



'''simple docstring'''

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Tuple:
	# Load checkpoint
	_lowercase					:       str       =		torch.load(_lowercase,				map_location='cpu'					)

	_lowercase					:       Union[str, Any]       =		chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	_lowercase					:       Union[str, Any]       =		{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			_lowercase					:       int       =		v
		else:
			_lowercase					:       Tuple       =		v

	_lowercase					:       Tuple       =		chkpt['params']
	_lowercase					:       Any       =		{n: v for n, v in config.items() if not isinstance(_lowercase,				(torch.FloatTensor, numpy.ndarray)					)}

	_lowercase					:       int       =		chkpt['dico_word2id']
	_lowercase					:       Any       =		{s + '</w>' if s.find('@@'					) == -1 and i > 13 else s.replace('@@',				''					): i for s, i in vocab.items()}

	# Save pytorch-model
	_lowercase					:       int       =		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	_lowercase					:       Union[str, Any]       =		pytorch_dump_folder_path + '/' + CONFIG_NAME
	_lowercase					:       str       =		pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''					)
	torch.save(_lowercase,				_lowercase					)

	print(f'''Save configuration file to {pytorch_config_dump_path}'''					)
	with open(_lowercase,				'w',				encoding='utf-8'					) as f:
		f.write(json.dumps(_lowercase,				indent=2					) + '\n'					)

	print(f'''Save vocab file to {pytorch_config_dump_path}'''					)
	with open(_lowercase,				'w',				encoding='utf-8'					) as f:
		f.write(json.dumps(_lowercase,				indent=2					) + '\n'					)


if __name__ == "__main__":
		_A				:    Union[str, Any]     						=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		_A				:    str     						=parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       str       =		AutoTokenizer.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       List[Any]       =		'The dog is cute and lives in the garden house'
						_lowercase					:       Optional[int]       =		jnp.array([tokenizer.encode(UpperCamelCase_					)]					)

						_lowercase					:       int       =		(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
						_lowercase					:       Tuple       =		jnp.array(
						    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]					)

						_lowercase					:       List[str]       =		model(UpperCamelCase_					)['last_hidden_state']
						self.assertEqual(output.shape     ,  UpperCamelCase_					)
						# compare the actual values for a slice of last dim
						self.assertTrue(jnp.allclose(output[:, :, -1]     ,  UpperCamelCase_     ,  atol=1E-3					)					)
