



"""simple docstring"""





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowercase  							=					logging.get_logger(__name__)




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

		'''simple docstring'''


		try:
				with open(A     ,				'rb'       ) as flax_state_f:
						_UpperCAmelCase							 =		from_bytes(A     ,				flax_state_f.read()       )
		except UnpicklingError as e:
				try:
						with open(A       ) as f:
								if f.read().startswith('version'       ):
										raise OSError(
										    'You seem to have cloned a repository without having git-lfs installed. Please'
										    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
										    ' folder you cloned.'       )
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '       )

		return load_flax_weights_in_pytorch_model(A     ,				A       )






def 	UpperCAmelCase     (		A	:						str     ,				A	:						Union[str, Any]       ):

		'''simple docstring'''


		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
				    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
				    ' instructions.'       )
				raise

		# check if we have bf16 weights
		_UpperCAmelCase							 =		flatten_dict(jax.tree_util.tree_map(lambda A       : x.dtype == jnp.bfloataa     ,				A       )       ).values()
		if any(A       ):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
				    'before loading those in PyTorch model.'       )
				_UpperCAmelCase							 =		jax.tree_util.tree_map(
				    lambda A       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params     ,				A       )

		_UpperCAmelCase							 =		''

		_UpperCAmelCase							 =		flatten_dict(A     ,				sep='.'       )
		_UpperCAmelCase							 =		pt_model.state_dict()

		# keep track of unexpected & missing keys
		_UpperCAmelCase							 =		[]
		_UpperCAmelCase							 =		set(pt_model_dict.keys()       )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				_UpperCAmelCase							 =		flax_key_tuple.split('.'       )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						_UpperCAmelCase							 =		flax_key_tuple_array[:-1] + ['weight']
						_UpperCAmelCase							 =		jnp.transpose(A     ,				(3, 2, 0, 1)       )
				elif flax_key_tuple_array[-1] == "kernel":
						_UpperCAmelCase							 =		flax_key_tuple_array[:-1] + ['weight']
						_UpperCAmelCase							 =		flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						_UpperCAmelCase							 =		flax_key_tuple_array[:-1] + ['weight']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(A       ):
								_UpperCAmelCase							 =		(
								    flax_key_tuple_string.replace('_0'     ,				'.0'       )
								    .replace('_1'     ,				'.1'       )
								    .replace('_2'     ,				'.2'       )
								    .replace('_3'     ,				'.3'       )
								    .replace('_4'     ,				'.4'       )
								    .replace('_5'     ,				'.5'       )
								    .replace('_6'     ,				'.6'       )
								    .replace('_7'     ,				'.7'       )
								    .replace('_8'     ,				'.8'       )
								    .replace('_9'     ,				'.9'       )
								)

				_UpperCAmelCase							 =		'.'.join(A       )

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
								    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )
						else:
								# add weight to pytorch dict
								_UpperCAmelCase							 =		np.asarray(A       ) if not isinstance(A     ,				np.ndarray       ) else flax_tensor
								_UpperCAmelCase							 =		torch.from_numpy(A       )
								# remove from missing keys
								missing_keys.remove(A       )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(A       )

		pt_model.load_state_dict(A       )

		# re-transform missing_keys to list
		_UpperCAmelCase							 =		list(A       )

		if len(A       ) > 0:
				logger.warning(
				    'Some weights of the Flax model were not used when initializing the PyTorch model'
				    f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
				    f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
				    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
				    f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
				    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
				    ' FlaxBertForSequenceClassification model).'       )
		if len(A       ) > 0:
				logger.warning(
				    f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
				    f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
				    ' use it for predictions and inference.'       )

		return pt_model







"""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	,       )


