






"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[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(lowerCamelCase__     ,   lowerCamelCase__				)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	list(s_dict.keys()				)
  for key in keys:
    if "transformer_layers" in key:
      _a  :       str             =	s_dict.pop(lowerCamelCase__				)
    elif "subsample" in key:
      _a  :       Any             =	s_dict.pop(lowerCamelCase__				)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Optional[int]             =	emb.weight.shape
  _a  :       int             =	nn.Linear(lowerCamelCase__     ,   lowerCamelCase__     ,   bias=lowerCamelCase__				)
  _a  :       int             =	emb.weight.data
  return lin_layer

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[Any]             =	torch.load(lowerCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[Any]             =	mam_aaa["""args"""]
  _a  :       Dict             =	mam_aaa["""model"""]
  _a  :       Dict             =	state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(lowerCamelCase__				)
  rename_keys(lowerCamelCase__				)

  _a  :       Union[str, Any]             =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

  _a  :       int             =	args.share_decoder_input_output_embed

  _a  :       List[Any]             =	[int(lowerCamelCase__				) for i in args.conv_kernel_sizes.split(""","""				)]
  _a  :       Any             =	SpeechaTextConfig(
      vocab_size=lowerCamelCase__     ,   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(lowerCamelCase__				)     ,   conv_channels=args.conv_channels     ,   conv_kernel_sizes=lowerCamelCase__     ,   input_feat_per_channel=args.input_feat_per_channel     ,   input_channels=args.input_channels     ,   tie_word_embeddings=lowerCamelCase__     ,   num_beams=5     ,   max_length=2_0_0     ,   use_cache=lowerCamelCase__     ,   decoder_start_token_id=2     ,   early_stopping=lowerCamelCase__     ,   )

  _a  :       Any             =	SpeechaTextForConditionalGeneration(lowerCamelCase__				)
  _a  :       Any             =	model.model.load_state_dict(lowerCamelCase__     ,   strict=lowerCamelCase__				)
  if len(lowerCamelCase__				) > 0 and not set(lowerCamelCase__				) <= {
      "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  :       int             =	make_linear_from_emb(model.model.decoder.embed_tokens				)
  else:
    _a  :       Tuple             =	lm_head_weights

  model.save_pretrained(lowerCamelCase__				)


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






'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()