='''train''' , choices=['''train''', '''test''', '''validation'''] )
parser.add_argument(
'''--limit''' , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , help='''Limit the number of shards (used for debugging).''' , )
parser.add_argument(
'''--max_length''' , type=_SCREAMING_SNAKE_CASE , default=512 , help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
''' sequence length that is a multiple of 8.''' , )
parser.add_argument(
'''--output_dir''' , default='''tf-tpu''' , type=_SCREAMING_SNAKE_CASE , help='''Output directory where the TFRecord shards will be saved. If the'''
''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
''' shards will be directly saved to a Google Cloud Storage bucket.''' , )
_UpperCAmelCase = parser.parse_args()
return args
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
def fn(_SCREAMING_SNAKE_CASE : List[Any] ):
return tokenizer(examples['''text'''] )
return fn
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = []
for i in range(len(tokenized_data['''input_ids'''] ) ):
_UpperCAmelCase = {
'''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ),
'''attention_mask''': tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ),
}
_UpperCAmelCase = tf.train.Features(feature=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tf.train.Example(features=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = example.SerializeToString()
records.append(_SCREAMING_SNAKE_CASE )
return records
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
_UpperCAmelCase = min(len(_SCREAMING_SNAKE_CASE ) , args.limit )
_UpperCAmelCase = dataset.select(range(_SCREAMING_SNAKE_CASE ) )
print(f'Limiting the dataset to {args.limit} entries.' )
_UpperCAmelCase = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
_UpperCAmelCase = os.path.join(args.output_dir , args.split )
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
_UpperCAmelCase = tokenize_function(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = dataset.map(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , num_proc=4 , remove_columns=['''text'''] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(_SCREAMING_SNAKE_CASE : str ):
# Concatenate all texts.
_UpperCAmelCase = {k: sum(examples[k] , [] ) for k in examples.keys()}
_UpperCAmelCase = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
_UpperCAmelCase = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
_UpperCAmelCase = {
k: [t[i : i + args.max_length] for i in range(0 , _SCREAMING_SNAKE_CASE , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
_UpperCAmelCase = dataset_tokenized.map(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , batch_size=1000 , num_proc=4 )
_UpperCAmelCase = 0
_UpperCAmelCase = 0
for shard in range(0 , len(_SCREAMING_SNAKE_CASE ) , args.shard_size ):
_UpperCAmelCase = grouped_dataset[shard : shard + args.shard_size]
_UpperCAmelCase = len(dataset_snapshot['''input_ids'''] )
_UpperCAmelCase = os.path.join(_SCREAMING_SNAKE_CASE , f'dataset-{shard_count}-{records_containing}.tfrecord' )
_UpperCAmelCase = get_serialized_examples(_SCREAMING_SNAKE_CASE )
with tf.io.TFRecordWriter(_SCREAMING_SNAKE_CASE ) as out_file:
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
_UpperCAmelCase = serialized_examples[i]
out_file.write(_SCREAMING_SNAKE_CASE )
print('''Wrote file {} containing {} records'''.format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
shard_count += 1
total_records += records_containing
with open(f'split-{args.split}-records-count.txt' , '''w''' ) as f:
print(f'Total {args.split} records: {total_records}' , file=_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = parse_args()
main(args)
| 260 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Union[str, Any] = 16
__A : Optional[Any] = 32
def lowercase ( _SCREAMING_SNAKE_CASE : Accelerator , _SCREAMING_SNAKE_CASE : int = 16 ):
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_UpperCAmelCase = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_SCREAMING_SNAKE_CASE : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase = datasets.map(
_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_SCREAMING_SNAKE_CASE : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase = 8
else:
_UpperCAmelCase = None
return tokenizer.pad(
_SCREAMING_SNAKE_CASE , padding='''longest''' , max_length=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : Optional[int] = mocked_dataloaders # noqa: F811
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _SCREAMING_SNAKE_CASE ) == "1":
_UpperCAmelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
_UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config['''lr''']
_UpperCAmelCase = int(config['''num_epochs'''] )
_UpperCAmelCase = int(config['''seed'''] )
_UpperCAmelCase = int(config['''batch_size'''] )
set_seed(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = get_dataloaders(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = AdamW(params=model.parameters() , lr=_SCREAMING_SNAKE_CASE )
# Instantiate scheduler
_UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=_SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(_SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_UpperCAmelCase = os.path.split(_SCREAMING_SNAKE_CASE )[-1].split('''.''' )[0]
accelerator.init_trackers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(_SCREAMING_SNAKE_CASE ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_UpperCAmelCase = 0
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(_SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_SCREAMING_SNAKE_CASE , references=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , _SCREAMING_SNAKE_CASE )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(_SCREAMING_SNAKE_CASE ),
'''epoch''': epoch,
} , step=_SCREAMING_SNAKE_CASE , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=_SCREAMING_SNAKE_CASE , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(_SCREAMING_SNAKE_CASE ).count('''1''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCAmelCase = 0
count += depth_first_search(_SCREAMING_SNAKE_CASE , row + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , row - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col + 1 , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col - 1 , _SCREAMING_SNAKE_CASE )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
while a != 0:
_UpperCAmelCase , _UpperCAmelCase = b % a, a
return b
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) != 1:
_UpperCAmelCase = f'mod inverse of {a!r} and {m!r} does not exist'
raise ValueError(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1, 0, a
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0, 1, m
while va != 0:
_UpperCAmelCase = ua // va
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 260 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
_UpperCAmelCase = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
_UpperCAmelCase = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
_UpperCAmelCase = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
