"""simple docstring"""






import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__lowerCAmelCase    :       Dict					     ={
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Union[str, Any]		) ->    int:
      '''simple docstring'''


      if got_ver is None or want_ver is None:
            raise ValueError(
                f'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
                f' reinstalling {pkg}.'		)
      if not ops[op](version.parse(__UpperCAmelCase		)					,		version.parse(__UpperCAmelCase		)		):
            raise ImportError(
                f'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :List[str] = None		) ->    None:
      '''simple docstring'''


      lowercase														=	f'\n{hint}' if hint is not None else """"""

      # non-versioned check
      if re.match(R"""^[\w_\-\d]+$"""					,		__UpperCAmelCase		):
            lowercase							,							lowercase							,							lowercase														=	requirement, None, None
      else:
            lowercase														=	re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""					,		__UpperCAmelCase		)
            if not match:
                  raise ValueError(
                      """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
                      f' got {requirement}'		)
            lowercase							,							lowercase														=	match[0]
            lowercase														=	want_full.split(""","""		)  # there could be multiple requirements
            lowercase														=	{}
            for w in want_range:
                  lowercase														=	re.findall(R"""^([\s!=<>]{1,2})(.+)"""					,		__UpperCAmelCase		)
                  if not match:
                        raise ValueError(
                            """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
                            f' but got {requirement}'		)
                  lowercase							,							lowercase														=	match[0]
                  lowercase														=	want_ver
                  if op not in ops:
                        raise ValueError(f'{requirement}: need one of {list(ops.keys()		)}, but got {op}'		)

    # special case
      if pkg == "python":
            lowercase														=	""".""".join([str(__UpperCAmelCase		) for x in sys.version_info[:3]]		)
            for op, want_ver in wanted.items():
                  _compare_versions(__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase		)
            return

      # check if any version is installed
      try:
            lowercase														=	importlib.metadata.version(__UpperCAmelCase		)
      except importlib.metadata.PackageNotFoundError:
            raise importlib.metadata.PackageNotFoundError(
                f'The \'{requirement}\' distribution was not found and is required by this application. {hint}'		)

      # check that the right version is installed if version number or a range was provided
      if want_ver is not None:
            for op, want_ver in wanted.items():
                  _compare_versions(__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase					,		__UpperCAmelCase		)






def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]		) ->    Dict:
      '''simple docstring'''


      lowercase														=	"""Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main"""
      return require_version(__UpperCAmelCase					,		__UpperCAmelCase		)









'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					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(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_			,snake_case_									=				emb.weight.shape
					snake_case_									=				nn.Linear(__UpperCAmelCase,      __UpperCAmelCase,      bias=__UpperCAmelCase	)
					snake_case_									=				emb.weight.data
					return lin_layer


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

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

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

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

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      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(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

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

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  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.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

