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