_UpperCAmelCase = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
_UpperCAmelCase = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
_UpperCAmelCase = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
_UpperCAmelCase = key.replace('''image_encoder.module''' , '''flava.image_model''' )
_UpperCAmelCase = key.replace('''text_encoder.module''' , '''flava.text_model''' )
_UpperCAmelCase = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
_UpperCAmelCase = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
_UpperCAmelCase = key.replace('''text_projection''' , '''flava.text_projection''' )
_UpperCAmelCase = key.replace('''image_projection''' , '''flava.image_projection''' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
_UpperCAmelCase = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__A : Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 260 | 1 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Dict:
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 5_0_0
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowercase__ ( self : Optional[int] )->List[str]:
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 5_0_0
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = GPTaTokenizerFast.from_pretrained('''gpt2''' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = GPTaTokenizerFast.from_pretrained('''gpt2''' )
# This check we did call the fake head request
mock_head.assert_called()
def lowercase__ ( self : List[Any] )->Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
try:
_UpperCAmelCase = tempfile.mktemp()
with open(__UpperCamelCase , '''wb''' ) as f:
http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''' , __UpperCamelCase )
_UpperCAmelCase = AlbertTokenizer.from_pretrained(__UpperCamelCase )
finally:
os.remove(__UpperCamelCase )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('''tokenizer.json''' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('''tokenizer.json''' , '''wb''' ) as f:
http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json''' , __UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 1_0_0_0 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('''tokenizer.json''' )
def lowercase__ ( self : List[Any] )->Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''' )
@is_staging_test
class _a ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
@classmethod
def lowercase__ ( cls : Any )->Optional[int]:
_UpperCAmelCase = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def lowercase__ ( cls : Dict )->int:
try:
delete_repo(token=cls._token , repo_id='''test-tokenizer''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-tokenizer-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-tokenizer''' )
except HTTPError:
pass
def lowercase__ ( self : List[Any] )->List[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''vocab.txt''' )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens] ) )
_UpperCAmelCase = BertTokenizer(__UpperCamelCase )
tokenizer.push_to_hub('''test-tokenizer''' , use_auth_token=self._token )
_UpperCAmelCase = BertTokenizer.from_pretrained(F'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='''test-tokenizer''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__UpperCamelCase , repo_id='''test-tokenizer''' , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = BertTokenizer.from_pretrained(F'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def lowercase__ ( self : Optional[int] )->Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''vocab.txt''' )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens] ) )
_UpperCAmelCase = BertTokenizer(__UpperCamelCase )
tokenizer.push_to_hub('''valid_org/test-tokenizer-org''' , use_auth_token=self._token )
_UpperCAmelCase = BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org''' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-tokenizer-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
__UpperCamelCase , repo_id='''valid_org/test-tokenizer-org''' , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org''' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def lowercase__ ( self : List[Any] )->str:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''vocab.txt''' )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens] ) )
_UpperCAmelCase = CustomTokenizer(__UpperCamelCase )
# No fast custom tokenizer
tokenizer.push_to_hub('''test-dynamic-tokenizer''' , use_auth_token=self._token )
_UpperCAmelCase = AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , '''CustomTokenizer''' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''vocab.txt''' )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens] ) )
_UpperCAmelCase = BertTokenizerFast.from_pretrained(__UpperCamelCase )
bert_tokenizer.save_pretrained(__UpperCamelCase )
_UpperCAmelCase = CustomTokenizerFast.from_pretrained(__UpperCamelCase )
tokenizer.push_to_hub('''test-dynamic-tokenizer''' , use_auth_token=self._token )
_UpperCAmelCase = AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , '''CustomTokenizerFast''' )
_UpperCAmelCase = AutoTokenizer.from_pretrained(
F'{USER}/test-dynamic-tokenizer' , use_fast=__UpperCamelCase , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , '''CustomTokenizer''' )
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[str] )->str:
_UpperCAmelCase = Trie()
trie.add('''Hello 友達''' )
self.assertEqual(trie.data , {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}} )
trie.add('''Hello''' )
trie.data
self.assertEqual(trie.data , {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}} )
def lowercase__ ( self : Any )->Optional[Any]:
_UpperCAmelCase = Trie()
self.assertEqual(trie.split('''[CLS] This is a extra_id_100''' ) , ['''[CLS] This is a extra_id_100'''] )
trie.add('''[CLS]''' )
trie.add('''extra_id_1''' )
trie.add('''extra_id_100''' )
self.assertEqual(trie.split('''[CLS] This is a extra_id_100''' ) , ['''[CLS]''', ''' This is a ''', '''extra_id_100'''] )
def lowercase__ ( self : Optional[int] )->List[str]:
_UpperCAmelCase = Trie()
trie.add('''A''' )
self.assertEqual(trie.split('''ABC''' ) , ['''A''', '''BC'''] )
self.assertEqual(trie.split('''BCA''' ) , ['''BC''', '''A'''] )
def lowercase__ ( self : Any )->Tuple:
_UpperCAmelCase = Trie()
trie.add('''TOKEN]''' )
trie.add('''[SPECIAL_TOKEN]''' )
self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]''' ) , ['''This is something ''', '''[SPECIAL_TOKEN]'''] )
def lowercase__ ( self : Union[str, Any] )->str:
_UpperCAmelCase = Trie()
trie.add('''A''' )
trie.add('''P''' )
trie.add('''[SPECIAL_TOKEN]''' )
self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]''' ) , ['''This is something ''', '''[SPECIAL_TOKEN]'''] )
def lowercase__ ( self : List[str] )->Optional[Any]:
_UpperCAmelCase = Trie()
trie.add('''AB''' )
trie.add('''B''' )
trie.add('''C''' )
self.assertEqual(trie.split('''ABC''' ) , ['''AB''', '''C'''] )
def lowercase__ ( self : List[Any] )->Union[str, Any]:
_UpperCAmelCase = Trie()
trie.add('''ABC''' )
trie.add('''B''' )
trie.add('''CD''' )
self.assertEqual(trie.split('''ABCD''' ) , ['''ABC''', '''D'''] )
def lowercase__ ( self : List[str] )->int:
# Even if the offsets are wrong, we necessarily output correct string
# parts.
_UpperCAmelCase = Trie()
_UpperCAmelCase = trie.cut_text('''ABC''' , [0, 0, 2, 1, 2, 3] )
self.assertEqual(__UpperCamelCase , ['''AB''', '''C'''] )
| 260 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowercase ( _SCREAMING_SNAKE_CASE : Features ):
'''simple docstring'''
_UpperCAmelCase = np.inf
def set_batch_size(_SCREAMING_SNAKE_CASE : FeatureType ) -> None:
nonlocal batch_size
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and feature.dtype == "binary":
_UpperCAmelCase = min(_SCREAMING_SNAKE_CASE , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return None if batch_size is np.inf else batch_size
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCamelCase : NestedDataStructureLike[PathLike] , __UpperCamelCase : Optional[NamedSplit] = None , __UpperCamelCase : Optional[Features] = None , __UpperCamelCase : str = None , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : int , )->Union[str, Any]:
super().__init__(
__UpperCamelCase , split=__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase , streaming=__UpperCamelCase , num_proc=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = path_or_paths if isinstance(__UpperCamelCase , __UpperCamelCase ) else {self.split: path_or_paths}
_UpperCAmelCase = _PACKAGED_DATASETS_MODULES['''parquet'''][1]
_UpperCAmelCase = Parquet(
cache_dir=__UpperCamelCase , data_files=__UpperCamelCase , features=__UpperCamelCase , hash=__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : Union[str, Any] )->Dict:
# Build iterable dataset
if self.streaming:
_UpperCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=__UpperCamelCase , download_mode=__UpperCamelCase , verification_mode=__UpperCamelCase , base_path=__UpperCamelCase , num_proc=self.num_proc , )
_UpperCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=__UpperCamelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , __UpperCamelCase : Dataset , __UpperCamelCase : Union[PathLike, BinaryIO] , __UpperCamelCase : Optional[int] = None , **__UpperCamelCase : Tuple , )->Optional[int]:
_UpperCAmelCase = dataset
_UpperCAmelCase = path_or_buf
_UpperCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_UpperCAmelCase = parquet_writer_kwargs
def lowercase__ ( self : Optional[int] )->int:
_UpperCAmelCase = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , '''wb+''' ) as buffer:
_UpperCAmelCase = self._write(file_obj=__UpperCamelCase , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
else:
_UpperCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=__UpperCamelCase , **self.parquet_writer_kwargs )
return written
def lowercase__ ( self : int , __UpperCamelCase : BinaryIO , __UpperCamelCase : int , **__UpperCamelCase : int )->int:
_UpperCAmelCase = 0
_UpperCAmelCase = parquet_writer_kwargs.pop('''path_or_buf''' , __UpperCamelCase )
_UpperCAmelCase = self.dataset.features.arrow_schema
_UpperCAmelCase = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase , **__UpperCamelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , __UpperCamelCase ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating parquet from Arrow format''' , ):
_UpperCAmelCase = query_table(
table=self.dataset._data , key=slice(__UpperCamelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(__UpperCamelCase )
written += batch.nbytes
writer.close()
return written
| 260 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
_UpperCAmelCase = parser.add_subparsers(help='''diffusers-cli command helpers''' )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
_UpperCAmelCase = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , '''func''' ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE ):
if char == separator:
split_words.append(string[last_index:index] )
_UpperCAmelCase = index + 1
elif index + 1 == len(_SCREAMING_SNAKE_CASE ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 1 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
__A : Optional[Any] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
__A : Dict = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
__A : str = BeautifulSoup(res.text, "html.parser")
__A : str = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f'''https://google.com{link.get("href")}''')
| 260 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = args.pruning_method
_UpperCAmelCase = args.threshold
_UpperCAmelCase = args.model_name_or_path.rstrip('''/''' )
_UpperCAmelCase = args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
_UpperCAmelCase = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
_UpperCAmelCase = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
elif "bias" in name:
_UpperCAmelCase = tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
_UpperCAmelCase = MagnitudeBinarizer.apply(inputs=_SCREAMING_SNAKE_CASE , threshold=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = TopKBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase = ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
_UpperCAmelCase = name[:-6]
_UpperCAmelCase = model[f'{prefix_}mask_scores']
_UpperCAmelCase , _UpperCAmelCase = -0.1, 1.1
_UpperCAmelCase = torch.sigmoid(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = s * (r - l) + l
_UpperCAmelCase = s_bar.clamp(min=0.0 , max=1.0 )
_UpperCAmelCase = tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
_UpperCAmelCase = os.path.join(
os.path.dirname(_SCREAMING_SNAKE_CASE ) , f'bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE )}' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
shutil.copytree(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f'\nCreated folder {target_model_path}' )
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__A : Optional[int] = parser.parse_args()
main(args)
| 260 | 1 |
"""simple docstring"""
__A : List[Any] = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : int = frozenset(["prompt", "negative_prompt"])
__A : List[Any] = frozenset([])
__A : Optional[Any] = frozenset(["image"])
__A : Dict = frozenset(
[
"image",
"height",
"width",
"guidance_scale",
]
)
__A : Optional[Any] = frozenset(["image"])
__A : str = frozenset(
[
"prompt",
"image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
__A : Tuple = frozenset(["prompt", "image", "negative_prompt"])
__A : List[Any] = 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 : str = frozenset(["prompt", "image", "mask_image", "negative_prompt"])
__A : Optional[Any] = frozenset(
[
# image variation with an image mask
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
__A : Union[str, Any] = frozenset(["image", "mask_image"])
__A : Any = frozenset(
[
"example_image",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
