




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__				:     List[str]			       =							logging.get_logger(__name__)

lowerCamelCase__				:     int			       =							'Hello world! cécé herlolip'

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			bool							)      ->				Optional[Any]:
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    os.path.split(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    DataaVecTextModel.from_pretrained(
          __UpperCAmelCase    ,    checkpoint_file=__UpperCAmelCase							)
      dataavec.eval()  # disable dropout
      SCREAMING_SNAKE_CASE_					=    dataavec.models[0]
      SCREAMING_SNAKE_CASE_					=    dataavec_model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE_					=    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=5_14    ,    type_vocab_size=1    ,    layer_norm_eps=1E-5    ,    )
      if classification_head:
            SCREAMING_SNAKE_CASE_					=    dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
      print('Our BERT config:'    ,    __UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_					=    DataaVecTextForSequenceClassification(__UpperCAmelCase							) if classification_head else DataaVecTextForMaskedLM(__UpperCAmelCase							)
      model.eval()

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

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

            # self attention
            SCREAMING_SNAKE_CASE_					=    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)							)}"
            )

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

            # self-attention output
            SCREAMING_SNAKE_CASE_					=    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}"
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.self_attn.out_proj.weight
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.self_attn.out_proj.bias
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.self_attn_layer_norm.weight
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            SCREAMING_SNAKE_CASE_					=    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}"
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.fca.weight
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.fca.bias

            # output
            SCREAMING_SNAKE_CASE_					=    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}"
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.fca.weight
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.fca.bias
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.final_layer_norm.weight
            SCREAMING_SNAKE_CASE_					=    dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

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





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase__				:     Optional[Any]			       =							logging.get_logger(__name__)





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def __init__(							self						:	Dict       ,       *_lowerCAmelCase						:	str       ,       **_lowerCAmelCase						:	List[str]		):
       warnings.warn(
           'The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use DetrImageProcessor instead.'       ,       _lowerCAmelCase       ,       )
       super().__init__(*_lowerCAmelCase       ,       **_lowerCAmelCase		)