

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  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					BarthezTokenizer
   UpperCamelCase       					=					BarthezTokenizerFast
   UpperCamelCase       					=					True
   UpperCamelCase       					=					True



   def 	__magic_name__   (							self							:					Any						):
     super().setUp()

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



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							Dict						=			'''<pad>'''
     UpperCAmelCase	:							str						=			1

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



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							int						=			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(__A						),		1_0_1_1_2_2						)



   def 	__magic_name__   (							self							:					Dict						):
     self.assertEqual(self.get_tokenizer().vocab_size,		1_0_1_1_2_2						)



   @require_torch
   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							Optional[Any]						=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     UpperCAmelCase	:							str						=			[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

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

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



   def 	__magic_name__   (							self							:					str						):
     if not self.test_rust_tokenizer:
       return

     UpperCAmelCase	:							int						=			self.get_tokenizer()
     UpperCAmelCase	:							List[Any]						=			self.get_rust_tokenizer()

     UpperCAmelCase	:							List[str]						=			'''I was born in 92000, and this is falsé.'''

     UpperCAmelCase	:							List[str]						=			tokenizer.tokenize(__A						)
     UpperCAmelCase	:							List[Any]						=			rust_tokenizer.tokenize(__A						)
     self.assertListEqual(__A,		__A						)

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

     UpperCAmelCase	:							Any						=			self.get_rust_tokenizer()
     UpperCAmelCase	:							Union[str, Any]						=			tokenizer.encode(__A						)
     UpperCAmelCase	:							List[str]						=			rust_tokenizer.encode(__A						)
     self.assertListEqual(__A,		__A						)





   @slow
   def 	__magic_name__   (							self							:					Tuple						):
     # fmt: off
     UpperCAmelCase	:							Dict						=			{'''input_ids''': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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	:							Tuple						=			[
         '''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=__A,		model_name='''moussaKam/mbarthez''',		revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''',		sequences=__A,		)








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor





class  __UpperCAmelCase					(	unittest.TestCase						):



   def __init__(							self							:					List[Any],		__A							:					str,		__A							:					List[str]=7,		__A							:					List[str]=3,		__A							:					Optional[int]=1_8,		__A							:					List[Any]=3_0,		__A							:					Tuple=4_0_0,		__A							:					Tuple=True,		__A							:					List[Any]=None,		__A							:					str=True,		__A							:					int=None,		__A							:					Optional[Any]=True,		__A							:					List[Any]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],		__A							:					List[str]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],		__A							:					Tuple=True,		):
     UpperCAmelCase	:							int						=			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     UpperCAmelCase	:							Optional[Any]						=			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
     UpperCAmelCase	:							int						=			parent
     UpperCAmelCase	:							Union[str, Any]						=			batch_size
     UpperCAmelCase	:							List[Any]						=			num_channels
     UpperCAmelCase	:							Dict						=			image_size
     UpperCAmelCase	:							List[str]						=			min_resolution
     UpperCAmelCase	:							Optional[Any]						=			max_resolution
     UpperCAmelCase	:							Union[str, Any]						=			do_resize
     UpperCAmelCase	:							Dict						=			size
     UpperCAmelCase	:							Any						=			do_center_crop
     UpperCAmelCase	:							Union[str, Any]						=			crop_size
     UpperCAmelCase	:							List[str]						=			do_normalize
     UpperCAmelCase	:							Optional[Any]						=			image_mean
     UpperCAmelCase	:							Optional[Any]						=			image_std
     UpperCAmelCase	:							List[Any]						=			do_convert_rgb



   def 	__magic_name__   (							self							:					int						):
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }





   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					Any=False,		__A							:					str=False,		__A							:					List[Any]=False						):
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       UpperCAmelCase	:							Dict						=			[]
       for i in range(self.batch_size						):
         image_inputs.append(
             np.random.randint(
                 2_5_5,		size=(self.num_channels, self.max_resolution, self.max_resolution),		dtype=np.uinta						)						)
     else:
       UpperCAmelCase	:							Tuple						=			[]
       for i in range(self.batch_size						):
         UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			np.random.choice(np.arange(self.min_resolution,		self.max_resolution						),		2						)
         image_inputs.append(np.random.randint(2_5_5,		size=(self.num_channels, width, height),		dtype=np.uinta						)						)

     if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       UpperCAmelCase	:							Optional[int]						=			[Image.fromarray(np.moveaxis(__A,		0,		-1						)						) for x in image_inputs]

     if torchify:
       UpperCAmelCase	:							str						=			[torch.from_numpy(__A						) for x in image_inputs]

     return image_inputs






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ChineseCLIPImageProcessor if is_vision_available() else None



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Optional[Any]						=			ChineseCLIPImageProcessingTester(self,		do_center_crop=__A						)



   @property
   def 	__magic_name__   (							self							:					Tuple						):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__A,		'''do_resize'''						)						)
     self.assertTrue(hasattr(__A,		'''size'''						)						)
     self.assertTrue(hasattr(__A,		'''do_center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__A,		'''image_std'''						)						)
     self.assertTrue(hasattr(__A,		'''do_convert_rgb'''						)						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size,		{'''height''': 2_2_4, '''width''': 2_2_4}						)
     self.assertEqual(image_processor.crop_size,		{'''height''': 1_8, '''width''': 1_8}						)

     UpperCAmelCase	:							int						=			self.image_processing_class.from_dict(self.image_processor_dict,		size=4_2,		crop_size=8_4						)
     self.assertEqual(image_processor.size,		{'''shortest_edge''': 4_2}						)
     self.assertEqual(image_processor.crop_size,		{'''height''': 8_4, '''width''': 8_4}						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     pass



   def 	__magic_name__   (							self							:					Tuple						):
     # Initialize image_processing
     UpperCAmelCase	:							Dict						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Optional[int]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		Image.Image						)

     # Test not batched input
     UpperCAmelCase	:							str						=			image_processing(image_inputs[0],		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)

     # Test batched
     UpperCAmelCase	:							Any						=			image_processing(__A,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     # Initialize image_processing
     UpperCAmelCase	:							List[str]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     UpperCAmelCase	:							List[Any]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A,		numpify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		np.ndarray						)

     # Test not batched input
     UpperCAmelCase	:							List[str]						=			image_processing(image_inputs[0],		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)

     # Test batched
     UpperCAmelCase	:							Dict						=			image_processing(__A,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)





   def 	__magic_name__   (							self							:					Any						):
     # Initialize image_processing
     UpperCAmelCase	:							str						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     UpperCAmelCase	:							Optional[Any]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A,		torchify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		torch.Tensor						)

     # Test not batched input
     UpperCAmelCase	:							str						=			image_processing(image_inputs[0],		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)

     # Test batched
     UpperCAmelCase	:							List[str]						=			image_processing(__A,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ChineseCLIPImageProcessor if is_vision_available() else None



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							str						=			ChineseCLIPImageProcessingTester(self,		num_channels=4,		do_center_crop=__A						)
     UpperCAmelCase	:							Dict						=			3



   @property
   def 	__magic_name__   (							self							:					List[Any]						):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__A,		'''do_resize'''						)						)
     self.assertTrue(hasattr(__A,		'''size'''						)						)
     self.assertTrue(hasattr(__A,		'''do_center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__A,		'''image_std'''						)						)
     self.assertTrue(hasattr(__A,		'''do_convert_rgb'''						)						)



   def 	__magic_name__   (							self							:					List[Any]						):
     pass





   def 	__magic_name__   (							self							:					Tuple						):
     # Initialize image_processing
     UpperCAmelCase	:							Optional[Any]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Dict						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		Image.Image						)

     # Test not batched input
     UpperCAmelCase	:							Tuple						=			image_processing(image_inputs[0],		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)

     # Test batched
     UpperCAmelCase	:							Tuple						=			image_processing(__A,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape,		(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)