__A : Union[str, Any] = frozenset(["example_image", "image", "mask_image"])
__A : Optional[Any] = frozenset(["class_labels"])
__A : Tuple = frozenset(["class_labels"])
__A : Tuple = frozenset(["batch_size"])
__A : Union[str, Any] = frozenset([])
__A : Optional[Any] = frozenset(["batch_size"])
__A : Optional[Any] = frozenset([])
__A : Union[str, Any] = frozenset(
[
"prompt",
"audio_length_in_s",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : Tuple = frozenset(["prompt", "negative_prompt"])
__A : int = frozenset(["input_tokens"])
__A : List[Any] = frozenset(["input_tokens"])
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCAmelCase = arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE )]
# Reverse whole list
_UpperCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE )]
cur -= 1
return arr
if __name__ == "__main__":
__A : List[str] = input("Enter numbers separated by a comma:\n").strip()
__A : List[Any] = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 260 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"
__A : List[str] = "\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to 'cuda' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n >>> results = perplexity.compute(model_id='gpt2',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 78.22\n >>> print(round(results[\"perplexities\"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = datasets.load_dataset(\"wikitext\",\n ... \"wikitext-2-raw-v1\",\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!='']\n >>> results = perplexity.compute(model_id='gpt2',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 60.35\n >>> print(round(results[\"perplexities\"][0], 2))\n 81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int = 1_6 , __UpperCamelCase : bool = True , __UpperCamelCase : List[Any]=None )->Any:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = '''cuda'''
else:
_UpperCAmelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors='''pt''' , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_UpperCAmelCase = encodings['''input_ids''']
_UpperCAmelCase = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 260 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase = np.zeros_like(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
_UpperCAmelCase = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
_UpperCAmelCase = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
_UpperCAmelCase = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__A : str = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
__A : str = np.array(Image.open(lena_path))
# kernel to be applied
__A : List[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__A : Optional[Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__A : Optional[Any] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 260 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """MCTCTFeatureExtractor"""
UpperCamelCase__ = """AutoTokenizer"""
def __init__( self : Any , __UpperCamelCase : int , __UpperCamelCase : Any )->List[Any]:
super().__init__(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
def __call__( self : Dict , *__UpperCamelCase : List[str] , **__UpperCamelCase : Optional[int] )->int:
# 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.''' )
_UpperCAmelCase = kwargs.pop('''raw_speech''' )
else:
_UpperCAmelCase = kwargs.pop('''audio''' , __UpperCamelCase )
_UpperCAmelCase = kwargs.pop('''sampling_rate''' , __UpperCamelCase )
_UpperCAmelCase = kwargs.pop('''text''' , __UpperCamelCase )
if len(__UpperCamelCase ) > 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(__UpperCamelCase , *__UpperCamelCase , sampling_rate=__UpperCamelCase , **__UpperCamelCase )
if text is not None:
_UpperCAmelCase = self.tokenizer(__UpperCamelCase , **__UpperCamelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase = encodings['''input_ids''']
return inputs
def lowercase__ ( self : List[str] , *__UpperCamelCase : List[str] , **__UpperCamelCase : str )->List[str]:
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : str , *__UpperCamelCase : List[str] , **__UpperCamelCase : int )->Optional[int]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = kwargs.pop('''input_features''' , __UpperCamelCase )
_UpperCAmelCase = kwargs.pop('''labels''' , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
_UpperCAmelCase = args[0]
_UpperCAmelCase = args[1:]
if input_features is not None:
_UpperCAmelCase = self.feature_extractor.pad(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
if labels is not None:
_UpperCAmelCase = self.tokenizer.pad(__UpperCamelCase , **__UpperCamelCase )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_UpperCAmelCase = labels['''input_ids''']
return input_features
def lowercase__ ( self : List[Any] , *__UpperCamelCase : Union[str, Any] , **__UpperCamelCase : List[str] )->Any:
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@contextmanager
def lowercase__ ( self : List[Any] )->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.''' )
_UpperCAmelCase = True
_UpperCAmelCase = self.tokenizer
yield
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
| 260 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """audio-spectrogram-transformer"""
def __init__( self : int , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : int=1_2 , __UpperCamelCase : List[Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Dict=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1e-12 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=1_0 , __UpperCamelCase : Optional[int]=1_0 , __UpperCamelCase : str=1_0_2_4 , __UpperCamelCase : Optional[Any]=1_2_8 , **__UpperCamelCase : Any , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = patch_size
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = frequency_stride
_UpperCAmelCase = time_stride
_UpperCAmelCase = max_length
_UpperCAmelCase = num_mel_bins
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = len(_SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCAmelCase = 0
count += depth_first_search(_SCREAMING_SNAKE_CASE , row + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , row - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col + 1 , _SCREAMING_SNAKE_CASE )
count += depth_first_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , col - 1 , _SCREAMING_SNAKE_CASE )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
_UpperCAmelCase = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
_UpperCAmelCase = day - days_per_month[month - 2]
if month > 12:
year += 1
_UpperCAmelCase = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 260 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 0
UpperCamelCase__ = False
UpperCamelCase__ = 3.0
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[str] )->List[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'''a''': 2} )
self.assertDictEqual(MockClass(a=2 , b=__UpperCamelCase ).to_kwargs() , {'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 , c=2.2_5 ).to_kwargs() , {'''a''': 2, '''c''': 2.2_5} )
@require_cuda
def lowercase__ ( self : List[str] )->str:
# If no defaults are changed, `to_kwargs` returns an empty dict.
_UpperCAmelCase = GradScalerKwargs(init_scale=1_0_2_4 , growth_factor=2 )
AcceleratorState._reset_state()
_UpperCAmelCase = Accelerator(mixed_precision='''fp16''' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
_UpperCAmelCase = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1_0_2_4.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2_0_0_0 )
self.assertEqual(scaler._enabled , __UpperCamelCase )
@require_multi_gpu
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
execute_subprocess_async(__UpperCamelCase , env=os.environ.copy() )
if __name__ == "__main__":
__A : List[Any] = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
__A : Union[str, Any] = Accelerator(kwargs_handlers=[ddp_scaler])
__A : List[str] = torch.nn.Linear(100, 200)
__A : Any = accelerator.prepare(model)
# Check the values changed in kwargs
__A : int = ""
__A : List[Any] = model.bucket_bytes_cap // (1024 * 1024)
if observed_bucket_cap_map != 15:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 260 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [n]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if len(str(_SCREAMING_SNAKE_CASE ) ) > 3:
if not is_prime(int(str(_SCREAMING_SNAKE_CASE )[-3:] ) ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE )[:3] ) ):
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(_SCREAMING_SNAKE_CASE ) != count:
if validate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_truncated_nums(_SCREAMING_SNAKE_CASE )
if all(is_prime(_SCREAMING_SNAKE_CASE ) for i in list_nums ):
list_truncated_primes.append(_SCREAMING_SNAKE_CASE )
num += 2
return list_truncated_primes
def lowercase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 260 | 1 |
"""simple docstring"""
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,
)
__A : Any = "\\n Text data.\n Second line of data."
__A : str = "file"
@pytest.fixture(scope='''session''' )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
_UpperCAmelCase = bytes(_SCREAMING_SNAKE_CASE , '''utf-8''' )
with zstd.open(_SCREAMING_SNAKE_CASE , '''wb''' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return path
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _SCREAMING_SNAKE_CASE ) , '''w''' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_UpperCAmelCase = input_paths[compression_format]
_UpperCAmelCase = tmp_path / '''cache'''
_UpperCAmelCase = DownloadConfig(cache_dir=_SCREAMING_SNAKE_CASE , extract_compressed_file=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = cached_path(_SCREAMING_SNAKE_CASE , download_config=_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE ) as f:
_UpperCAmelCase = f.read()
with open(_SCREAMING_SNAKE_CASE ) as f:
_UpperCAmelCase = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
_UpperCAmelCase = '''custom_cache'''
_UpperCAmelCase = '''custom_extracted_dir'''
_UpperCAmelCase = tmp_path / '''custom_extracted_path'''
if default_extracted:
_UpperCAmelCase = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , _SCREAMING_SNAKE_CASE )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_UpperCAmelCase = xz_file
_UpperCAmelCase = (
DownloadConfig(extract_compressed_file=_SCREAMING_SNAKE_CASE )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_SCREAMING_SNAKE_CASE )
)
_UpperCAmelCase = cached_path(_SCREAMING_SNAKE_CASE , download_config=_SCREAMING_SNAKE_CASE )
assert Path(_SCREAMING_SNAKE_CASE ).parent.parts[-2:] == expected
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = str(Path(_SCREAMING_SNAKE_CASE ).resolve() )
assert cached_path(_SCREAMING_SNAKE_CASE ) == text_file
# relative path
_UpperCAmelCase = str(Path(_SCREAMING_SNAKE_CASE ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_SCREAMING_SNAKE_CASE ) == text_file
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
cached_path(_SCREAMING_SNAKE_CASE )
# relative path
_UpperCAmelCase = '''./__missing_file__.txt'''
with pytest.raises(_SCREAMING_SNAKE_CASE ):
cached_path(_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = get_from_cache(f'tmp://{tmpfs_file}' )
with open(_SCREAMING_SNAKE_CASE ) as f:
_UpperCAmelCase = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _SCREAMING_SNAKE_CASE )
def lowercase ( ):
'''simple docstring'''
with pytest.raises(_SCREAMING_SNAKE_CASE ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_SCREAMING_SNAKE_CASE ):
http_get('''https://huggingface.co''' , temp_file=_SCREAMING_SNAKE_CASE )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_SCREAMING_SNAKE_CASE ):
ftp_get('''ftp://huggingface.co''' , temp_file=_SCREAMING_SNAKE_CASE )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , _SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(_SCREAMING_SNAKE_CASE ):
fsspec_get('''s3://huggingface.co''' , temp_file=_SCREAMING_SNAKE_CASE )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
fsspec_head('''s3://huggingface.co''' )
| 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : str = sys.version_info >= (3, 10)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : Tuple )->Optional[int]:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : List[str] )->List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def lowercase__ ( self : int )->str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : int , __UpperCamelCase : argparse.ArgumentParser , __UpperCamelCase : argparse.ArgumentParser )->Dict:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , __UpperCamelCase ) and yy.get('''choices''' , __UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](__UpperCamelCase ) , yy['''type'''](__UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--bar''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--baz''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--flag''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(__UpperCamelCase , look_for_args_file=__UpperCamelCase )
self.assertFalse(example.flag )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=4_2 , type=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=__UpperCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 4_2] , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : List[str] )->List[str]:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 4_2) , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
def lowercase__ ( self : int )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=__UpperCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--bar''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , bar=__UpperCamelCase , baz=__UpperCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo=1_2 , bar=3.1_4 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--required_str''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
