






import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
   import tensorflow as tf

   tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
   import torch

if is_datasets_available():
   from datasets import load_dataset


lowerCamelCase					    =	5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    return PTtoTFCommand(
        args.model_name,   args.local_dir,   args.max_error,   args.new_weights,   args.no_pr,   args.push,   args.extra_commit_description,   args.override_model_class,   )

class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   @staticmethod
   def       SCREAMING_SNAKE_CASE__					(     snake_case__:						ArgumentParser						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						parser.add_parser(
           'pt-to-tf' ,							help=(
               'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
               ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
           ) ,							)
       train_parser.add_argument(
           '--model-name' ,							type=snake_case__ ,							required=snake_case__ ,							help='The model name, including owner/organization, as seen on the hub.' ,							)
       train_parser.add_argument(
           '--local-dir' ,							type=snake_case__ ,							default='' ,							help='Optional local directory of the model repository. Defaults to /tmp/{model_name}' ,							)
       train_parser.add_argument(
           '--max-error' ,							type=snake_case__ ,							default=snake_case__ ,							help=(
               f'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
           ) ,							)
       train_parser.add_argument(
           '--new-weights' ,							action='store_true' ,							help='Optional flag to create new TensorFlow weights, even if they already exist.' ,							)
       train_parser.add_argument(
           '--no-pr' ,							action='store_true' ,							help='Optional flag to NOT open a PR with converted weights.'						)
       train_parser.add_argument(
           '--push' ,							action='store_true' ,							help='Optional flag to push the weights directly to `main` (requires permissions)' ,							)
       train_parser.add_argument(
           '--extra-commit-description' ,							type=snake_case__ ,							default='' ,							help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).' ,							)
       train_parser.add_argument(
           '--override-model-class' ,							type=snake_case__ ,							default=snake_case__ ,							help='If you think you know better than the auto-detector, you can specify the model class here. '
           'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.' ,							)
       train_parser.set_defaults(func=snake_case__						)

   @staticmethod
   def       SCREAMING_SNAKE_CASE__					(     snake_case__:						Optional[int] ,							snake_case__:						Dict						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						set(pt_outputs.keys()						)
       _lowercase     			=						set(tf_outputs.keys()						)
       if pt_out_attrs != tf_out_attrs:
           raise ValueError(
               f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
               f''' {tf_out_attrs})'''						)

       # 2. For each output attribute, computes the difference
       def _find_pt_tf_differences(snake_case__:						str ,							snake_case__:						List[Any] ,							snake_case__:						List[Any] ,							snake_case__:						Dict=""						):
           # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
           # recursivelly, keeping the name of the attribute.
           if isinstance(snake_case__ ,							torch.Tensor						):
               _lowercase     			=						np.max(np.abs(pt_out.numpy() - tf_out.numpy()						)						)
               _lowercase     			=						tensor_difference
           else:
               _lowercase     			=						attr_name
               for i, pt_item in enumerate(snake_case__						):
                   # If it is a named attribute, we keep the name. Otherwise, just its index.
                   if isinstance(snake_case__ ,							snake_case__						):
                       _lowercase     			=						root_name + pt_item
                       _lowercase     			=						tf_out[pt_item]
                       _lowercase     			=						pt_out[pt_item]
                   else:
                       _lowercase     			=						root_name + f'''[{i}]'''
                       _lowercase     			=						tf_out[i]
                   _lowercase     			=						_find_pt_tf_differences(snake_case__ ,							snake_case__ ,							snake_case__ ,							snake_case__						)

           return differences

       return _find_pt_tf_differences(snake_case__ ,							snake_case__ ,							{}						)

   def __init__(     self:						str ,							snake_case__:						str ,							snake_case__:						str ,							snake_case__:						float ,							snake_case__:						bool ,							snake_case__:						bool ,							snake_case__:						bool ,							snake_case__:						str ,							snake_case__:						str ,							*snake_case__:						List[str] ,							)	->		str:


       """simple docstring"""



       _lowercase     			=						logging.get_logger('transformers-cli/pt_to_tf'						)
       _lowercase     			=						model_name
       _lowercase     			=						local_dir if local_dir else os.path.join('/tmp' ,							snake_case__						)
       _lowercase     			=						max_error
       _lowercase     			=						new_weights
       _lowercase     			=						no_pr
       _lowercase     			=						push
       _lowercase     			=						extra_commit_description
       _lowercase     			=						override_model_class

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						Union[str, Any] ,							snake_case__:						Optional[Any] ,							snake_case__:						Optional[Any]						)	->		str:


       """simple docstring"""



       def _get_audio_input():
           _lowercase     			=						load_dataset('hf-internal-testing/librispeech_asr_dummy' ,							'clean' ,							split='validation'						)
           _lowercase     			=						ds.sort('id'						).select(range(2						)						)[:2]['audio']
           _lowercase     			=						[x['array'] for x in speech_samples]
           return raw_samples

