





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def       _A  (      lowerCamelCase   ):
   a__	:						str      		=  [
       "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(_A							, _A   )






def       _A  (      lowerCamelCase   ):
   a__		,     a__	:						Union[str, Any]      		=  emb.weight.shape
   a__	:						Any      		=  nn.Linear(_A							, _A							, bias=_A   )
   a__	:						Union[str, Any]      		=  emb.weight.data
   return lin_layer






def       _A  (      lowerCamelCase   ):
   a__	:						Dict      		=  torch.load(_A							, map_location="cpu"   )
   a__	:						List[str]      		=  mam_aaa["args"] or mam_aaa["cfg"]["model"]
   a__	:						List[str]      		=  mam_aaa["model"]
   remove_ignore_keys_(_A   )
   a__	:						List[Any]      		=  state_dict["encoder.embed_tokens.weight"].shape[0]

   a__	:						str      		=  MaMaaaConfig(
       vocab_size=_A							, 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"							, )

   a__	:						Dict      		=  state_dict["decoder.embed_tokens.weight"]
   a__	:						List[str]      		=  MaMaaaForConditionalGeneration(_A   )
   model.model.load_state_dict(_A							, strict=_A   )
   a__	:						int      		=  make_linear_from_emb(model.model.shared   )

   return model


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__			      =						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.""")
  SCREAMING_SNAKE_CASE__			      =						parser.parse_args()
  SCREAMING_SNAKE_CASE__			      =						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       :			Dict			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				"""mobilenet_v2"""





   def __init__( self					,			snake_case=3					,			snake_case=224					,			snake_case=1.0					,			snake_case=8					,			snake_case=8					,			snake_case=6					,			snake_case=32					,			snake_case=True					,			snake_case=True					,			snake_case="relu6"					,			snake_case=True					,			snake_case=0.8					,			snake_case=0.02					,			snake_case=0.001					,			snake_case=255					,			**snake_case					,			)				->  int:
      """simple docstring"""





      super().__init__(**snake_case      )

      if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."      )

      a__	:						str      		=  num_channels
      a__	:						Dict      		=  image_size
      a__	:						Any      		=  depth_multiplier
      a__	:						str      		=  depth_divisible_by
      a__	:						Optional[int]      		=  min_depth
      a__	:						Dict      		=  expand_ratio
      a__	:						str      		=  output_stride
      a__	:						Optional[int]      		=  first_layer_is_expansion
      a__	:						Union[str, Any]      		=  finegrained_output
      a__	:						Union[str, Any]      		=  hidden_act
      a__	:						str      		=  tf_padding
      a__	:						List[Any]      		=  classifier_dropout_prob
      a__	:						List[Any]      		=  initializer_range
      a__	:						Optional[Any]      		=  layer_norm_eps
      a__	:						str      		=  semantic_loss_ignore_index







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Any     		=				version.parse("""1.11"""  )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      return OrderedDict([("pixel_values", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]      )
      else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  float:
      """simple docstring"""





      return 1E-4

