


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Any:
  snake_case__						= [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''decoder.output_projection.weight''',
      '''_float_tensor''',
      '''encoder.embed_positions._float_tensor''',
      '''decoder.embed_positions._float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				str:
  snake_case__				,				snake_case__						= emb.weight.shape
  snake_case__						= nn.Linear(__lowerCAmelCase							,   __lowerCAmelCase							,   bias=__lowerCAmelCase				)
  snake_case__						= emb.weight.data
  return lin_layer







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Dict:
  snake_case__						= torch.load(__lowerCAmelCase							,   map_location='''cpu'''				)
  snake_case__						= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
  snake_case__						= mam_aaa['''model''']
  remove_ignore_keys_(__lowerCAmelCase				)
  snake_case__						= state_dict['''encoder.embed_tokens.weight'''].shape[0]

  snake_case__						= MaMaaaConfig(
      vocab_size=__lowerCAmelCase							,   max_position_embeddings=1024							,   encoder_layers=args.encoder_layers							,   decoder_layers=args.decoder_layers							,   encoder_attention_heads=args.encoder_attention_heads							,   decoder_attention_heads=args.decoder_attention_heads							,   encoder_ffn_dim=args.encoder_ffn_embed_dim							,   decoder_ffn_dim=args.decoder_ffn_embed_dim							,   d_model=args.encoder_embed_dim							,   encoder_layerdrop=args.encoder_layerdrop							,   decoder_layerdrop=args.decoder_layerdrop							,   dropout=args.dropout							,   attention_dropout=args.attention_dropout							,   activation_dropout=args.activation_dropout							,   activation_function='''relu'''							,   )

  snake_case__						= state_dict['''decoder.embed_tokens.weight''']
  snake_case__						= MaMaaaForConditionalGeneration(__lowerCAmelCase				)
  model.model.load_state_dict(__lowerCAmelCase							,   strict=__lowerCAmelCase				)
  snake_case__						= make_linear_from_emb(model.model.shared				)

  return model


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[int]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   lowerCamelCase__							:						Optional[int]							     =   parser.parse_args()
   lowerCamelCase__							:						List[str]							     =   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
   model.save_pretrained(args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__							:						Tuple							     =   {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RobertaForCausalLM""",
       """RobertaForMaskedLM""",
       """RobertaForMultipleChoice""",
       """RobertaForQuestionAnswering""",
       """RobertaForSequenceClassification""",
       """RobertaForTokenClassification""",
       """RobertaModel""",
       """RobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						List[str]							     =   [
       """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRobertaForCausalLM""",
       """TFRobertaForMaskedLM""",
       """TFRobertaForMultipleChoice""",
       """TFRobertaForQuestionAnswering""",
       """TFRobertaForSequenceClassification""",
       """TFRobertaForTokenClassification""",
       """TFRobertaMainLayer""",
       """TFRobertaModel""",
       """TFRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						str							     =   [
       """FlaxRobertaForCausalLM""",
       """FlaxRobertaForMaskedLM""",
       """FlaxRobertaForMultipleChoice""",
       """FlaxRobertaForQuestionAnswering""",
       """FlaxRobertaForSequenceClassification""",
       """FlaxRobertaForTokenClassification""",
       """FlaxRobertaModel""",
       """FlaxRobertaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase__							:						List[Any]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

