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
  
  
  lowerCamelCase_     :     Dict    		=						5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  def _A		(		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    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A		)      ->  List[str]:
       a					=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=__A				,					required=__A				,					help='''The model name, including owner/organization, as seen on the hub.'''				,					)
       train_parser.add_argument(
           '''--local-dir'''				,					type=__A				,					default=''''''				,					help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''				,					)
       train_parser.add_argument(
           '''--max-error'''				,					type=__A				,					default=__A				,					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=__A				,					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=__A				,					default=__A				,					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=__A		)
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A				,					__A		)      ->  Tuple:
       a					=set(pt_outputs.keys()		)
       a					=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(__A				,					__A				,					__A				,					__A=""		):
           # 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(__A				,					torch.Tensor		):
               a					=np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
               a					=tensor_difference
           else:
               a					=attr_name
               for i, pt_item in enumerate(__A		):
                   # If it is a named attribute, we keep the name. Otherwise, just its index.
                   if isinstance(__A				,					__A		):
                       a					=root_name + pt_item
                       a					=tf_out[pt_item]
                       a					=pt_out[pt_item]
                   else:
                       a					=root_name + f'''[{i}]'''
                       a					=tf_out[i]
                   a					=_find_pt_tf_differences(__A				,					__A				,					__A				,					__A		)
  
           return differences
  
       return _find_pt_tf_differences(__A				,					__A				,					{}		)
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					*__A				,					)      ->  Optional[int]:
       a					=logging.get_logger('''transformers-cli/pt_to_tf'''		)
       a					=model_name
       a					=local_dir if local_dir else os.path.join('''/tmp'''				,					__A		)
       a					=max_error
       a					=new_weights
       a					=no_pr
       a					=push
       a					=extra_commit_description
       a					=override_model_class
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  Optional[int]:
  
       def _get_audio_input():
           a					=load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,					'''clean'''				,					split='''validation'''		)
           a					=ds.sort('''id'''		).select(range(2		)		)[:2]['''audio''']
           a					=[x['''array'''] for x in speech_samples]
           return raw_samples
  
       a					=type(pt_model.config		)
       if model_config_class in PROCESSOR_MAPPING:
           a					=AutoProcessor.from_pretrained(self._local_dir		)
           if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
               a					=processor.tokenizer.eos_token
       elif model_config_class in IMAGE_PROCESSOR_MAPPING:
           a					=AutoImageProcessor.from_pretrained(self._local_dir		)
       elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
           a					=AutoFeatureExtractor.from_pretrained(self._local_dir		)
       elif model_config_class in TOKENIZER_MAPPING:
           a					=AutoTokenizer.from_pretrained(self._local_dir		)
           if processor.pad_token is None:
               a					=processor.eos_token
       else:
           raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''		)
  
       a					=set(inspect.signature(pt_model.forward		).parameters.keys()		)
       a					={}
       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:
           a					=load_dataset('''cifar10'''				,					'''plain_text'''				,					split='''test'''		)[:2]['''img''']
           processor_inputs.update({'''images''': sample_images}		)
       if "input_features" in model_forward_signature:
           a					=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:
               a					=feature_extractor_signature['''padding'''].default
               if default_strategy is not False and default_strategy is not None:
                   a					=default_strategy
               else:
                   a					=True
           else:
               a					=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}		)
       a					=processor(**__A				,					return_tensors='''pt'''		)
       a					=processor(**__A				,					return_tensors='''tf'''		)
  
       # Extra input requirements, in addition to the input modality
       if (
           config.is_encoder_decoder
           or (hasattr(__A				,					'''encoder'''		) and hasattr(__A				,					'''decoder'''		))
           or "decoder_input_ids" in tf_dummy_inputs
       ):
           a					=np.asarray([[1], [1]]				,					dtype=__A		) * (pt_model.config.decoder_start_token_id or 0)
           pt_input.update({'''decoder_input_ids''': torch.tensor(__A		)}		)
           tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(__A		)}		)
  