_UpperCAmelCase = parser.parse_dict(__UpperCamelCase )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 4_2,
}
self.assertRaises(__UpperCamelCase , parser.parse_dict , __UpperCamelCase , allow_extra_keys=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_json''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_yaml''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 260 | 1 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Optional[int] , *__UpperCamelCase : List[Any] , **__UpperCamelCase : str )->int:
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = {}
def lowercase__ ( self : int , __UpperCamelCase : Any , *__UpperCamelCase : List[str] , **__UpperCamelCase : Tuple )->str:
_UpperCAmelCase = super().add_tokens(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
''' `placeholder_token` that is not already in the tokenizer.''' )
def lowercase__ ( self : List[Any] , __UpperCamelCase : Union[str, Any] , *__UpperCamelCase : Any , __UpperCamelCase : Dict=1 , **__UpperCamelCase : str )->Dict:
_UpperCAmelCase = []
if num_vec_per_token == 1:
self.try_adding_tokens(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
output.append(__UpperCamelCase )
else:
_UpperCAmelCase = []
for i in range(__UpperCamelCase ):
_UpperCAmelCase = placeholder_token + F'_{i}'
self.try_adding_tokens(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
output.append(__UpperCamelCase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
_UpperCAmelCase = output
def lowercase__ ( self : List[str] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str]=False , __UpperCamelCase : Union[str, Any]=1.0 )->List[Any]:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCAmelCase = []
for i in range(len(__UpperCamelCase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=__UpperCamelCase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
_UpperCAmelCase = self.token_map[placeholder_token]
_UpperCAmelCase = tokens[: 1 + int(len(__UpperCamelCase ) * prop_tokens_to_load )]
if vector_shuffle:
_UpperCAmelCase = copy.copy(__UpperCamelCase )
random.shuffle(__UpperCamelCase )
_UpperCAmelCase = text.replace(__UpperCamelCase , ''' '''.join(__UpperCamelCase ) )
return text
def __call__( self : List[Any] , __UpperCamelCase : List[Any] , *__UpperCamelCase : Any , __UpperCamelCase : Dict=False , __UpperCamelCase : Tuple=1.0 , **__UpperCamelCase : Tuple )->Any:
return super().__call__(
self.replace_placeholder_tokens_in_text(
__UpperCamelCase , vector_shuffle=__UpperCamelCase , prop_tokens_to_load=__UpperCamelCase ) , *__UpperCamelCase , **__UpperCamelCase , )
def lowercase__ ( self : List[str] , __UpperCamelCase : List[Any] , *__UpperCamelCase : str , __UpperCamelCase : Tuple=False , __UpperCamelCase : Union[str, Any]=1.0 , **__UpperCamelCase : Any )->Optional[Any]:
return super().encode(
self.replace_placeholder_tokens_in_text(
__UpperCamelCase , vector_shuffle=__UpperCamelCase , prop_tokens_to_load=__UpperCamelCase ) , *__UpperCamelCase , **__UpperCamelCase , )
| 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase = True
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_UpperCAmelCase = True
if a[i].islower():
_UpperCAmelCase = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int]=0.999 , _SCREAMING_SNAKE_CASE : Optional[Any]="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(_SCREAMING_SNAKE_CASE : str ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_SCREAMING_SNAKE_CASE : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
_UpperCAmelCase = []
for i in range(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = i / num_diffusion_timesteps
_UpperCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) )
return torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.floataa )
class _a ( lowerCAmelCase , lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 1
@register_to_config
def __init__( self : Optional[int] , __UpperCamelCase : int = 1_0_0_0 , __UpperCamelCase : float = 0.0_0_0_1 , __UpperCamelCase : float = 0.0_2 , __UpperCamelCase : str = "linear" , __UpperCamelCase : Optional[Union[np.ndarray, List[float]]] = None , __UpperCamelCase : bool = True , __UpperCamelCase : bool = True , __UpperCamelCase : int = 0 , __UpperCamelCase : str = "epsilon" , __UpperCamelCase : float = 1.0 , **__UpperCamelCase : Union[str, Any] , )->Optional[int]:
if kwargs.get('''set_alpha_to_one''' , __UpperCamelCase ) is not None:
_UpperCAmelCase = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase )
_UpperCAmelCase = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
_UpperCAmelCase = torch.tensor(__UpperCamelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
_UpperCAmelCase = torch.linspace(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCAmelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __UpperCamelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase = betas_for_alpha_bar(__UpperCamelCase )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
_UpperCAmelCase = 1.0 - self.betas
_UpperCAmelCase = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_UpperCAmelCase = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_UpperCAmelCase = 1.0
# setable values
_UpperCAmelCase = None
_UpperCAmelCase = torch.from_numpy(np.arange(0 , __UpperCamelCase ).copy().astype(np.intaa ) )
def lowercase__ ( self : List[str] , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : Optional[int] = None )->torch.FloatTensor:
return sample
def lowercase__ ( self : Dict , __UpperCamelCase : int , __UpperCamelCase : Union[str, torch.device] = None )->str:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.' )
_UpperCAmelCase = num_inference_steps
_UpperCAmelCase = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_UpperCAmelCase = (np.arange(0 , __UpperCamelCase ) * step_ratio).round().copy().astype(np.intaa )
_UpperCAmelCase = torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )
self.timesteps += self.config.steps_offset
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : int , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : float = 0.0 , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[torch.FloatTensor] = None , __UpperCamelCase : bool = True , )->Union[DDIMSchedulerOutput, Tuple]:
# 1. get previous step value (=t+1)
_UpperCAmelCase = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_UpperCAmelCase = self.alphas_cumprod[timestep]
