
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_		,       a_		,       a_=None						)		->     Tuple:







				"""simple docstring"""



				assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
				__A             =			nn.Parameter(a_						)
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
								__A             =			nn.Parameter(a_						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Dict:







				"""simple docstring"""



				__A             =			np.asarray(weights[0]						)
				__A             =			np.asarray(weights[1]						)
				__A             =			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_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				__A             =			np.asarray(weights[0]						)
				__A             =			np.asarray(weights[1]						)
				__A             =			np.asarray(weights[2]						)
				__A             =			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_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			weights[0][0][0]
				__A             =			np.asarray(layer_norm_a[0]						)
				__A             =			np.asarray(layer_norm_a[1]						)
				set_param(
				    torch_block.attention.layer_norm		,       torch.tensor(a_						)		,       torch.tensor(a_						)		,       )

				# lsh weights + output
				__A             =			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
				__A             =			weights[2][0][1][2]

				# Chunked Feed Forward
				if len(a_						) == 4:
								__A             =			intermediate_weights[2]

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

				# intermediate dense
				__A             =			np.asarray(intermediate_weights[1][0]						)
				__A             =			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
				__A             =			np.asarray(intermediate_weights[4][0]						)
				__A             =			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_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				__A             =			torch_model.reformer

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

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

				__A             =			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						):
								__A             =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
								set_block_weights_in_torch(a_		,       a_		,       a_						)

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

				# output embeddings
				__A             =			np.asarray(weights[9][0]						)
				__A             =			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_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			ReformerConfig.from_json_file(a_						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				__A             =			ReformerModelWithLMHead(a_						)

				with open(a_		,       "rb"						) as f:
								__A             =			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__":
					SCREAMING_SNAKE_CASE							:str										=    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.'
					)
					SCREAMING_SNAKE_CASE							:str										=    parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="text-classification"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"text": Value("string"  )}  )
	snake_case_    =					Features({"labels": ClassLabel}  )
	snake_case_    =					"text"
	snake_case_    =					"labels"






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					if self.label_column not in features:
									raise ValueError(f'''Column {self.label_column} is not present in features.'''			)
					if not isinstance(features[self.label_column]							,A			):
									raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.label_schema.copy()
					__A             =			features[self.label_column]
					__A             =			label_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return {
					    self.text_column: "text",
					    self.label_column: "labels",
					}