       return pt_input, tf_input
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
       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
       a					=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
       a					=AutoConfig.from_pretrained(self._local_dir		)
       a					=config.architectures
       if self._override_model_class is not None:
           if self._override_model_class.startswith('''TF'''		):
               a					=[self._override_model_class[2:]]
           else:
               a					=[self._override_model_class]
           try:
               a					=getattr(import_module('''transformers'''		)				,					architectures[0]		)
           except AttributeError:
               raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''		)
           try:
               a					=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
           a					=getattr(import_module('''transformers'''		)				,					'''AutoModel'''		)
           a					=getattr(import_module('''transformers'''		)				,					'''TFAutoModel'''		)
           self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''		)
       else:  # Architecture defined -- use it
           if len(__A		) > 1:
               raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''		)
           self._logger.warning(f'''Detected architecture: {architectures[0]}'''		)
           a					=getattr(import_module('''transformers'''		)				,					architectures[0]		)
           try:
               a					=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
       a					=tf_class.from_config(__A		)
       a					=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
       a					=pt_class.from_pretrained(self._local_dir		)
       pt_model.eval()
  
       a	,   a					=self.get_inputs(__A				,					__A				,					__A		)
  
       with torch.no_grad():
           a					=pt_model(**__A				,					output_hidden_states=__A		)
       del pt_model  # will no longer be used, and may have a large memory footprint
  
       a					=tf_class.from_pretrained(self._local_dir				,					from_pt=__A		)
       a					=tf_from_pt_model(**__A				,					output_hidden_states=__A				,					training=__A		)
  
       # Confirms that cross loading PT weights into TF worked.
       a					=self.find_pt_tf_differences(__A				,					__A		)
       a					={k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
       a					={k: v for k, v in crossload_differences.items() if '''hidden''' in k}
       if len(__A		) == 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\''''		)
       a					=max(output_differences.values()		) if output_differences else 0.0
       a					=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
       a					=os.path.join(self._local_dir				,					__A		)
       a					=os.path.join(self._local_dir				,					__A		)
       if (not os.path.exists(__A		) and not os.path.exists(__A		)) 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
  
       a					=tf_class.from_pretrained(self._local_dir		)
       a					=tf_model(**__A				,					output_hidden_states=__A		)
  
       a					=self.find_pt_tf_differences(__A				,					__A		)
       a					={k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
       a					={k: v for k, v in conversion_differences.items() if '''hidden''' in k}
       if len(__A		) == 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\''''		)
       a					=max(output_differences.values()		) if output_differences else 0.0
       a					=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]		)		)
  
       a					='''Update TF weights''' if self._new_weights else '''Add TF weights'''
       if self._push:
           repo.git_add(auto_lfs_track=__A		)
           repo.git_commit(__A		)
           repo.git_push(blocking=__A		)  # 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...'''		)
           a					=(
               '''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(__A		):
               a					=[
                   CommitOperationAdd(path_in_repo=__A				,					path_or_fileobj=__A		)
               ]
               for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''		):
                   operations += [
                       CommitOperationAdd(path_in_repo=os.path.basename(__A		)				,					path_or_fileobj=__A		)
                   ]
           else:
               a					=[CommitOperationAdd(path_in_repo=__A				,					path_or_fileobj=__A		)]
  
           a					=create_commit(
               repo_id=self._model_name				,					operations=__A				,					commit_message=__A				,					commit_description=__A				,					repo_type='''model'''				,					create_pr=__A				,					).pr_url
           self._logger.warning(f'''PR open in {hub_pr_url}'''		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=TaConfig.from_pretrained(lowercase	)
      a					=FlaxTaForConditionalGeneration(config=lowercase	)
      a					=checkpoints.load_tax_checkpoint(lowercase	)
  
      a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
      # Encoder
      for layer_index in range(config.num_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
          if split_mlp_wi:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_attention_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
          a					=tax_mlp_layer_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_encoder_rel_embedding
  
      # Assigning
      a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      a					=tax_encoder_norm
  
      # Decoder
      for layer_index in range(config.num_decoder_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]
  
          # Encoder-Decoder-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
          # MLP
          if split_mlp_wi:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_pre_attention_layer_norm
  
          a					=tax_enc_dec_attention_key
          a					=tax_enc_dec_attention_out
          a					=tax_enc_dec_attention_query
          a					=tax_enc_dec_attention_value
  
          a					=tax_cross_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
  
          a					=txa_mlp_layer_norm
  
      # Decoder Normalization
      a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      a					=txa_decoder_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_decoder_rel_embedding
  
      # Token Embeddings
      a					=tax_model['''target''']['''token_embedder''']['''embedding''']
      a					=txa_token_embeddings
  
      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
          a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
      flax_model.save_pretrained(lowercase	)
      print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
         )
         parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
         parser.add_argument(
             """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
         )
         lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)