



"""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 	UpperCAmelCase     (		A	:						Dict     ,				A	:						str     ,				A	:						List[Any]=None       ):

		'''simple docstring'''


		assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
		_UpperCAmelCase							 =		nn.Parameter(UpperCAmelCase__       )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
				_UpperCAmelCase							 =		nn.Parameter(UpperCAmelCase__       )




def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						Optional[Any]     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		np.asarray(weights[0]       )
		_UpperCAmelCase							 =		np.asarray(weights[1]       )
		_UpperCAmelCase							 =		np.asarray(weights[2]       )

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




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						Any     ,				A	:						Tuple       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		np.asarray(weights[0]       )
		_UpperCAmelCase							 =		np.asarray(weights[1]       )
		_UpperCAmelCase							 =		np.asarray(weights[2]       )
		_UpperCAmelCase							 =		np.asarray(weights[3]       )

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




def 	UpperCAmelCase     (		A	:						Tuple     ,				A	:						Tuple     ,				A	:						Optional[Any]       ):

		'''simple docstring'''


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

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

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

		# Chunked Feed Forward
		if len(UpperCAmelCase__       ) == 4:
				_UpperCAmelCase							 =		intermediate_weights[2]

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

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

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




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						List[Any]     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		torch_model.reformer

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

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

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

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

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




def 	UpperCAmelCase     (		A	:						int     ,				A	:						List[str]     ,				A	:						Dict       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		ReformerConfig.from_json_file(UpperCAmelCase__       )
		print(f'Building PyTorch model from configuration: {config}'       )
		_UpperCAmelCase							 =		ReformerModelWithLMHead(UpperCAmelCase__       )

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

		set_model_weights_in_torch(UpperCAmelCase__     ,				UpperCAmelCase__     ,				config.hidden_size       )

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


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







"""simple docstring"""





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					BarthezTokenizer
							_UpperCAmelCase      				=					BarthezTokenizerFast
							_UpperCAmelCase      				=					True
							_UpperCAmelCase      				=					True






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									super().setUp()

									_UpperCAmelCase							 =		BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'						)
									tokenizer.save_pretrained(self.tmpdirname						)
									tokenizer.save_pretrained(self.tmpdirname	,       legacy_format=snake_case						)
									_UpperCAmelCase							 =		tokenizer






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		'<pad>'
									_UpperCAmelCase							 =		1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case						)	,       snake_case						)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case						)	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]	,       '<s>'						)
									self.assertEqual(vocab_keys[1]	,       '<pad>'						)
									self.assertEqual(vocab_keys[-1]	,       '<mask>'						)
									self.assertEqual(len(snake_case						)	,       101122						)






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									self.assertEqual(self.get_tokenizer().vocab_size	,       101122						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_UpperCAmelCase							 =		[0, 57, 3018, 70307, 91, 2]

									_UpperCAmelCase							 =		self.tokenizer(
									    snake_case	,       max_length=len(snake_case						)	,       padding=snake_case	,       truncation=snake_case	,       return_tensors='pt'						)
									self.assertIsInstance(snake_case	,       snake_case						)

									self.assertEqual((2, 6)	,       batch.input_ids.shape						)
									self.assertEqual((2, 6)	,       batch.attention_mask.shape						)
									_UpperCAmelCase							 =		batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									if not self.test_rust_tokenizer:
											return

									_UpperCAmelCase							 =		self.get_tokenizer()
									_UpperCAmelCase							 =		self.get_rust_tokenizer()

									_UpperCAmelCase							 =		'I was born in 92000, and this is falsé.'

									_UpperCAmelCase							 =		tokenizer.tokenize(snake_case						)
									_UpperCAmelCase							 =		rust_tokenizer.tokenize(snake_case						)
									self.assertListEqual(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		tokenizer.encode(snake_case	,       add_special_tokens=snake_case						)
									_UpperCAmelCase							 =		rust_tokenizer.encode(snake_case	,       add_special_tokens=snake_case						)
									self.assertListEqual(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		self.get_rust_tokenizer()
									_UpperCAmelCase							 =		tokenizer.encode(snake_case						)
									_UpperCAmelCase							 =		rust_tokenizer.encode(snake_case						)
									self.assertListEqual(snake_case	,       snake_case						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									# fmt: off
									_UpperCAmelCase							 =		{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_UpperCAmelCase							 =		[
									    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
									    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
									    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
									    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
									    'telles que la traduction et la synthèse de texte.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=snake_case	,       model_name='moussaKam/mbarthez'	,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,       sequences=snake_case	,       )


