
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


UpperCAmelCase__						:	Optional[int]	     = 5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors






def     lowerCamelCase__				(   a ) ->  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 UpperCAmelCase  (		__snake_case  ):





   '''simple docstring'''


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				Optional[Any]	):

      """simple docstring"""
      _A:    Optional[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=__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 					__magic_name__							(   lowerCAmelCase_	:				Union[str, Any]     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    int						     =			set(pt_outputs.keys()	)
      _A:    int						     =			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_	:				List[str]     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				Optional[Any]     ,     lowerCAmelCase_	:				Optional[Any]=""	):
         # 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	):
            _A:    Optional[Any]						     =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()	)	)
            _A:    List[str]						     =			tensor_difference
         else:
            _A:    Optional[int]						     =			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	):
                  _A:    List[Any]						     =			root_name + pt_item
                  _A:    List[Any]						     =			tf_out[pt_item]
                  _A:    int						     =			pt_out[pt_item]
               else:
                  _A:    Dict						     =			root_name + F"""[{i}]"""
                  _A:    Tuple						     =			tf_out[i]
               _A:    Any						     =			_find_pt_tf_differences(__lowerCamelCase     ,     __lowerCamelCase     ,     __lowerCamelCase     ,     __lowerCamelCase	)

         return differences

      return _find_pt_tf_differences(__lowerCamelCase     ,     __lowerCamelCase     ,     {}	)


   def __init__(   self	:				str     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				Union[str, Any]     ,     lowerCAmelCase_	:				List[Any]     ,     lowerCAmelCase_	:				Optional[Any]     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				Dict     ,     *lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			logging.get_logger('''transformers-cli/pt_to_tf'''	)
      _A:    Optional[Any]						     =			model_name
      _A:    str						     =			local_dir if local_dir else os.path.join('''/tmp'''     ,     __lowerCamelCase	)
      _A:    Tuple						     =			max_error
      _A:    Optional[int]						     =			new_weights
      _A:    int						     =			no_pr
      _A:    int						     =			push
      _A:    Any						     =			extra_commit_description
      _A:    List[str]						     =			override_model_class


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				List[str]	):

      """simple docstring"""







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

      _A:    List[str]						     =			type(pt_model.config	)
      if model_config_class in PROCESSOR_MAPPING:
         _A:    Union[str, Any]						     =			AutoProcessor.from_pretrained(self._local_dir	)
         if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
            _A:    Union[str, Any]						     =			processor.tokenizer.eos_token
      elif model_config_class in IMAGE_PROCESSOR_MAPPING:
         _A:    List[str]						     =			AutoImageProcessor.from_pretrained(self._local_dir	)
      elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
         _A:    Union[str, Any]						     =			AutoFeatureExtractor.from_pretrained(self._local_dir	)
      elif model_config_class in TOKENIZER_MAPPING:
         _A:    List[Any]						     =			AutoTokenizer.from_pretrained(self._local_dir	)
         if processor.pad_token is None:
            _A:    List[str]						     =			processor.eos_token
      else:
         raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""	)

      _A:    str						     =			set(inspect.signature(pt_model.forward	).parameters.keys()	)
      _A:    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:
         _A:    Any						     =			load_dataset('''cifar10'''     ,     '''plain_text'''     ,     split='''test'''	)[:2]["""img"""]
         processor_inputs.update({'''images''': sample_images}	)
      if "input_features" in model_forward_signature:
         _A:    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:
            _A:    str						     =			feature_extractor_signature["""padding"""].default
            if default_strategy is not False and default_strategy is not None:
               _A:    Tuple						     =			default_strategy
            else:
               _A:    Optional[Any]						     =			True
         else:
            _A:    Union[str, 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}	)
      _A:    str						     =			processor(**__lowerCamelCase     ,     return_tensors='''pt'''	)
      _A:    Optional[int]						     =			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
      ):
         _A:    Optional[int]						     =			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 					__magic_name__							(   self	:				Union[str, Any]	):

      """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
      _A:    Tuple						     =			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:    str						     =			AutoConfig.from_pretrained(self._local_dir	)
      _A:    str						     =			config.architectures
      if self._override_model_class is not None:
         if self._override_model_class.startswith('''TF'''	):
            _A:    Any						     =			[self._override_model_class[2:]]
         else:
            _A:    Optional[Any]						     =			[self._override_model_class]
         try:
            _A:    Dict						     =			getattr(import_module('''transformers'''	)     ,     architectures[0]	)
         except AttributeError:
            raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""	)
         try:
            _A:    List[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
         _A:    Any						     =			getattr(import_module('''transformers'''	)     ,     '''AutoModel'''	)
         _A:    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]}"""	)
         _A:    Dict						     =			getattr(import_module('''transformers'''	)     ,     architectures[0]	)
         try:
            _A:    List[Any]						     =			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:    Optional[Any]						     =			tf_class.from_config(__lowerCamelCase	)
      _A:    Tuple						     =			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:    List[Any]						     =			pt_class.from_pretrained(self._local_dir	)
      pt_model.eval()