       _lowercase     			=						type(pt_model.config						)
       if model_config_class in PROCESSOR_MAPPING:
           _lowercase     			=						AutoProcessor.from_pretrained(self._local_dir						)
           if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
               _lowercase     			=						processor.tokenizer.eos_token
       elif model_config_class in IMAGE_PROCESSOR_MAPPING:
           _lowercase     			=						AutoImageProcessor.from_pretrained(self._local_dir						)
       elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
           _lowercase     			=						AutoFeatureExtractor.from_pretrained(self._local_dir						)
       elif model_config_class in TOKENIZER_MAPPING:
           _lowercase     			=						AutoTokenizer.from_pretrained(self._local_dir						)
           if processor.pad_token is None:
               _lowercase     			=						processor.eos_token
       else:
           raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''						)

       _lowercase     			=						set(inspect.signature(pt_model.forward						).parameters.keys()						)
       _lowercase     			=						{}
       if "input_ids" in model_forward_signature:
           processor_inputs.update(
               {
                   'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
                   'padding': True,
                   'truncation': True,
               }						)
       if "pixel_values" in model_forward_signature:
           _lowercase     			=						load_dataset('cifar10' ,							'plain_text' ,							split='test'						)[:2]['img']
           processor_inputs.update({'images': sample_images}						)
       if "input_features" in model_forward_signature:
           _lowercase     			=						inspect.signature(processor.feature_extractor						).parameters
           # Pad to the largest input length by default but take feature extractor default
           # padding value if it exists e.g. "max_length" and is not False or None
           if "padding" in feature_extractor_signature:
               _lowercase     			=						feature_extractor_signature['padding'].default
               if default_strategy is not False and default_strategy is not None:
                   _lowercase     			=						default_strategy
               else:
                   _lowercase     			=						True
           else:
               _lowercase     			=						True
           processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy}						)
       if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
           processor_inputs.update({'audio': _get_audio_input(), 'padding': True}						)
       _lowercase     			=						processor(**snake_case__ ,							return_tensors='pt'						)
       _lowercase     			=						processor(**snake_case__ ,							return_tensors='tf'						)

       # Extra input requirements, in addition to the input modality
       if (
           config.is_encoder_decoder
           or (hasattr(snake_case__ ,							'encoder'						) and hasattr(snake_case__ ,							'decoder'						))
           or "decoder_input_ids" in tf_dummy_inputs
       ):
           _lowercase     			=						np.asarray([[1], [1]] ,							dtype=snake_case__						) * (pt_model.config.decoder_start_token_id or 0)
           pt_input.update({'decoder_input_ids': torch.tensor(snake_case__						)}						)
           tf_input.update({'decoder_input_ids': tf.convert_to_tensor(snake_case__						)}						)

       return pt_input, tf_input

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		List[str]:


       """simple docstring"""



       if version.parse(huggingface_hub.__version__						) < version.parse('0.9.0'						):
           raise ImportError(
               'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
               ' installation.'						)
       else:
           from huggingface_hub import Repository, create_commit
           from huggingface_hub._commit_api import CommitOperationAdd

       # Fetch remote data
       _lowercase     			=						Repository(local_dir=self._local_dir ,							clone_from=self._model_name						)

       # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
       _lowercase     			=						AutoConfig.from_pretrained(self._local_dir						)
       _lowercase     			=						config.architectures
       if self._override_model_class is not None:
           if self._override_model_class.startswith('TF'						):
               _lowercase     			=						[self._override_model_class[2:]]
           else:
               _lowercase     			=						[self._override_model_class]
           try:
               _lowercase     			=						getattr(import_module('transformers'						) ,							architectures[0]						)
           except AttributeError:
               raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''						)
           try:
               _lowercase     			=						getattr(import_module('transformers'						) ,							'TF' + architectures[0]						)
           except AttributeError:
               raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.'''						)
       elif architectures is None:  # No architecture defined -- use auto classes
           _lowercase     			=						getattr(import_module('transformers'						) ,							'AutoModel'						)
           _lowercase     			=						getattr(import_module('transformers'						) ,							'TFAutoModel'						)
           self._logger.warning('No detected architecture, using AutoModel/TFAutoModel'						)
       else:  # Architecture defined -- use it
           if len(snake_case__						) > 1:
               raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''						)
           self._logger.warning(f'''Detected architecture: {architectures[0]}'''						)
           _lowercase     			=						getattr(import_module('transformers'						) ,							architectures[0]						)
           try:
               _lowercase     			=						getattr(import_module('transformers'						) ,							'TF' + architectures[0]						)
           except AttributeError:
               raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''						)

