code: 
  
  
  
  """simple docstring"""
  
  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
  
  
  __A 		=			5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
  		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 			lowerCamelCase__    (  A_			):
  
  
  			@staticmethod
  			def 							lowerCamelCase_				(     SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Any								=				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=SCREAMING_SNAKE_CASE				, required=SCREAMING_SNAKE_CASE				, help="The model name, including owner/organization, as seen on the hub."				, )
  					train_parser.add_argument(
  					    "--local-dir"				, type=SCREAMING_SNAKE_CASE				, default=""				, help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"				, )
  					train_parser.add_argument(
  					    "--max-error"				, type=SCREAMING_SNAKE_CASE				, default=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE				, default=SCREAMING_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=SCREAMING_SNAKE_CASE							)
  
  
  			@staticmethod
  			def 							lowerCamelCase_				(     SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				set(pt_outputs.keys()							)
  					snake_case		:						str								=				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(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=""							):
  							# 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(SCREAMING_SNAKE_CASE				, torch.Tensor							):
  									snake_case		:						str								=				np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
  									snake_case		:						Dict								=				tensor_difference
  							else:
  									snake_case		:						Tuple								=				attr_name
  									for i, pt_item in enumerate(SCREAMING_SNAKE_CASE							):
  											# If it is a named attribute, we keep the name. Otherwise, just its index.
  											if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  													snake_case		:						List[str]								=				root_name + pt_item
  													snake_case		:						Dict								=				tf_out[pt_item]
  													snake_case		:						Tuple								=				pt_out[pt_item]
  											else:
  													snake_case		:						Dict								=				root_name + F'''[{i}]'''
  													snake_case		:						List[Any]								=				tf_out[i]
  											snake_case		:						Any								=				_find_pt_tf_differences(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  							return differences
  
  					return _find_pt_tf_differences(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, {}							)
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, *SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				logging.get_logger("transformers-cli/pt_to_tf"							)
  					snake_case		:						Optional[Any]								=				model_name
  					snake_case		:						int								=				local_dir if local_dir else os.path.join("/tmp"				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				max_error
  					snake_case		:						List[Any]								=				new_weights
  					snake_case		:						Union[str, Any]								=				no_pr
  					snake_case		:						Tuple								=				push
  					snake_case		:						Tuple								=				extra_commit_description
  					snake_case		:						Optional[int]								=				override_model_class
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					def _get_audio_input():
  							snake_case		:						str								=				load_dataset("hf-internal-testing/librispeech_asr_dummy"				, "clean"				, split="validation"							)
  							snake_case		:						Union[str, Any]								=				ds.sort("id"							).select(range(2							)							)[:2]['''audio''']
  							snake_case		:						List[Any]								=				[x['''array'''] for x in speech_samples]
  							return raw_samples
  
  					snake_case		:						List[str]								=				type(pt_model.config							)
  					if model_config_class in PROCESSOR_MAPPING:
  							snake_case		:						str								=				AutoProcessor.from_pretrained(self._local_dir							)
  							if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
  									snake_case		:						str								=				processor.tokenizer.eos_token
  					elif model_config_class in IMAGE_PROCESSOR_MAPPING:
  							snake_case		:						List[Any]								=				AutoImageProcessor.from_pretrained(self._local_dir							)
  					elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
  							snake_case		:						Union[str, Any]								=				AutoFeatureExtractor.from_pretrained(self._local_dir							)
  					elif model_config_class in TOKENIZER_MAPPING:
  							snake_case		:						Any								=				AutoTokenizer.from_pretrained(self._local_dir							)
  							if processor.pad_token is None:
  									snake_case		:						Tuple								=				processor.eos_token
  					else:
  							raise ValueError(F'''Unknown data processing type (model config type: {model_config_class})'''							)
  
  					snake_case		:						int								=				set(inspect.signature(pt_model.forward							).parameters.keys()							)
  					snake_case		:						Optional[int]								=				{}
  					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:
  							snake_case		:						List[Any]								=				load_dataset("cifar10"				, "plain_text"				, split="test"							)[:2]['''img''']
  							processor_inputs.update({"images": sample_images}							)
  					if "input_features" in model_forward_signature:
  							snake_case		:						Any								=				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:
  									snake_case		:						str								=				feature_extractor_signature['''padding'''].default
  									if default_strategy is not False and default_strategy is not None:
  											snake_case		:						Dict								=				default_strategy
  									else:
  											snake_case		:						int								=				True
  							else:
  									snake_case		:						Any								=				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}							)
  					snake_case		:						Optional[Any]								=				processor(**SCREAMING_SNAKE_CASE				, return_tensors="pt"							)
  					snake_case		:						Optional[int]								=				processor(**SCREAMING_SNAKE_CASE				, return_tensors="tf"							)
  
