
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 							A						(    snake_case				:List[Any]		)				->     Union[str, Any]:
 __UpperCamelCase      =	[
     '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						(    snake_case				:Dict		)				->     Optional[Any]:
 __UpperCamelCase      =	list(s_dict.keys()		)
 for key in keys:
  if "transformer_layers" in key:
   __UpperCamelCase      =	s_dict.pop(snake_case		)
  elif "subsample" in key:
   __UpperCamelCase      =	s_dict.pop(snake_case		)






def 							A						(    snake_case				:List[str]		)				->     Tuple:
 __UpperCamelCase  ,     __UpperCamelCase      =	emb.weight.shape
 __UpperCamelCase      =	nn.Linear(snake_case    , snake_case    , bias=snake_case		)
 __UpperCamelCase      =	emb.weight.data
 return lin_layer






def 							A						(    snake_case				:Dict    , snake_case				:int		)				->     str:
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)
 __UpperCamelCase      =	mam_aaa['args']
 __UpperCamelCase      =	mam_aaa['model']
 __UpperCamelCase      =	state_dict['decoder.output_projection.weight']

 remove_ignore_keys_(snake_case		)
 rename_keys(snake_case		)

 __UpperCamelCase      =	state_dict['decoder.embed_tokens.weight'].shape[0]

 __UpperCamelCase      =	args.share_decoder_input_output_embed

 __UpperCamelCase      =	[int(snake_case		) for i in args.conv_kernel_sizes.split(','		)]
 __UpperCamelCase      =	SpeechaTextConfig(
     vocab_size=snake_case    , max_source_positions=args.max_source_positions    , max_target_positions=args.max_target_positions    , 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    , dropout=args.dropout    , attention_dropout=args.attention_dropout    , activation_dropout=args.activation_dropout    , activation_function='relu'    , num_conv_layers=len(snake_case		)    , conv_channels=args.conv_channels    , conv_kernel_sizes=snake_case    , input_feat_per_channel=args.input_feat_per_channel    , input_channels=args.input_channels    , tie_word_embeddings=snake_case    , num_beams=5    , max_length=2_0_0    , use_cache=snake_case    , decoder_start_token_id=2    , early_stopping=snake_case    , )

 __UpperCamelCase      =	SpeechaTextForConditionalGeneration(snake_case		)
 __UpperCamelCase  ,     __UpperCamelCase      =	model.model.load_state_dict(snake_case    , strict=snake_case		)
 if len(snake_case		) > 0 and not set(snake_case		) <= {
     "encoder.embed_positions.weights",
     "decoder.embed_positions.weights",
 }:
  raise ValueError(
      'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
      f' but all the following weights are missing {missing}'		)

 if tie_embeds:
  __UpperCamelCase      =	make_linear_from_emb(model.model.decoder.embed_tokens		)
 else:
  __UpperCamelCase      =	lm_head_weights

 model.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Tuple       =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      UpperCamelCase     :    List[str]       =			parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase     :    Union[str, Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    int       =			{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"distilbert"
     lowercase						       =						{
         "hidden_size": "dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
     }





     def __init__(     self     ,					__UpperCAmelCase=3_0522     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=6     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=4 * 768     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.2     ,					__UpperCAmelCase=0     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	max_position_embeddings
      __UpperCamelCase      =	sinusoidal_pos_embds
      __UpperCamelCase      =	n_layers
      __UpperCamelCase      =	n_heads
      __UpperCamelCase      =	dim
      __UpperCamelCase      =	hidden_dim
      __UpperCamelCase      =	dropout
      __UpperCamelCase      =	attention_dropout
      __UpperCamelCase      =	activation
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	qa_dropout
      __UpperCamelCase      =	seq_classif_dropout
      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase				)


class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.task == "multiple-choice":
       __UpperCamelCase      =	{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       __UpperCamelCase      =	{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]				)