        # Check the TF dummy inputs to see what keys we need in the forward pass
       _lowercase     			=						tf_class.from_config(snake_case__						)
       _lowercase     			=						tf_from_pt_model.dummy_inputs

       del tf_from_pt_model  # Try to keep only one model in memory at a time

       # Load the model and get some basic inputs
       _lowercase     			=						pt_class.from_pretrained(self._local_dir						)
       pt_model.eval()

       _lowercase			,     _lowercase     			=						self.get_inputs(snake_case__ ,							snake_case__ ,							snake_case__						)

       with torch.no_grad():
           _lowercase     			=						pt_model(**snake_case__ ,							output_hidden_states=snake_case__						)
       del pt_model  # will no longer be used, and may have a large memory footprint

       _lowercase     			=						tf_class.from_pretrained(self._local_dir ,							from_pt=snake_case__						)
       _lowercase     			=						tf_from_pt_model(**snake_case__ ,							output_hidden_states=snake_case__ ,							training=snake_case__						)

       # Confirms that cross loading PT weights into TF worked.
       _lowercase     			=						self.find_pt_tf_differences(snake_case__ ,							snake_case__						)
       _lowercase     			=						{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
       _lowercase     			=						{k: v for k, v in crossload_differences.items() if 'hidden' in k}
       if len(snake_case__						) == 0 and architectures is not None:
           raise ValueError(
               f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
               ' output was found. All outputs start with \'hidden\''						)
       _lowercase     			=						max(output_differences.values()						) if output_differences else 0.0
       _lowercase     			=						max(hidden_differences.values()						)
       if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
           raise ValueError(
               'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
               + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
               + '\n'.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]						)
               + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
               + '\n'.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]						)						)

       # Save the weights in a TF format (if needed) and confirms that the results are still good
       _lowercase     			=						os.path.join(self._local_dir ,							snake_case__						)
       _lowercase     			=						os.path.join(self._local_dir ,							snake_case__						)
       if (not os.path.exists(snake_case__						) and not os.path.exists(snake_case__						)) or self._new_weights:
           tf_from_pt_model.save_pretrained(self._local_dir						)
       del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

       _lowercase     			=						tf_class.from_pretrained(self._local_dir						)
       _lowercase     			=						tf_model(**snake_case__ ,							output_hidden_states=snake_case__						)

       _lowercase     			=						self.find_pt_tf_differences(snake_case__ ,							snake_case__						)
       _lowercase     			=						{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
       _lowercase     			=						{k: v for k, v in conversion_differences.items() if 'hidden' in k}
       if len(snake_case__						) == 0 and architectures is not None:
           raise ValueError(
               f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
               ' output was found. All outputs start with \'hidden\''						)
       _lowercase     			=						max(output_differences.values()						) if output_differences else 0.0
       _lowercase     			=						max(hidden_differences.values()						)
       if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
           raise ValueError(
               'The converted TensorFlow model has different outputs, something went wrong!\n'
               + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
               + '\n'.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]						)
               + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
               + '\n'.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]						)						)

       _lowercase     			=						'Update TF weights' if self._new_weights else 'Add TF weights'
       if self._push:
           repo.git_add(auto_lfs_track=snake_case__						)
           repo.git_commit(snake_case__						)
           repo.git_push(blocking=snake_case__						)  # this prints a progress bar with the upload
           self._logger.warning(f'''TF weights pushed into {self._model_name}'''						)
       elif not self._no_pr:
           self._logger.warning('Uploading the weights into a new PR...'						)
           _lowercase     			=						(
               'Model converted by the [`transformers`\' `pt_to_tf`'
               ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
               'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
               f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
               f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
               f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
               f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
           )
           if self._max_error > MAX_ERROR:
               commit_descrition += (
                   f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
               )
           if self._extra_commit_description:
               commit_descrition += "\n\n" + self._extra_commit_description

           # sharded model -> adds all related files (index and .h5 shards)
           if os.path.exists(snake_case__						):
               _lowercase     			=						[
                   CommitOperationAdd(path_in_repo=snake_case__ ,							path_or_fileobj=snake_case__						)
               ]
               for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'						):
                   operations += [
                       CommitOperationAdd(path_in_repo=os.path.basename(snake_case__						) ,							path_or_fileobj=snake_case__						)
                   ]
           else:
               _lowercase     			=						[CommitOperationAdd(path_in_repo=snake_case__ ,							path_or_fileobj=snake_case__						)]

