




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

__SCREAMING_SNAKE_CASE    :   str       ="""Hello world! cécé herlolip"""

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       str     ,				lowerCamelCase:       bool						)					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__  ,		lowerCamelCase__        =				os.path.split(lowerCamelCase						)
      lowerCamelCase__        =				DataaVecTextModel.from_pretrained(
          lowerCamelCase     ,				checkpoint_file=lowerCamelCase						)
      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=5_14     ,				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:'     ,				lowerCamelCase						)

      lowerCamelCase__        =				DataaVecTextForSequenceClassification(lowerCamelCase						) if classification_head else DataaVecTextForMaskedLM(lowerCamelCase						)
      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(lowerCamelCase						).unsqueeze(0						)  # batch of size 1

      lowerCamelCase__        =				model(lowerCamelCase						)[0]
      if classification_head:
            lowerCamelCase__        =				dataavec.model.classification_heads['mnli'](dataavec.extract_features(lowerCamelCase						)						)
      else:
            lowerCamelCase__        =				dataavec_model(lowerCamelCase						)[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(lowerCamelCase     ,				lowerCamelCase     ,				atol=1e-3						)
      print('Do both models output the same tensors?'     ,				'🔥' if success else '💩'						)
      if not success:
            raise Exception('Something went wRoNg'						)

      pathlib.Path(lowerCamelCase						).mkdir(parents=lowerCamelCase     ,				exist_ok=lowerCamelCase						)
      print(f"""Saving model to {pytorch_dump_folder_path}"""						)
      model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Union[str, 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."""
      )
      __SCREAMING_SNAKE_CASE    :   Dict       =parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)