





"""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"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_lowercase						   =     logging.get_logger(__name__)

_lowercase						   =     {
    """facebook/musicgen-small""": """https://huggingface.co/facebook/musicgen-small/resolve/main/config.json""",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="musicgen_decoder"
				_lowerCAmelCase						      =["past_key_values"]

				def __init__( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  Dict=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  List[str]=2_4   ,SCREAMING_SNAKE_CASE_ :  int=4_0_9_6   ,SCREAMING_SNAKE_CASE_ :  List[Any]=1_6   ,SCREAMING_SNAKE_CASE_ :  Dict=0.0   ,SCREAMING_SNAKE_CASE_ :  Any=True   ,SCREAMING_SNAKE_CASE_ :  Any="gelu"   ,SCREAMING_SNAKE_CASE_ :  Tuple=1_0_2_4   ,SCREAMING_SNAKE_CASE_ :  Any=0.1   ,SCREAMING_SNAKE_CASE_ :  List[str]=0.0   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=0.0   ,SCREAMING_SNAKE_CASE_ :  Tuple=0.02   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=False   ,SCREAMING_SNAKE_CASE_ :  List[str]=4   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=None   ,SCREAMING_SNAKE_CASE_ :  Tuple=False   ,**SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ,):
										snake_case__							:       List[str]				      =       vocab_size
										snake_case__							:       List[str]				      =       max_position_embeddings
										snake_case__							:       List[Any]				      =       hidden_size
										snake_case__							:       Dict				      =       ffn_dim
										snake_case__							:       Union[str, Any]				      =       num_hidden_layers
										snake_case__							:       List[str]				      =       num_attention_heads
										snake_case__							:       Optional[Any]				      =       dropout
										snake_case__							:       Dict				      =       attention_dropout
										snake_case__							:       Union[str, Any]				      =       activation_dropout
										snake_case__							:       Dict				      =       activation_function
										snake_case__							:       Union[str, Any]				      =       initializer_factor
										snake_case__							:       List[str]				      =       layerdrop
										snake_case__							:       Any				      =       use_cache
										snake_case__							:       Tuple				      =       scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case__							:       Any				      =       num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_   ,bos_token_id=SCREAMING_SNAKE_CASE_   ,eos_token_id=SCREAMING_SNAKE_CASE_   ,tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)







class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="musicgen"
				_lowerCAmelCase						      =True

				def __init__( self :  Optional[Any]   ,**SCREAMING_SNAKE_CASE_ :  Dict   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case__							:       Optional[int]				      =       kwargs.pop('''text_encoder'''   )
										snake_case__							:       Optional[Any]				      =       text_encoder_config.pop('''model_type'''   )

										snake_case__							:       Union[str, Any]				      =       kwargs.pop('''audio_encoder'''   )
										snake_case__							:       Dict				      =       audio_encoder_config.pop('''model_type'''   )

										snake_case__							:       Optional[Any]				      =       kwargs.pop('''decoder'''   )

										snake_case__							:       Union[str, Any]				      =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[Any]				      =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Union[str, Any]				      =       True

				@classmethod
				def    _lowerCamelCase       ( cls :  Dict   ,SCREAMING_SNAKE_CASE_ :  PretrainedConfig   ,SCREAMING_SNAKE_CASE_ :  PretrainedConfig   ,SCREAMING_SNAKE_CASE_ :  MusicgenDecoderConfig   ,**SCREAMING_SNAKE_CASE_ :  str   ,):
										return cls(
										    text_encoder=text_encoder_config.to_dict()   ,audio_encoder=audio_encoder_config.to_dict()   ,decoder=decoder_config.to_dict()   ,**SCREAMING_SNAKE_CASE_   ,)





				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										snake_case__							:       Optional[Any]				      =       copy.deepcopy(self.__dict__   )
										snake_case__							:       Any				      =       self.text_encoder.to_dict()
										snake_case__							:       Dict				      =       self.audio_encoder.to_dict()
										snake_case__							:       Dict				      =       self.decoder.to_dict()
										snake_case__							:       Optional[int]				      =       self.__class__.model_type
										return output


