

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

__a						:int            = 'Hello world! cécé herlolip'

def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							os.path.split(__UpperCamelCase						)
      A_	     =							DataaVecTextModel.from_pretrained(
          __UpperCamelCase							,checkpoint_file=__UpperCamelCase						)
      dataavec.eval()  # disable dropout
      A_	     =							dataavec.models[0]
      A_	     =							dataavec_model.encoder.sentence_encoder
      A_	     =							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_	     =							dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
      print("Our BERT config:"							,__UpperCamelCase						)

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

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

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

            # self attention
            A_	     =							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_	     =							dataavec_layer.self_attn.q_proj.weight
            A_	     =							dataavec_layer.self_attn.q_proj.bias
            A_	     =							dataavec_layer.self_attn.k_proj.weight
            A_	     =							dataavec_layer.self_attn.k_proj.bias
            A_	     =							dataavec_layer.self_attn.v_proj.weight
            A_	     =							dataavec_layer.self_attn.v_proj.bias

            # self-attention output
            A_	     =							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_	     =							dataavec_layer.self_attn.out_proj.weight
            A_	     =							dataavec_layer.self_attn.out_proj.bias
            A_	     =							dataavec_layer.self_attn_layer_norm.weight
            A_	     =							dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            A_	     =							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_	     =							dataavec_layer.fca.weight
            A_	     =							dataavec_layer.fca.bias

            # output
            A_	     =							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_	     =							dataavec_layer.fca.weight
            A_	     =							dataavec_layer.fca.bias
            A_	     =							dataavec_layer.final_layer_norm.weight
            A_	     =							dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

      A_	     =							model(__UpperCamelCase						)[0]
      if classification_head:
            A_	     =							dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase						)						)
      else:
            A_	     =							dataavec_model(__UpperCamelCase						)[0]
      print(our_output.shape							,their_output.shape						)
      A_	     =							torch.max(torch.abs(our_output - their_output						)						).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
      A_	     =							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						:int            = 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						:str            = 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_mobilenet_va import MobileNetVaImageProcessor


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

class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							List[Any]	, *UpperCAmelCase       :							Any	, **UpperCAmelCase       :							int  ):
            warnings.warn(
                "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                " Please use MobileNetV2ImageProcessor instead."	, UpperCAmelCase	, )
            super().__init__(*UpperCAmelCase	, **UpperCAmelCase  )