





"""simple docstring"""



from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
       import tensorflow as tf

       from transformers import TFBlipTextModel
       from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=12 ,						__lowerCamelCase=7 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=99 ,						__lowerCamelCase=32 ,						__lowerCamelCase=32 ,						__lowerCamelCase=2 ,						__lowerCamelCase=4 ,						__lowerCamelCase=37 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=512 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=0 ,						__lowerCamelCase=None ,						):



     '''simple docstring'''




     __A   :						List[Any]									=					parent
     __A   :						Union[str, Any]									=					batch_size
     __A   :						Optional[int]									=					seq_length
     __A   :						Union[str, Any]									=					is_training
     __A   :						str									=					use_input_mask
     __A   :						List[Any]									=					use_labels
     __A   :						Any									=					vocab_size
     __A   :						Optional[Any]									=					hidden_size
     __A   :						List[Any]									=					projection_dim
     __A   :						Dict									=					num_hidden_layers
     __A   :						Optional[int]									=					num_attention_heads
     __A   :						Any									=					intermediate_size
     __A   :						Optional[Any]									=					dropout
     __A   :						Any									=					attention_dropout
     __A   :						List[Any]									=					max_position_embeddings
     __A   :						Any									=					initializer_range
     __A   :						Dict									=					scope
     __A   :						Dict									=					bos_token_id

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )

     __A   :						int									=					None
     if self.use_input_mask:
         __A   :						Optional[Any]									=					random_attention_mask([self.batch_size, self.seq_length]  )

     if input_mask is not None:
         __A   :						str									=					input_mask.numpy()
         __A			,	__A   :						Dict									=					input_mask.shape
         __A   :						int									=					np.random.randint(1 ,						seq_length - 1 ,						size=(batch_size,)  )
         for batch_idx, start_index in enumerate(__lowerCamelCase  ):
             __A   :						Any									=					1
             __A   :						Tuple									=					0

     __A   :						Union[str, Any]									=					self.get_config()

     return config, input_ids, tf.convert_to_tensor(__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return BlipTextConfig(
         vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						projection_dim=self.projection_dim ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						dropout=self.dropout ,						attention_dropout=self.attention_dropout ,						max_position_embeddings=self.max_position_embeddings ,						initializer_range=self.initializer_range ,						bos_token_id=self.bos_token_id ,						)

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					TFBlipTextModel(config=__lowerCamelCase  )
     __A   :						Dict									=					model(__lowerCamelCase ,						attention_mask=__lowerCamelCase ,						training=__lowerCamelCase  )
     __A   :						Dict									=					model(__lowerCamelCase ,						training=__lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)  )
     self.parent.assertEqual(result.pooler_output.shape ,						(self.batch_size, self.hidden_size)  )







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.prepare_config_and_inputs()
     __A			,	__A			,	__A   :						Any									=					config_and_inputs
     __A   :						Dict									=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict






@require_tf
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					(TFBlipTextModel,) if is_tf_available() else ()
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					BlipTextModelTester(self  )
     __A   :						Optional[Any]									=					ConfigTester(self ,						config_class=__lowerCamelCase ,						hidden_size=37  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 @unittest.skip(reason='''Blip does not use inputs_embeds'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __A   :						List[str]									=					TFBlipTextModel.from_pretrained(__lowerCamelCase  )
         self.assertIsNotNone(__lowerCamelCase  )







 def  UpperCamelCase__(   self ,						__lowerCamelCase=True  ):



     '''simple docstring'''




     super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase  )











"""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 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					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(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

     self.assertEqual((2, 6) ,						batch.input_ids.shape  )
     self.assertEqual((2, 6) ,						batch.attention_mask.shape  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''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.
     __A   :						Union[str, Any]									=					[
         '''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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




