



class     __a			(  _UpperCamelCase					):
	pass






class     __a			(  _UpperCamelCase					):
	pass






class     __a			:



	def __init__(    self				:     int)->				Dict:
						__lowerCAmelCase						 =[
						    [],
						    [],
						    [],
						]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     int					,						snake_case_				:     int)->				None:



						try:
											if len(self.queues[priority]) >= 1_00:
																raise OverflowError("""Maximum queue size is 100""")
											self.queues[priority].append(a_)
						except IndexError:
											raise ValueError("""Valid priorities are 0, 1, and 2""")



	def 		UpperCamelCase				(    self				:     Optional[int])->				int:



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



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






class     __a			:



	def __init__(    self				:     List[str])->				Optional[Any]:
						__lowerCAmelCase						 =[]



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     int)->				None:



						if len(self.queue) == 1_00:
											raise OverFlowError("""Maximum queue size is 100""")
						self.queue.append(a_)



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				int:



						if not self.queue:
											raise UnderFlowError("""The queue is empty""")
						else:
											__lowerCAmelCase						 =min(self.queue)
											self.queue.remove(a_)
											return data



	def __str__(    self				:     Optional[int])->				str:
						return str(self.queue)






def        __lowerCAmelCase		(	)	->    int:
					__lowerCAmelCase						 =FixedPriorityQueue()
					fpq.enqueue(0					, 10						)
					fpq.enqueue(1					, 70						)
					fpq.enqueue(0					, 100						)
					fpq.enqueue(2					, 1						)
					fpq.enqueue(2					, 5						)
					fpq.enqueue(1					, 7						)
					fpq.enqueue(2					, 4						)
					fpq.enqueue(1					, 64						)
					fpq.enqueue(0					, 128						)
					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        __lowerCAmelCase		(	)	->    Dict:
					__lowerCAmelCase						 =ElementPriorityQueue()
					epq.enqueue(10						)
					epq.enqueue(70						)
					epq.enqueue(100						)
					epq.enqueue(1						)
					epq.enqueue(5						)
					epq.enqueue(7						)
					epq.enqueue(4						)
					epq.enqueue(64						)
					epq.enqueue(128						)
					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()











"""simple docstring"""






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[Any]=None			)    ->			str:



			'''simple docstring'''
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
			a__  :	Optional[Any]		 =			nn.Parameter(lowerCAmelCase__			)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
						a__  :	str		 =			nn.Parameter(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	Tuple		 =			np.asarray(weights[0]			)
			a__  :	Any		 =			np.asarray(weights[1]			)
			a__  :	Optional[Any]		 =			np.asarray(weights[2]			)

			set_param(
			    torch_layer.self_attention.query_key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	int		 =			np.asarray(weights[0]			)
			a__  :	List[str]		 =			np.asarray(weights[1]			)
			a__  :	Optional[int]		 =			np.asarray(weights[2]			)
			a__  :	int		 =			np.asarray(weights[3]			)

			set_param(
			    torch_layer.self_attention.query							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# layernorm 1
			a__  :	int		 =			weights[0][0][0]
			a__  :	Optional[Any]		 =			np.asarray(layer_norm_a[0]			)
			a__  :	Optional[int]		 =			np.asarray(layer_norm_a[1]			)
			set_param(
			    torch_block.attention.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# lsh weights + output
			a__  :	Dict		 =			weights[0][1]
			if len(lowerCAmelCase__			) < 4:
						set_layer_weights_in_torch_lsh(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)
			else:
						set_layer_weights_in_torch_local(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)

			# intermediate weighs
			a__  :	Optional[int]		 =			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(lowerCAmelCase__			) == 4:
						a__  :	List[str]		 =			intermediate_weights[2]

			# layernorm 2
			a__  :	List[Any]		 =			np.asarray(intermediate_weights[0][0]			)
			a__  :	Optional[int]		 =			np.asarray(intermediate_weights[0][1]			)
			set_param(
			    torch_block.feed_forward.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate dense
			a__  :	Tuple		 =			np.asarray(intermediate_weights[1][0]			)
			a__  :	Union[str, Any]		 =			np.asarray(intermediate_weights[1][1]			)
			set_param(
			    torch_block.feed_forward.dense.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate out
			a__  :	Optional[Any]		 =			np.asarray(intermediate_weights[4][0]			)
			a__  :	Dict		 =			np.asarray(intermediate_weights[4][1]			)
			set_param(
			    torch_block.feed_forward.output.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# reformer model
			a__  :	int		 =			torch_model.reformer

			# word embeds
			a__  :	List[Any]		 =			np.asarray(weights[1]			)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(lowerCAmelCase__			)							,			)

			if isinstance(weights[3]							,			lowerCAmelCase__			):
						a__  :	Dict		 =			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights			)			):
									a__  :	str		 =			np.asarray(weights[3][emb_idx][0]			)
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), F"{position_embeddings[emb_idx]} emb does not match"
									a__  :	List[str]		 =			nn.Parameter(torch.tensor(lowerCAmelCase__			)			)

			a__  :	Optional[Any]		 =			weights[5]
			assert len(torch_model_reformer.encoder.layers			) * 4 == len(
			    lowerCAmelCase__			), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
						a__  :	Union[str, Any]		 =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# output layer norm
			a__  :	str		 =			np.asarray(weights[7][0]			)
			a__  :	List[Any]		 =			np.asarray(weights[7][1]			)
			set_param(
			    torch_model_reformer.encoder.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# output embeddings
			a__  :	str		 =			np.asarray(weights[9][0]			)
			a__  :	Optional[int]		 =			np.asarray(weights[9][1]			)
			set_param(
			    torch_model.lm_head.decoder							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	List[Any]		 =			ReformerConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	Optional[Any]		 =			ReformerModelWithLMHead(lowerCAmelCase__			)

			with open(lowerCAmelCase__							,			"rb"			) as f:
						a__  :	List[str]		 =			pickle.load(lowerCAmelCase__			)["weights"]

			set_model_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			config.hidden_size			)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"			)
			torch.save(model.state_dict()							,			lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained Reformer model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)