






from typing import Any

def _UpperCAmelCase   (	a						:	list  ,					a						:	list  ,					a						:	dict  ,					a						:	dict  ,					a						:	dict  ,					):
							_validation(
							    a  ,					a  ,					a  ,					a  ,					a  ,					)
							# Creates data structures and fill initial step
							snake_case__	  =       {}
							snake_case__	  =       {}
							for state in states_space:
														snake_case__	  =       observations_space[0]
														snake_case__	  =       (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														snake_case__	  =       None

							# Fills the data structure with the probabilities of
							# different transitions and pointers to previous states
							for o in range(1  ,					len(a ) ):
														snake_case__	  =       observations_space[o]
														snake_case__	  =       observations_space[o - 1]
														for state in states_space:
																					# Calculates the argmax for probability function
																					snake_case__	  =       """"""
																					snake_case__	  =       -1
																					for k_state in states_space:
																												snake_case__	  =       (
																												    probabilities[(k_state, prior_observation)]
																												    * transition_probabilities[k_state][state]
																												    * emission_probabilities[state][observation]
																												)
																												if probability > max_probability:
																																			snake_case__	  =       probability
																																			snake_case__	  =       k_state

            # Update probabilities and pointers dicts
																					snake_case__	  =       (
																					    probabilities[(arg_max, prior_observation)]
																					    * transition_probabilities[arg_max][state]
																					    * emission_probabilities[state][observation]
																					)

																					snake_case__	  =       arg_max

    # The final observation
							snake_case__	  =       observations_space[len(a ) - 1]

							# argmax for given final observation
							snake_case__	  =       """"""
							snake_case__	  =       -1
							for k_state in states_space:
														snake_case__	  =       probabilities[(k_state, final_observation)]
														if probability > max_probability:
																					snake_case__	  =       probability
																					snake_case__	  =       k_state
							snake_case__	  =       arg_max

							# Process pointers backwards
							snake_case__	  =       last_state
							snake_case__	  =       []
							for o in range(len(a ) - 1  ,					-1  ,					-1 ):
														result.append(a )
														snake_case__	  =       pointers[previous, observations_space[o]]
							result.reverse()

							return result

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					):
							_validate_not_empty(
							    a  ,					a  ,					a  ,					a  ,					a  ,					)
							_validate_lists(a  ,					a )
							_validate_dicts(
							    a  ,					a  ,					a )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					):
							if not all(
							    [
							        observations_space,
							        states_space,
							        initial_probabilities,
							        transition_probabilities,
							        emission_probabilities,
							    ] ):
														raise ValueError("""There's an empty parameter""" )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any ):
							_validate_list(a  ,					"""observations_space""" )
							_validate_list(a  ,					"""states_space""" )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str ):
							if not isinstance(_object  ,					a ):
														snake_case__	  =       F'''{var_name} must be a list'''
														raise ValueError(a )
							else:
														for x in _object:
																					if not isinstance(a  ,					a ):
																												snake_case__	  =       F'''{var_name} must be a list of strings'''
																												raise ValueError(a )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					):
							_validate_dict(a  ,					"""initial_probabilities"""  ,					a )
							_validate_nested_dict(a  ,					"""transition_probabilities""" )
							_validate_nested_dict(a  ,					"""emission_probabilities""" )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str ):
							_validate_dict(_object  ,					a  ,					a )
							for x in _object.values():
														_validate_dict(a  ,					a  ,					a  ,					a )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	type  ,					a						:	bool = False ):
							if not isinstance(_object  ,					a ):
														snake_case__	  =       F'''{var_name} must be a dict'''
														raise ValueError(a )
							if not all(isinstance(a  ,					a ) for x in _object ):
														snake_case__	  =       F'''{var_name} all keys must be strings'''
														raise ValueError(a )
							if not all(isinstance(a  ,					a ) for x in _object.values() ):
														snake_case__	  =       """nested dictionary """ if nested else """"""
														snake_case__	  =       F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(a )


if __name__ == "__main__":
	from doctest import testmod

	testmod()








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 _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	List[Any] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )

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

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )
							snake_case__	  =       np.asarray(weights[3] )

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

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# layernorm 1
							snake_case__	  =       weights[0][0][0]
							snake_case__	  =       np.asarray(layer_norm_a[0] )
							snake_case__	  =       np.asarray(layer_norm_a[1] )
							set_param(
							    torch_block.attention.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

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

							# intermediate weighs
							snake_case__	  =       weights[2][0][1][2]

							# Chunked Feed Forward
							if len(a ) == 4:
														snake_case__	  =       intermediate_weights[2]

							# layernorm 2
							snake_case__	  =       np.asarray(intermediate_weights[0][0] )
							snake_case__	  =       np.asarray(intermediate_weights[0][1] )
							set_param(
							    torch_block.feed_forward.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# intermediate dense
							snake_case__	  =       np.asarray(intermediate_weights[1][0] )
							snake_case__	  =       np.asarray(intermediate_weights[1][1] )
							set_param(
							    torch_block.feed_forward.dense.dense  ,					torch.tensor(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

							# intermediate out
							snake_case__	  =       np.asarray(intermediate_weights[4][0] )
							snake_case__	  =       np.asarray(intermediate_weights[4][1] )
							set_param(
							    torch_block.feed_forward.output.dense  ,					torch.tensor(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any] ):
							# reformer model
							snake_case__	  =       torch_model.reformer

							# word embeds
							snake_case__	  =       np.asarray(weights[1] )
							set_param(
							    torch_model_reformer.embeddings.word_embeddings  ,					torch.tensor(a )  ,					)

							if isinstance(weights[3]  ,					a ):
														snake_case__	  =       torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights ) ):
																					snake_case__	  =       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'''
																					snake_case__	  =       nn.Parameter(torch.tensor(a ) )

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

							# output layer norm
							snake_case__	  =       np.asarray(weights[7][0] )
							snake_case__	  =       np.asarray(weights[7][1] )
							set_param(
							    torch_model_reformer.encoder.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# output embeddings
							snake_case__	  =       np.asarray(weights[9][0] )
							snake_case__	  =       np.asarray(weights[9][1] )
							set_param(
							    torch_model.lm_head.decoder  ,					torch.tensor(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

							with open(a  ,					"""rb""" ) as f:
														snake_case__	  =       pickle.load(a )["""weights"""]

							set_model_weights_in_torch(a  ,					a  ,					config.hidden_size )

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


if __name__ == "__main__":
	a__       =     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."""
	)
	a__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