  					# Extra input requirements, in addition to the input modality
  					if (
  					    config.is_encoder_decoder
  					    or (hasattr(SCREAMING_SNAKE_CASE				, "encoder"							) and hasattr(SCREAMING_SNAKE_CASE				, "decoder"							))
  					    or "decoder_input_ids" in tf_dummy_inputs
  					):
  							snake_case		:						Any								=				np.asarray([[1], [1]]				, dtype=SCREAMING_SNAKE_CASE							) * (pt_model.config.decoder_start_token_id or 0)
  							pt_input.update({"decoder_input_ids": torch.tensor(SCREAMING_SNAKE_CASE							)}							)
  							tf_input.update({"decoder_input_ids": tf.convert_to_tensor(SCREAMING_SNAKE_CASE							)}							)
  
  					return pt_input, tf_input
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""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
  					snake_case		:						Optional[int]								=				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
  					snake_case		:						List[str]								=				AutoConfig.from_pretrained(self._local_dir							)
  					snake_case		:						Tuple								=				config.architectures
  					if self._override_model_class is not None:
  							if self._override_model_class.startswith("TF"							):
  									snake_case		:						int								=				[self._override_model_class[2:]]
  							else:
  									snake_case		:						Union[str, Any]								=				[self._override_model_class]
  							try:
  									snake_case		:						str								=				getattr(import_module("transformers"							)				, architectures[0]							)
  							except AttributeError:
  									raise ValueError(F'''Model class {self._override_model_class} not found in transformers.'''							)
  							try:
  									snake_case		:						str								=				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
  							snake_case		:						Tuple								=				getattr(import_module("transformers"							)				, "AutoModel"							)
  							snake_case		:						Any								=				getattr(import_module("transformers"							)				, "TFAutoModel"							)
  							self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"							)
  					else:  # Architecture defined -- use it
  							if len(SCREAMING_SNAKE_CASE							) > 1:
  									raise ValueError(F'''More than one architecture was found, aborting. (architectures = {architectures})'''							)
  							self._logger.warning(F'''Detected architecture: {architectures[0]}'''							)
  							snake_case		:						Optional[int]								=				getattr(import_module("transformers"							)				, architectures[0]							)
  							try:
  									snake_case		:						Dict								=				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
  					snake_case		:						List[str]								=				tf_class.from_config(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				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
  					snake_case		:						List[Any]								=				pt_class.from_pretrained(self._local_dir							)
  					pt_model.eval()
  
  					snake_case		:						Union[str, Any]								=				self.get_inputs(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  					with torch.no_grad():
  							snake_case		:						Tuple								=				pt_model(**SCREAMING_SNAKE_CASE				, output_hidden_states=SCREAMING_SNAKE_CASE							)
  					del pt_model  # will no longer be used, and may have a large memory footprint
  
  					snake_case		:						Dict								=				tf_class.from_pretrained(self._local_dir				, from_pt=SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[Any]								=				tf_from_pt_model(**SCREAMING_SNAKE_CASE				, output_hidden_states=SCREAMING_SNAKE_CASE				, training=SCREAMING_SNAKE_CASE							)
  
  					# Confirms that cross loading PT weights into TF worked.
  					snake_case		:						Optional[int]								=				self.find_pt_tf_differences(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						Any								=				{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
  					snake_case		:						List[str]								=				{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
  					if len(SCREAMING_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\'"							)
  					snake_case		:						Union[str, Any]								=				max(output_differences.values()							) if output_differences else 0.0
  					snake_case		:						Tuple								=				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
  					snake_case		:						Any								=				os.path.join(self._local_dir				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						int								=				os.path.join(self._local_dir				, SCREAMING_SNAKE_CASE							)
  					if (not os.path.exists(SCREAMING_SNAKE_CASE							) and not os.path.exists(SCREAMING_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
  
  					snake_case		:						int								=				tf_class.from_pretrained(self._local_dir							)
  					snake_case		:						Optional[int]								=				tf_model(**SCREAMING_SNAKE_CASE				, output_hidden_states=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						int								=				self.find_pt_tf_differences(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
  					snake_case		:						int								=				{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
  					if len(SCREAMING_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\'"							)
  					snake_case		:						Optional[int]								=				max(output_differences.values()							) if output_differences else 0.0
  					snake_case		:						int								=				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]							)							)
  
  					snake_case		:						Tuple								=				'''Update TF weights''' if self._new_weights else '''Add TF weights'''
  					if self._push:
  							repo.git_add(auto_lfs_track=SCREAMING_SNAKE_CASE							)
  							repo.git_commit(SCREAMING_SNAKE_CASE							)
  							repo.git_push(blocking=SCREAMING_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..."							)
  							snake_case		:						Any								=				(
  							    '''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(SCREAMING_SNAKE_CASE							):
  									snake_case		:						List[str]								=				[
  									    CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE				, path_or_fileobj=SCREAMING_SNAKE_CASE							)
  									]
  									for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"							):
  											operations += [
  											    CommitOperationAdd(path_in_repo=os.path.basename(SCREAMING_SNAKE_CASE							)				, path_or_fileobj=SCREAMING_SNAKE_CASE							)
  											]
  							else:
  									snake_case		:						Any								=				[CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE				, path_or_fileobj=SCREAMING_SNAKE_CASE							)]
  
  							snake_case		:						List[str]								=				create_commit(
  							    repo_id=self._model_name				, operations=SCREAMING_SNAKE_CASE				, commit_message=SCREAMING_SNAKE_CASE				, commit_description=SCREAMING_SNAKE_CASE				, repo_type="model"				, create_pr=SCREAMING_SNAKE_CASE				, ).pr_url
  							self._logger.warning(F'''PR open in {hub_pr_url}'''							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import random
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
  
  		def is_sorted(lowercase__  :     int   ):
  				for i in range(len(lowercase__   ) - 1   ):
  						if collection[i] > collection[i + 1]:
  								return False
  				return True
  
  		while not is_sorted(lowercase__   ):
  				random.shuffle(lowercase__   )
  		return collection
  
  
  if __name__ == "__main__":
  							__A 		=			input("Enter numbers separated by a comma:\n").strip()
  							__A 		=			[int(item) for item in user_input.split(",")]
  							print(bogo_sort(unsorted))
  
  