      _A:    Any						     =			self.get_inputs(__lowerCamelCase     ,     __lowerCamelCase     ,     __lowerCamelCase	)

      with torch.no_grad():
         _A:    Optional[Any]						     =			pt_model(**__lowerCamelCase     ,     output_hidden_states=__lowerCamelCase	)
      del pt_model  # will no longer be used, and may have a large memory footprint

      _A:    Optional[Any]						     =			tf_class.from_pretrained(self._local_dir     ,     from_pt=__lowerCamelCase	)
      _A:    str						     =			tf_from_pt_model(**__lowerCamelCase     ,     output_hidden_states=__lowerCamelCase     ,     training=__lowerCamelCase	)

      # Confirms that cross loading PT weights into TF worked.
      _A:    List[str]						     =			self.find_pt_tf_differences(__lowerCamelCase     ,     __lowerCamelCase	)
      _A:    List[str]						     =			{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
      _A:    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\''''	)
      _A:    Any						     =			max(output_differences.values()	) if output_differences else 0.0
      _A:    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
      _A:    List[Any]						     =			os.path.join(self._local_dir     ,     __lowerCamelCase	)
      _A:    Tuple						     =			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

      _A:    int						     =			tf_class.from_pretrained(self._local_dir	)
      _A:    Tuple						     =			tf_model(**__lowerCamelCase     ,     output_hidden_states=__lowerCamelCase	)

      _A:    Any						     =			self.find_pt_tf_differences(__lowerCamelCase     ,     __lowerCamelCase	)
      _A:    List[str]						     =			{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
      _A:    Union[str, Any]						     =			{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\''''	)
      _A:    Optional[Any]						     =			max(output_differences.values()	) if output_differences else 0.0
      _A:    Any						     =			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:    Dict						     =			"""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...'''	)
         _A:    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(__lowerCamelCase	):
            _A:    Optional[Any]						     =			[
                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:
            _A:    List[str]						     =			[CommitOperationAdd(path_in_repo=__lowerCamelCase     ,     path_or_fileobj=__lowerCamelCase	)]

         _A:    Optional[Any]						     =			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}"""	)




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def   a_					(_lowerCAmelCase  :   Tuple       )->				List[Any]:
    if "model" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""model."""			,	""""""       )
    if "norm1" in orig_key:
        snake_case:						int         =   orig_key.replace("""norm1"""			,	"""attention.output.LayerNorm"""       )
    if "norm2" in orig_key:
        snake_case:						Tuple         =   orig_key.replace("""norm2"""			,	"""output.LayerNorm"""       )
    if "norm" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""norm"""			,	"""LayerNorm"""       )
    if "transformer" in orig_key:
        snake_case:						Any         =   orig_key.split("""."""       )[0].split("""_"""       )[-1]
        snake_case:						List[Any]         =   orig_key.replace(F"transformer_{layer_num}"			,	F"encoder.layer.{layer_num}"       )
    if "mha.attn" in orig_key:
        snake_case:						List[Any]         =   orig_key.replace("""mha.attn"""			,	"""attention.self"""       )
    if "mha" in orig_key:
        snake_case:						str         =   orig_key.replace("""mha"""			,	"""attention"""       )
    if "W_q" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_q"""			,	"""self.query"""       )
    if "W_k" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_k"""			,	"""self.key"""       )
    if "W_v" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_v"""			,	"""self.value"""       )
    if "ff.0" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""ff.0"""			,	"""intermediate.dense"""       )
    if "ff.2" in orig_key:
        snake_case:						Optional[Any]         =   orig_key.replace("""ff.2"""			,	"""output.dense"""       )
    if "ff" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""ff"""			,	"""output.dense"""       )
    if "mlm_class" in orig_key:
        snake_case:						int         =   orig_key.replace("""mlm.mlm_class"""			,	"""cls.predictions.decoder"""       )
    if "mlm" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""mlm"""			,	"""cls.predictions.transform"""       )
    if "backbone.backbone.encoders" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""backbone.backbone.encoders"""			,	"""encoder.layer"""       )
    if "cls" not in orig_key:
        snake_case:						Dict         =   """mra.""" + orig_key

    return orig_key

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Any       )->				Dict:
    for key in orig_state_dict.copy().keys():
        snake_case:						Tuple         =   orig_state_dict.pop(_lowerCAmelCase       )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            snake_case:						int         =   val

    snake_case:						str         =   orig_state_dict["""cls.predictions.decoder.bias"""]
    snake_case:						List[str]         =   torch.arange(_lowerCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   Any       )->				List[Any]:
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )["""model_state_dict"""]
    snake_case:						Optional[int]         =   MraConfig.from_json_file(_lowerCAmelCase       )
    snake_case:						List[str]         =   MraForMaskedLM(_lowerCAmelCase       )

    snake_case:						Any         =   convert_checkpoint_helper(config.max_position_embeddings			,	_lowerCAmelCase       )

    print(model.load_state_dict(_lowerCAmelCase       )       )
    model.eval()
    model.save_pretrained(_lowerCAmelCase       )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, Any]										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Mra model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __lowerCAmelCase    :  Dict										= parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


