





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

import huggingface_hub
import numpy as np
from packaging import version

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


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


lowerCAmelCase_        =			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def 			lowerCamelCase_		(					lowerCAmelCase:		Namespace )->						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			(	UpperCAmelCase_							):


  '''simple docstring'''







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    ArgumentParser							):


    '''simple docstring'''





    _snake_case      : 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=UpperCamelCase					, required=UpperCamelCase					, help='The model name, including owner/organization, as seen on the hub.'					, )
    train_parser.add_argument(
        '--local-dir'					, type=UpperCamelCase					, default=''					, help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'					, )
    train_parser.add_argument(
        '--max-error'					, type=UpperCamelCase					, default=UpperCamelCase					, 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=UpperCamelCase					, 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=UpperCamelCase					, default=UpperCamelCase					, 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=UpperCamelCase							)







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    Optional[int]					, UpperCamelCase					:    int							):


    '''simple docstring'''





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

      return differences

    return _find_pt_tf_differences(UpperCamelCase					, UpperCamelCase					, {}							)







  def __init__(					self					:    Optional[int]					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    float					, UpperCamelCase					:    bool					, UpperCamelCase					:    bool					, UpperCamelCase					:    bool					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, *UpperCamelCase					:    int					, ):


    '''simple docstring'''





    _snake_case      : List[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'					, UpperCamelCase							)
    _snake_case      : Optional[int]														=		max_error
    _snake_case      : Union[str, Any]														=		new_weights
    _snake_case      : str														=		no_pr
    _snake_case      : Any														=		push
    _snake_case      : Optional[Any]														=		extra_commit_description
    _snake_case      : Union[str, Any]														=		override_model_class







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    Any					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Optional[int]							):


    '''simple docstring'''





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

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

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

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

    return pt_input, tf_input







  def UpperCamelCase_       (					self					:    int							):


    '''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[Any]														=		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      : Any														=		AutoConfig.from_pretrained(self._local_dir							)
    _snake_case      : Optional[Any]														=		config.architectures
    if self._override_model_class is not None:
      if self._override_model_class.startswith('TF'							):
        _snake_case      : Tuple														=		[self._override_model_class[2:]]
      else:
        _snake_case      : Tuple														=		[self._override_model_class]
      try:
        _snake_case      : int														=		getattr(import_module('transformers'							)					, architectures[0]							)
      except AttributeError:
        raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
      try:
        _snake_case      : Optional[int]														=		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      : Any														=		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(UpperCamelCase							) > 1:
        raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})"""							)
      self._logger.warning(f"""Detected architecture: {architectures[0]}"""							)
      _snake_case      : str														=		getattr(import_module('transformers'							)					, architectures[0]							)
      try:
        _snake_case      : int														=		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      : Dict														=		tf_class.from_config(UpperCamelCase							)
    _snake_case      : 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
    _snake_case      : Dict														=		pt_class.from_pretrained(self._local_dir							)
    pt_model.eval()

    _snake_case							,    _snake_case      : str														=		self.get_inputs(UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

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

    _snake_case      : str														=		tf_class.from_pretrained(self._local_dir					, from_pt=UpperCamelCase							)
    _snake_case      : Dict														=		tf_from_pt_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase					, training=UpperCamelCase							)

    # Confirms that cross loading PT weights into TF worked.
    _snake_case      : Tuple														=		self.find_pt_tf_differences(UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Any														=		{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
    _snake_case      : int														=		{k: v for k, v in crossload_differences.items() if 'hidden' in k}
    if len(UpperCamelCase							) == 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[Any]														=		max(output_differences.values()							) if output_differences else 0.0
    _snake_case      : int														=		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					, UpperCamelCase							)
    _snake_case      : List[Any]														=		os.path.join(self._local_dir					, UpperCamelCase							)
    if (not os.path.exists(UpperCamelCase							) and not os.path.exists(UpperCamelCase							)) 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      : Any														=		tf_class.from_pretrained(self._local_dir							)
    _snake_case      : Union[str, Any]														=		tf_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase							)

    _snake_case      : List[str]														=		self.find_pt_tf_differences(UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Any														=		{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
    _snake_case      : Union[str, Any]														=		{k: v for k, v in conversion_differences.items() if 'hidden' in k}
    if len(UpperCamelCase							) == 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[Any]														=		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      : int														=		'Update TF weights' if self._new_weights else 'Add TF weights'
    if self._push:
      repo.git_add(auto_lfs_track=UpperCamelCase							)
      repo.git_commit(UpperCamelCase							)
      repo.git_push(blocking=UpperCamelCase							)  # 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      : Optional[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(UpperCamelCase							):
        _snake_case      : int														=		[
            CommitOperationAdd(path_in_repo=UpperCamelCase					, path_or_fileobj=UpperCamelCase							)
        ]
        for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'							):
          operations += [
              CommitOperationAdd(path_in_repo=os.path.basename(UpperCamelCase							)					, path_or_fileobj=UpperCamelCase							)
          ]
      else:
        _snake_case      : List[Any]														=		[CommitOperationAdd(path_in_repo=UpperCamelCase					, path_or_fileobj=UpperCamelCase							)]

      _snake_case      : Dict														=		create_commit(
          repo_id=self._model_name					, operations=UpperCamelCase					, commit_message=UpperCamelCase					, commit_description=UpperCamelCase					, repo_type='model'					, create_pr=UpperCamelCase					, ).pr_url
      self._logger.warning(f"""PR open in {hub_pr_url}"""							)






"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
			raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
__A       						=       logging.get_logger(__name__)

__A       						=       'Hello world! cécé herlolip'



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings    , hidden_size=dataavec_model.args.encoder_embed_dim    , num_hidden_layers=dataavec_model.args.encoder_layers    , num_attention_heads=dataavec_model.args.encoder_attention_heads    , intermediate_size=dataavec_model.args.encoder_ffn_embed_dim    , max_position_embeddings=514    , type_vocab_size=1    , layer_norm_eps=1E-5    , )
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers       ):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase       ).unsqueeze(0       )  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase       )[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase       )       )
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase       )[0]
	print(our_output.shape    , their_output.shape       )
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output       )       ).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
	_lowerCAmelCase								=torch.allclose(__UpperCamelCase    , __UpperCamelCase    , atol=1E-3       )
	print("""Do both models output the same tensors?"""    , """🔥""" if success else """💩"""       )
	if not success:
		raise Exception("""Something went wRoNg"""       )

	pathlib.Path(__UpperCamelCase       ).mkdir(parents=__UpperCamelCase    , exist_ok=__UpperCamelCase       )
	print(F'''Saving model to {pytorch_dump_folder_path}'''       )
	model.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
			__A       						=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			__A       						=       parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