           _lowercase     			=						create_commit(
               repo_id=self._model_name ,							operations=snake_case__ ,							commit_message=snake_case__ ,							commit_description=snake_case__ ,							repo_type='model' ,							create_pr=snake_case__ ,							).pr_url
           self._logger.warning(f'''PR open in {hub_pr_url}'''						)








import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase					    =	threading.Lock()
lowerCamelCase					    =	None

lowerCamelCase					    =	{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCamelCase					    =	logging.WARNING

lowerCamelCase					    =	True
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						os.getenv('DIFFUSERS_VERBOSITY',   _lowercase   )
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                f'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
                f'''has to be one of: { ", ".join(log_levels.keys()   ) }'''   )
    return _default_log_level
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return __name__.split('.'   )[0]
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return logging.getLogger(_get_library_name()   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _default_handler

    with _lock:
        if _default_handler:
            # This library has already configured the library root logger.
            return
        _lowercase     			=						logging.StreamHandler()  # Set sys.stderr as stream.
        _lowercase     			=						sys.stderr.flush

        # Apply our default configuration to the library root logger.
        _lowercase     			=						_get_library_root_logger()
        library_root_logger.addHandler(_default_handler   )
        library_root_logger.setLevel(_get_default_logging_level()   )
        _lowercase     			=						False
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _default_handler

    with _lock:
        if not _default_handler:
            return

        _lowercase     			=						_get_library_root_logger()
        library_root_logger.removeHandler(_default_handler   )
        library_root_logger.setLevel(logging.NOTSET   )
        _lowercase     			=						None
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return log_levels
def 							UpperCAmelCase   ( _lowercase = None   ):


    '''simple docstring'''



    if name is None:
        _lowercase     			=						_get_library_name()

    _configure_library_root_logger()
    return logging.getLogger(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()
    return _get_library_root_logger().getEffectiveLevel()
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _configure_library_root_logger()
    _get_library_root_logger().setLevel(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    return set_verbosity(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().removeHandler(_default_handler   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().addHandler(_default_handler   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert handler is not None
    _get_library_root_logger().addHandler(_lowercase   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _configure_library_root_logger()

    assert handler is not None and handler not in _get_library_root_logger().handlers
    _get_library_root_logger().removeHandler(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()
    _lowercase     			=						False
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _configure_library_root_logger()
    _lowercase     			=						True
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						_get_library_root_logger().handlers

    for handler in handlers:
        _lowercase     			=						logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'   )
        handler.setFormatter(_lowercase   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						_get_library_root_logger().handlers

    for handler in handlers:
        handler.setFormatter(_lowercase   )
def 							UpperCAmelCase   ( self,   *_lowercase,   **_lowercase   ):


    '''simple docstring'''



    _lowercase     			=						os.getenv('DIFFUSERS_NO_ADVISORY_WARNINGS',   _lowercase   )
    if no_advisory_warnings:
        return
    self.warning(*_lowercase,   **_lowercase   )


lowerCamelCase					    =	warning_advice

class        snake_case_				:







   """simple docstring"""



   def __init__(     self:						int ,							*snake_case__:						str ,							**snake_case__:						Any						)	->		Any:  # pylint: disable=unused-argument


       """simple docstring"""



       _lowercase     			=						args[0] if args else None

   def __iter__(     self:						Union[str, Any]						)	->		str:


       """simple docstring"""



       return iter(self._iterator						)

   def __getattr__(     self:						int ,							snake_case__:						Any						)	->		List[Any]:


       """simple docstring"""



       def empty_fn(*snake_case__:						Optional[int] ,							**snake_case__:						Union[str, Any]						):  # pylint: disable=unused-argument
           return

       return empty_fn

   def __enter__(     self:						List[Any]						)	->		Tuple:


       """simple docstring"""



       return self

   def __exit__(     self:						List[str] ,							snake_case__:						Optional[Any] ,							snake_case__:						Dict ,							snake_case__:						List[str]						)	->		Optional[Any]:


       """simple docstring"""



       return






class        snake_case_				:







   """simple docstring"""



   def __call__(     self:						List[str] ,							*snake_case__:						Tuple ,							**snake_case__:						Union[str, Any]						)	->		List[str]:


       """simple docstring"""



       if _tqdm_active:
           return tqdm_lib.tqdm(*snake_case__ ,							**snake_case__						)
       else:
           return EmptyTqdm(*snake_case__ ,							**snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							*snake_case__:						Union[str, Any] ,							**snake_case__:						str						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						None
       if _tqdm_active:
           return tqdm_lib.tqdm.set_lock(*snake_case__ ,							**snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		int:


       """simple docstring"""



       if _tqdm_active:
           return tqdm_lib.tqdm.get_lock()






lowerCamelCase					    =	_tqdm_cls()
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _tqdm_active
    return bool(_tqdm_active   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _tqdm_active
    _lowercase     			=						True
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    global _tqdm_active
    _lowercase     			=						False
