





"""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()
lowerCamelCase_	:		Optional[int]         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[Any]         =   'Hello world! cécé herlolip'





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_		,			A_    :			Optional[Any]									=    os.path.split(_UpperCAmelCase   )
  A_    :			Optional[int]									=    DataaVecTextModel.from_pretrained(
      _UpperCAmelCase					,   checkpoint_file=_UpperCAmelCase   )
  dataavec.eval()  # disable dropout
  A_    :			int									=    dataavec.models[0]
  A_    :			int									=    dataavec_model.encoder.sentence_encoder
  A_    :			List[str]									=    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:
    A_    :			List[Any]									=    dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our BERT config:'					,   _UpperCAmelCase   )

  A_    :			Union[str, Any]									=    DataaVecTextForSequenceClassification(_UpperCAmelCase   ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase   )
  model.eval()

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

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

    # self attention
    A_    :			BertSelfAttention									=    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)   )}"""
    )

    A_    :			Union[str, Any]									=    dataavec_layer.self_attn.q_proj.weight
    A_    :			int									=    dataavec_layer.self_attn.q_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn.k_proj.weight
    A_    :			Optional[int]									=    dataavec_layer.self_attn.k_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn.v_proj.weight
    A_    :			Optional[Any]									=    dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    A_    :			BertSelfOutput									=    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}"""
    A_    :			Dict									=    dataavec_layer.self_attn.out_proj.weight
    A_    :			Any									=    dataavec_layer.self_attn.out_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn_layer_norm.weight
    A_    :			Optional[Any]									=    dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    A_    :			BertIntermediate									=    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}"""
    A_    :			Optional[Any]									=    dataavec_layer.fca.weight
    A_    :			Any									=    dataavec_layer.fca.bias

    # output
    A_    :			BertOutput									=    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}"""
    A_    :			Optional[Any]									=    dataavec_layer.fca.weight
    A_    :			Union[str, Any]									=    dataavec_layer.fca.bias
    A_    :			Optional[int]									=    dataavec_layer.final_layer_norm.weight
    A_    :			Tuple									=    dataavec_layer.final_layer_norm.bias
    # end of layer

  if classification_head:
    A_    :			Optional[Any]									=    dataavec.model.classification_heads['mnli'].dense.weight
    A_    :			Tuple									=    dataavec.model.classification_heads['mnli'].dense.bias
    A_    :			List[str]									=    dataavec.model.classification_heads['mnli'].out_proj.weight
    A_    :			Any									=    dataavec.model.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    A_    :			str									=    dataavec_model.encoder.lm_head.dense.weight
    A_    :			Optional[Any]									=    dataavec_model.encoder.lm_head.dense.bias
    A_    :			Tuple									=    dataavec_model.encoder.lm_head.layer_norm.weight
    A_    :			List[Any]									=    dataavec_model.encoder.lm_head.layer_norm.bias
    A_    :			Optional[int]									=    dataavec_model.encoder.lm_head.weight
    A_    :			Dict									=    dataavec_model.encoder.lm_head.bias

  # Let's check that we get the same results.
  A_    :			torch.Tensor									=    dataavec.encode(_UpperCAmelCase   ).unsqueeze(0   )  # batch of size 1

  A_    :			Dict									=    model(_UpperCAmelCase   )[0]
  if classification_head:
    A_    :			List[Any]									=    dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase   )   )
  else:
    A_    :			Optional[Any]									=    dataavec_model(_UpperCAmelCase   )[0]
  print(our_output.shape					,   their_output.shape   )
  A_    :			str									=    torch.max(torch.abs(our_output - their_output   )   ).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
  A_    :			List[Any]									=    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__":
   lowerCamelCase_	:		str         =   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.'
   )
   lowerCamelCase_	:		Any         =   parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['CamembertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)