

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  a__  =[
      '''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 			UpperCAmelCase__				(			_A      : int							):





  '''simple docstring'''







  a__  =list(s_dict.keys()							)
  for key in keys:
    if "transformer_layers" in key:
      a__  =s_dict.pop(__a							)
    elif "subsample" in key:
      a__  =s_dict.pop(__a							)




def 			UpperCAmelCase__				(			_A      : Dict							):





  '''simple docstring'''







  a__  =emb.weight.shape
  a__  =nn.Linear(__a	,      __a	,      bias=__a							)
  a__  =emb.weight.data
  return lin_layer




def 			UpperCAmelCase__				(			_A      : int	,      _A      : Optional[Any]							):





  '''simple docstring'''







  a__  =torch.load(__a	,      map_location='''cpu'''							)
  a__  =mam_aaa['''args''']
  a__  =mam_aaa['''model''']
  a__  =state_dict['''decoder.output_projection.weight''']

  remove_ignore_keys_(__a							)
  rename_keys(__a							)

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

  a__  =args.share_decoder_input_output_embed

  a__  =[int(__a							) for i in args.conv_kernel_sizes.split(''','''							)]
  a__  =SpeechaTextConfig(
      vocab_size=__a	,      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(__a							)	,      conv_channels=args.conv_channels	,      conv_kernel_sizes=__a	,      input_feat_per_channel=args.input_feat_per_channel	,      input_channels=args.input_channels	,      tie_word_embeddings=__a	,      num_beams=5	,      max_length=2_00	,      use_cache=__a	,      decoder_start_token_id=2	,      early_stopping=__a	,      )

  a__  =SpeechaTextForConditionalGeneration(__a							)
  a__  =model.model.load_state_dict(__a	,      strict=__a							)
  if len(__a							) > 0 and not set(__a							) <= {
      "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:
    a__  =make_linear_from_emb(model.model.decoder.embed_tokens							)
  else:
    a__  =lm_head_weights

  model.save_pretrained(__a							)


if __name__ == "__main__":
       lowerCamelCase           =					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.''')
       lowerCamelCase           =					parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)




'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


__snake_case							      =     {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''DPTFeatureExtractor''']
		__snake_case							      =     ['''DPTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DPTForDepthEstimation''',
		    '''DPTForSemanticSegmentation''',
		    '''DPTModel''',
		    '''DPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)