





"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						[
      '''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       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						list(s_dict.keys()  )
  for key in keys:
    if "transformer_layers" in key:
      __UpperCAmelCase    :						Tuple       =						s_dict.pop(UpperCAmelCase_  )
    elif "subsample" in key:
      __UpperCAmelCase    :						List[str]       =						s_dict.pop(UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						emb.weight.shape
  __UpperCAmelCase    :						Dict       =						nn.Linear(UpperCAmelCase_  ,			UpperCAmelCase_  ,			bias=UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						emb.weight.data
  return lin_layer


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )
  __UpperCAmelCase    :						Dict       =						mam_aaa['''args''']
  __UpperCAmelCase    :						Optional[int]       =						mam_aaa['''model''']
  __UpperCAmelCase    :						Any       =						state_dict['''decoder.output_projection.weight''']

  remove_ignore_keys_(UpperCAmelCase_  )
  rename_keys(UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						state_dict['''decoder.embed_tokens.weight'''].shape[0]

  __UpperCAmelCase    :						str       =						args.share_decoder_input_output_embed

  __UpperCAmelCase    :						Tuple       =						[int(UpperCAmelCase_  ) for i in args.conv_kernel_sizes.split(''','''  )]
  __UpperCAmelCase    :						Any       =						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=2_00  ,			use_cache=UpperCAmelCase_  ,			decoder_start_token_id=2  ,			early_stopping=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Any       =						SpeechaTextForConditionalGeneration(UpperCAmelCase_  )
  __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						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:
    __UpperCAmelCase    :						Union[str, Any]       =						make_linear_from_emb(model.model.decoder.embed_tokens  )
  else:
    __UpperCAmelCase    :						int       =						lm_head_weights

  model.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:Optional[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.')
  lowercase__				:Dict				   =       parser.parse_args()
  convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__				:int				   =       TypeVar('T')




class 							snake_case							(			Generic[T] ):

      '''simple docstring'''



      def __init__(							self       :      Optional[Any]						,				__lowercase       :      list[T]						,				__lowercase       :      Callable[[T, T], T] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any | T       =						None

        __UpperCAmelCase    :						int       =						len(__lowercase )
        __UpperCAmelCase    :						list[T]       =						[any_type for _ in range(self.N )] + arr
        __UpperCAmelCase    :						List[Any]       =						fnc
        self.build()

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for p in range(self.N - 1						,				0						,				-1 ):
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      int						,				__lowercase       :      T ):


        '''simple docstring'''




        p += self.N
        __UpperCAmelCase    :						Tuple       =						v
        while p > 1:
          __UpperCAmelCase    :						Union[str, Any]       =						p // 2
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )







      def       A_					(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      int ):  # noqa: E741


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						l + self.N, r + self.N

        __UpperCAmelCase    :						T | None       =						None
        while l <= r:
          if l % 2 == 1:
            __UpperCAmelCase    :						Any       =						self.st[l] if res is None else self.fn(__lowercase						,				self.st[l] )
          if r % 2 == 0:
            __UpperCAmelCase    :						Any       =						self.st[r] if res is None else self.fn(__lowercase						,				self.st[r] )
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						(l + 1) // 2, (r - 1) // 2
        return res


if __name__ == "__main__":
  from functools import reduce

  lowercase__				:Any				   =       [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

  lowercase__				:List[Any]				   =       {
      0: 7,
      1: 2,
      2: 6,
      3: -1_4,
      4: 5,
      5: 4,
      6: 7,
      7: -1_0,
      8: 9,
      9: 1_0,
      1_0: 1_2,
      1_1: 1,
  }

  lowercase__				:List[str]				   =       SegmentTree(test_array, min)
  lowercase__				:List[str]				   =       SegmentTree(test_array, max)
  lowercase__				:Tuple				   =       SegmentTree(test_array, lambda a, b: a + b)


  def       lowerCamelCase_       (   )						->None:





    """simple docstring"""


    for i in range(len(UpperCAmelCase_  )  ):
      for j in range(UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ):
        __UpperCAmelCase    :						Optional[Any]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Optional[int]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Union[str, Any]       =						reduce(lambda UpperCAmelCase_  ,			UpperCAmelCase_  : a + b  ,			test_array[i : j + 1]  )
        assert min_range == min_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert max_range == max_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert sum_range == sum_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )

  test_all_segments()

  for index, value in test_updates.items():
    lowercase__				:Optional[Any]				   =       value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()