


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=13		,      A=7		,      A=True		,      A=True		,      A=True		,      A=True		,      A=99		,      A=32		,      A=5		,      A=4		,      A=37		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=16		,      A=2		,      A=0.02		,      A=4		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  parent
					lowerCamelCase    		=  batch_size
					lowerCamelCase    		=  seq_length
					lowerCamelCase    		=  is_training
					lowerCamelCase    		=  use_attention_mask
					lowerCamelCase    		=  use_token_type_ids
					lowerCamelCase    		=  use_labels
					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  type_vocab_size
					lowerCamelCase    		=  type_sequence_label_size
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  num_choices







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size	)

					lowerCamelCase    		=  None
					if self.use_attention_mask:
								lowerCamelCase    		=  random_attention_mask([self.batch_size, self.seq_length]	)

					lowerCamelCase    		=  None
					if self.use_token_type_ids:
								lowerCamelCase    		=  ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size	)

					lowerCamelCase    		=  RoFormerConfig(
					    vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      is_decoder=__SCREAMING_SNAKE_CASE		,      initializer_range=self.initializer_range		,      )

					return config, input_ids, token_type_ids, attention_mask







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  self.prepare_config_and_inputs()
					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase    		=  config_and_inputs
					lowerCamelCase    		=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
					return config, inputs_dict







@require_flax
class        __lowercase       (							__lowerCAmelCase			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Tuple			       =		True

		UpperCamelCase     :					Optional[int]			       =		(
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  FlaxRoFormerModelTester(self	)







		@slow
		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					for model_class_name in self.all_model_classes:
								lowerCamelCase    		=  model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		,      from_pt=__SCREAMING_SNAKE_CASE	)
								lowerCamelCase    		=  model(np.ones((1, 1)	)	)
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)







@require_flax
class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		@slow
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""	)
					lowerCamelCase    		=  jnp.array([[0, 1, 2, 3, 4, 5]]	)
					lowerCamelCase    		=  model(__SCREAMING_SNAKE_CASE	)[0]

					lowerCamelCase    		=  5_00_00

					lowerCamelCase    		=  (1, 6, vocab_size)
					self.assertEqual(output.shape		,      __SCREAMING_SNAKE_CASE	)

					lowerCamelCase    		=  jnp.array(
					    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]	)

					self.assertTrue(jnp.allclose(output[:, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1e-4	)	)







'''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      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

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


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	1

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


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	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(__SCREAMING_SNAKE_CASE	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	max_length=len(__SCREAMING_SNAKE_CASE	)     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
            self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

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

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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.
            __snake_case            =	[
                '''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=__SCREAMING_SNAKE_CASE     ,	model_name='''moussaKam/mbarthez'''     ,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,	sequences=__SCREAMING_SNAKE_CASE     ,	)






