

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=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(SCREAMING_SNAKE_CASE__       )
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
												snake_case_   =	nn.Parameter(SCREAMING_SNAKE_CASE__       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# 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(SCREAMING_SNAKE_CASE__       ).transpose(1  ,		2       ).contiguous().view(-1  ,		SCREAMING_SNAKE_CASE__       )  ,		)
						set_param(
						    torch_layer.self_attention.value  ,		torch.tensor(SCREAMING_SNAKE_CASE__       ).transpose(1  ,		2       ).contiguous().view(-1  ,		SCREAMING_SNAKE_CASE__       )  ,		)
						set_param(
						    torch_layer.output.dense  ,		torch.tensor(SCREAMING_SNAKE_CASE__       ).view(-1  ,		SCREAMING_SNAKE_CASE__       ).contiguous().transpose(0  ,		1       )  ,		)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# 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(SCREAMING_SNAKE_CASE__       ).transpose(1  ,		2       ).contiguous().view(-1  ,		SCREAMING_SNAKE_CASE__       )  ,		)
						set_param(
						    torch_layer.self_attention.key  ,		torch.tensor(SCREAMING_SNAKE_CASE__       ).transpose(1  ,		2       ).contiguous().view(-1  ,		SCREAMING_SNAKE_CASE__       )  ,		)
						set_param(
						    torch_layer.self_attention.value  ,		torch.tensor(SCREAMING_SNAKE_CASE__       ).transpose(1  ,		2       ).contiguous().view(-1  ,		SCREAMING_SNAKE_CASE__       )  ,		)
						set_param(
						    torch_layer.output.dense  ,		torch.tensor(SCREAMING_SNAKE_CASE__       ).view(-1  ,		SCREAMING_SNAKE_CASE__       ).contiguous().transpose(0  ,		1       )  ,		)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# 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(SCREAMING_SNAKE_CASE__       )  ,		torch.tensor(SCREAMING_SNAKE_CASE__       )  ,		)

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

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

						# Chunked Feed Forward
						if len(SCREAMING_SNAKE_CASE__       ) == 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(SCREAMING_SNAKE_CASE__       )  ,		torch.tensor(SCREAMING_SNAKE_CASE__       )  ,		)

						# 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(SCREAMING_SNAKE_CASE__       ).transpose(0  ,		1       ).contiguous()  ,		torch.tensor(SCREAMING_SNAKE_CASE__       )  ,		)

						# 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(SCREAMING_SNAKE_CASE__       ).transpose(0  ,		1       ).contiguous()  ,		torch.tensor(SCREAMING_SNAKE_CASE__       )  ,		)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# 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(SCREAMING_SNAKE_CASE__       )  ,		)

						if isinstance(weights[3]  ,		SCREAMING_SNAKE_CASE__       ):
												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(SCREAMING_SNAKE_CASE__       )       )

						snake_case_   =	weights[5]
						assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
						    SCREAMING_SNAKE_CASE__       ), "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(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						# 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(SCREAMING_SNAKE_CASE__       )  ,		torch.tensor(SCREAMING_SNAKE_CASE__       )  ,		)

						# 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(SCREAMING_SNAKE_CASE__       ).transpose(0  ,		1       ).contiguous()  ,		torch.tensor(SCREAMING_SNAKE_CASE__       )  ,		)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# Initialise PyTorch model
						snake_case_   =	ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
						print(F'''Building PyTorch model from configuration: {config}'''       )
						snake_case_   =	ReformerModelWithLMHead(SCREAMING_SNAKE_CASE__       )

						with open(SCREAMING_SNAKE_CASE__  ,		'''rb'''       ) as f:
												snake_case_   =	pickle.load(SCREAMING_SNAKE_CASE__       )['''weights''']

						set_model_weights_in_torch(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		config.hidden_size       )

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


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


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)