_UpperCAmelCase = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
_UpperCAmelCase = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
_UpperCAmelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_UpperCAmelCase = model_output
elif self.config.prediction_type == "sample":
_UpperCAmelCase = model_output
_UpperCAmelCase = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_UpperCAmelCase = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_UpperCAmelCase = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_UpperCAmelCase = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=__UpperCamelCase , pred_original_sample=__UpperCamelCase )
def __len__( self : List[str] )->List[str]:
return self.config.num_train_timesteps
| 260 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
quick_sort_random(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_SCREAMING_SNAKE_CASE , pivot_index + 1 , _SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = input('''Enter numbers separated by a comma:\n''' ).strip()
_UpperCAmelCase = [int(_SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) )
print(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 1 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
if isinstance(_SCREAMING_SNAKE_CASE , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class _a :
"""simple docstring"""
def lowercase__ ( self : List[Any] , __UpperCamelCase : int , __UpperCamelCase : List[str] )->List[Any]:
pass
def lowercase__ ( self : Optional[int] )->Tuple:
pass
def lowercase__ ( self : Dict )->str:
pass
def lowercase__ ( self : str , __UpperCamelCase : np.ndarray , __UpperCamelCase : np.ndarray , __UpperCamelCase : float )->Optional[Any]:
_UpperCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(__UpperCamelCase , __UpperCamelCase , F'Difference between torch and flax is {diff} (>= {tol}).' )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str] , __UpperCamelCase : Any , __UpperCamelCase : Any , __UpperCamelCase : List[Any]=None , **__UpperCamelCase : Any )->Optional[Any]:
_UpperCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = FlaxVisionTextDualEncoderModel(__UpperCamelCase )
_UpperCAmelCase = model(input_ids=__UpperCamelCase , pixel_values=__UpperCamelCase , attention_mask=__UpperCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], config.projection_dim) )
def lowercase__ ( self : int , __UpperCamelCase : List[str] , __UpperCamelCase : Any , __UpperCamelCase : Any , __UpperCamelCase : List[str] , __UpperCamelCase : Any=None , **__UpperCamelCase : Optional[Any] )->Optional[Any]:
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = {'''vision_model''': vision_model, '''text_model''': text_model}
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase )
_UpperCAmelCase = model(input_ids=__UpperCamelCase , pixel_values=__UpperCamelCase , attention_mask=__UpperCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def lowercase__ ( self : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str=None , **__UpperCamelCase : Dict )->str:
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = {'''vision_model''': vision_model, '''text_model''': text_model}
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase )
_UpperCAmelCase = model(input_ids=__UpperCamelCase , pixel_values=__UpperCamelCase , attention_mask=__UpperCamelCase )
_UpperCAmelCase = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCamelCase )
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model(input_ids=__UpperCamelCase , pixel_values=__UpperCamelCase , attention_mask=__UpperCamelCase )
_UpperCAmelCase = after_output[0]
_UpperCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1e-3 )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Any , __UpperCamelCase : str , __UpperCamelCase : List[str]=None , **__UpperCamelCase : int )->Dict:
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = {'''vision_model''': vision_model, '''text_model''': text_model}
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase )
_UpperCAmelCase = model(
input_ids=__UpperCamelCase , pixel_values=__UpperCamelCase , attention_mask=__UpperCamelCase , output_attentions=__UpperCamelCase )
_UpperCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(__UpperCamelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase = to_atuple(vision_model.config.image_size )
_UpperCAmelCase = to_atuple(vision_model.config.patch_size )
_UpperCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_UpperCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_UpperCAmelCase = output.text_model_output.attentions
self.assertEqual(len(__UpperCamelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : Tuple )->Optional[Any]:
pt_model.to(__UpperCamelCase )
pt_model.eval()
# prepare inputs
_UpperCAmelCase = inputs_dict
_UpperCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
_UpperCAmelCase = pt_model(**__UpperCamelCase ).to_tuple()
_UpperCAmelCase = fx_model(**__UpperCamelCase ).to_tuple()
self.assertEqual(len(__UpperCamelCase ) , len(__UpperCamelCase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(__UpperCamelCase , pt_output.numpy() , 4e-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(__UpperCamelCase )
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
_UpperCAmelCase = fx_model_loaded(**__UpperCamelCase ).to_tuple()
self.assertEqual(len(__UpperCamelCase ) , len(__UpperCamelCase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(__UpperCamelCase , pt_output.numpy() , 4e-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(__UpperCamelCase )
_UpperCAmelCase = VisionTextDualEncoderModel.from_pretrained(__UpperCamelCase , from_flax=__UpperCamelCase )
pt_model_loaded.to(__UpperCamelCase )
pt_model_loaded.eval()
with torch.no_grad():
_UpperCAmelCase = pt_model_loaded(**__UpperCamelCase ).to_tuple()
self.assertEqual(len(__UpperCamelCase ) , len(__UpperCamelCase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(__UpperCamelCase , pt_output_loaded.numpy() , 4e-2 )
def lowercase__ ( self : List[str] , __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] )->List[str]:
_UpperCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = VisionTextDualEncoderModel(__UpperCamelCase )
_UpperCAmelCase = FlaxVisionTextDualEncoderModel(__UpperCamelCase )
_UpperCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , __UpperCamelCase )
_UpperCAmelCase = fx_state
