"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					__a   (		A      ):






					'''simple docstring'''



					lowercase__				 =							[
					    "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(_snake_case    ,				_snake_case      )







def 					__a   (		A      ):






					'''simple docstring'''



					lowercase__ ,						lowercase__				 =							emb.weight.shape
					lowercase__				 =							nn.Linear(_snake_case    ,				_snake_case    ,				bias=_snake_case      )
					lowercase__				 =							emb.weight.data
					return lin_layer







def 					__a   (		A      ):






					'''simple docstring'''



					lowercase__				 =							torch.load(_snake_case    ,				map_location="cpu"      )
					lowercase__				 =							mam_aaa["args"] or mam_aaa["cfg"]["model"]
					lowercase__				 =							mam_aaa["model"]
					remove_ignore_keys_(_snake_case      )
					lowercase__				 =							state_dict["encoder.embed_tokens.weight"].shape[0]

					lowercase__				 =							MaMaaaConfig(
					    vocab_size=_snake_case    ,				max_position_embeddings=10_24    ,				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"    ,				)

					lowercase__				 =							state_dict["decoder.embed_tokens.weight"]
					lowercase__				 =							MaMaaaForConditionalGeneration(_snake_case      )
					model.model.load_state_dict(_snake_case    ,				strict=_snake_case      )
					lowercase__				 =							make_linear_from_emb(model.model.shared      )

					return model


if __name__ == "__main__":
	lowerCAmelCase_:					List[Any]     =					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_:					str     =					parser.parse_args()
	lowerCAmelCase_:					Any     =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)





"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase_:					Union[str, Any]     =					{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Union[str, Any]     =					["DistilBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Any     =					[
     "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DistilBertForMaskedLM",
     "DistilBertForMultipleChoice",
     "DistilBertForQuestionAnswering",
     "DistilBertForSequenceClassification",
     "DistilBertForTokenClassification",
     "DistilBertModel",
     "DistilBertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Tuple     =					[
     "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDistilBertForMaskedLM",
     "TFDistilBertForMultipleChoice",
     "TFDistilBertForQuestionAnswering",
     "TFDistilBertForSequenceClassification",
     "TFDistilBertForTokenClassification",
     "TFDistilBertMainLayer",
     "TFDistilBertModel",
     "TFDistilBertPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Optional[Any]     =					[
     "FlaxDistilBertForMaskedLM",
     "FlaxDistilBertForMultipleChoice",
     "FlaxDistilBertForQuestionAnswering",
     "FlaxDistilBertForSequenceClassification",
     "FlaxDistilBertForTokenClassification",
     "FlaxDistilBertModel",
     "FlaxDistilBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_:					Optional[int]     =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




