



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_		:  Tuple										=  logging.get_logger(__name__)

A_		:  Union[str, Any]										=  '''Hello world! cécé herlolip'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:str			,       UpperCAmelCase__			:str			,       UpperCAmelCase__			:bool       ):






 '''simple docstring'''

 a		,	a							=		os.path.split(_a       )
 a							=		DataaVecTextModel.from_pretrained(
     _a			,       checkpoint_file=_a       )
 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=5_14			,       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:"			,       _a       )

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

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

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


if __name__ == "__main__":
     A_		:  List[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.'''
     )
     A_		:  Union[str, Any]										=  parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )







'''simple docstring'''







from ..utils import DummyObject, requires_backends


class      _a    (      metaclass=__a					):
       __a							:	str  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Any  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Optional[int]  ,							*lowercase		:      List[str]  ,							**lowercase		:      str							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      List[Any]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)






class      _a    (      metaclass=__a					):
       __a							:	List[Any]  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Tuple  ,							**lowercase		:      List[str]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Any  ,							*lowercase		:      Any  ,							**lowercase		:      Tuple							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)