self.check_pt_flax_equivalence(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple , __UpperCamelCase : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : int )->List[str]:
_UpperCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = VisionTextDualEncoderModel(__UpperCamelCase )
_UpperCAmelCase = FlaxVisionTextDualEncoderModel(__UpperCamelCase )
_UpperCAmelCase = load_flax_weights_in_pytorch_model(__UpperCamelCase , fx_model.params )
self.check_pt_flax_equivalence(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Any )->Any:
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**__UpperCamelCase )
def lowercase__ ( self : str )->Any:
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**__UpperCamelCase )
def lowercase__ ( self : Tuple )->List[Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**__UpperCamelCase )
@is_pt_flax_cross_test
def lowercase__ ( self : Any )->List[str]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = config_inputs_dict.pop('''vision_config''' )
_UpperCAmelCase = config_inputs_dict.pop('''text_config''' )
_UpperCAmelCase = config_inputs_dict
self.check_equivalence_pt_to_flax(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
self.check_equivalence_flax_to_pt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@slow
def lowercase__ ( self : Tuple )->str:
_UpperCAmelCase , _UpperCAmelCase = self.get_pretrained_model_and_inputs()
_UpperCAmelCase = model_a(**__UpperCamelCase )
_UpperCAmelCase = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(__UpperCamelCase )
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model_a(**__UpperCamelCase )
_UpperCAmelCase = after_outputs[0]
_UpperCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCamelCase , 1e-5 )
@require_flax
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->List[Any]:
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' , '''hf-internal-testing/tiny-bert''' , vision_from_pt=__UpperCamelCase , text_from_pt=__UpperCamelCase , )
_UpperCAmelCase = 1_3
_UpperCAmelCase = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
_UpperCAmelCase = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
_UpperCAmelCase = random_attention_mask([batch_size, 4] )
_UpperCAmelCase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def lowercase__ ( self : List[str] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] )->Optional[Any]:
_UpperCAmelCase = FlaxViTModel(__UpperCamelCase )
_UpperCAmelCase = FlaxBertModel(__UpperCamelCase )
return vision_model, text_model
def lowercase__ ( self : str )->Optional[Any]:
_UpperCAmelCase = FlaxViTModelTester(self )
_UpperCAmelCase = FlaxBertModelTester(self )
_UpperCAmelCase = vit_model_tester.prepare_config_and_inputs()
_UpperCAmelCase = bert_model_tester.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase = vision_config_and_inputs
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : str )->Optional[Any]:
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-clip''' , '''hf-internal-testing/tiny-bert''' , vision_from_pt=__UpperCamelCase , text_from_pt=__UpperCamelCase , )
_UpperCAmelCase = 1_3
_UpperCAmelCase = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
_UpperCAmelCase = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
_UpperCAmelCase = random_attention_mask([batch_size, 4] )
_UpperCAmelCase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] )->str:
_UpperCAmelCase = FlaxCLIPVisionModel(__UpperCamelCase )
_UpperCAmelCase = FlaxBertModel(__UpperCamelCase )
return vision_model, text_model
def lowercase__ ( self : Dict )->List[str]:
_UpperCAmelCase = FlaxCLIPVisionModelTester(self )
_UpperCAmelCase = FlaxBertModelTester(self )
_UpperCAmelCase = clip_model_tester.prepare_config_and_inputs()
_UpperCAmelCase = bert_model_tester.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase = vision_config_and_inputs
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class _a ( unittest.TestCase):
"""simple docstring"""
@slow
def lowercase__ ( self : Any )->List[str]:
_UpperCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian''' , logit_scale_init_value=1.0 )
_UpperCAmelCase = VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
_UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_UpperCAmelCase = processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] , images=__UpperCamelCase , padding=__UpperCamelCase , return_tensors='''np''' )
_UpperCAmelCase = model(**__UpperCamelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_UpperCAmelCase = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image , __UpperCamelCase , atol=1e-3 ) )
| 260 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"
__A : List[str] = "\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to 'cuda' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n >>> results = perplexity.compute(model_id='gpt2',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 78.22\n >>> print(round(results[\"perplexities\"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric(\"perplexity\")\n >>> input_texts = datasets.load_dataset(\"wikitext\",\n ... \"wikitext-2-raw-v1\",\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!='']\n >>> results = perplexity.compute(model_id='gpt2',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n ['perplexities', 'mean_perplexity']\n >>> print(round(results[\"mean_perplexity\"], 2))\n 60.35\n >>> print(round(results[\"perplexities\"][0], 2))\n 81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : List[Any] )->Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int = 1_6 , __UpperCamelCase : bool = True , __UpperCamelCase : List[Any]=None )->Any:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = '''cuda'''
else:
_UpperCAmelCase = '''cuda''' if torch.cuda.is_available() else '''cpu'''
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCamelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors='''pt''' , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_UpperCAmelCase = encodings['''input_ids''']
_UpperCAmelCase = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 260 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.getbasetemp() / '''cache'''
_UpperCAmelCase = test_hf_cache_home / '''datasets'''
_UpperCAmelCase = test_hf_cache_home / '''metrics'''
_UpperCAmelCase = test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(_SCREAMING_SNAKE_CASE ) )
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE , scope='''session''' )
def lowercase ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , _SCREAMING_SNAKE_CASE )
| 260 | 1 |
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