
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						tempfile.mkdtemp()

							# fmt: off
							UpperCamelCase_:							Optional[int]	=						['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
							# fmt: on
							UpperCamelCase_:							List[Any]	=						os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["""vocab_file"""]      )
							with open(self.vocab_file						,	"""w"""						,	encoding="""utf-8"""      ) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )

							UpperCamelCase_:							List[Any]	=						{
							    '''do_resize''': True,
							    '''size''': {'''height''': 18, '''width''': 18},
							    '''do_normalize''': True,
							    '''image_mean''': [0.5, 0.5, 0.5],
							    '''image_std''': [0.5, 0.5, 0.5],
							}
							UpperCamelCase_:							Dict	=						os.path.join(self.tmpdirname						,	snake_case_      )
							with open(self.image_processor_file						,	"""w"""						,	encoding="""utf-8"""      ) as fp:
											json.dump(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any						,	**snake_case_ :       Any      ):
							return BertTokenizer.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Any      ):
							return ViTImageProcessor.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Optional[int]	=						[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta      )]

							UpperCamelCase_:							Any	=						[Image.fromarray(np.moveaxis(snake_case_						,	0						,	-1      )      ) for x in image_inputs]

							return image_inputs

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Optional[int]	=						self.get_image_processor()

							UpperCamelCase_:							int	=						VisionTextDualEncoderProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Optional[int]	=						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	(BertTokenizer, BertTokenizerFast)      )

							self.assertEqual(processor.image_processor.to_json_string()						,	image_processor.to_json_string()      )
							self.assertIsInstance(processor.image_processor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Optional[Any]	=						VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							str	=						self.get_image_processor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							str	=						VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	(BertTokenizer, BertTokenizerFast)      )

							self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.image_processor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Any	=						self.get_image_processor()
							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer()

							UpperCamelCase_:							Union[str, Any]	=						VisionTextDualEncoderProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							str	=						self.prepare_image_inputs()

							UpperCamelCase_:							Any	=						image_processor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							Union[str, Any]	=						processor(images=snake_case_						,	return_tensors="""np"""      )

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						VisionTextDualEncoderProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							int	=						'''lower newer'''

							UpperCamelCase_:							List[str]	=						processor(text=snake_case_      )

							UpperCamelCase_:							Tuple	=						tokenizer(snake_case_      )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Optional[int]	=						self.get_image_processor()
							UpperCamelCase_:							Dict	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						VisionTextDualEncoderProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Dict	=						'''lower newer'''
							UpperCamelCase_:							List[Any]	=						self.prepare_image_inputs()

							UpperCamelCase_:							Dict	=						processor(text=snake_case_						,	images=snake_case_      )

							self.assertListEqual(list(inputs.keys()      )						,	["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]      )

							# test if it raises when no input is passed
							with self.assertRaises(snake_case_      ):
											processor()

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							List[str]	=						self.get_image_processor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							str	=						VisionTextDualEncoderProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							Optional[int]	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_image_processor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							Tuple	=						VisionTextDualEncoderProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						'''lower newer'''
							UpperCamelCase_:							Dict	=						self.prepare_image_inputs()

							UpperCamelCase_:							Any	=						processor(text=snake_case_						,	images=snake_case_      )

							self.assertListEqual(list(inputs.keys()      )						,	processor.model_input_names      )








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				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = BarthezTokenizer
			__UpperCamelCase      :						str					      = BarthezTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							super().setUp()

							UpperCamelCase_:							Tuple	=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""      )
							tokenizer.save_pretrained(self.tmpdirname      )
							tokenizer.save_pretrained(self.tmpdirname						,	legacy_format=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						"""<pad>"""
							UpperCamelCase_:							int	=						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 :       Dict      ):
							UpperCamelCase_:							Tuple	=						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_      )						,	10_1122      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	10_1122      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Union[str, Any]	=						[0, 57, 3018, 7_0307, 91, 2]

							UpperCamelCase_:							Union[str, Any]	=						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_:							Any	=						batch.input_ids.tolist()[0]
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()

							UpperCamelCase_:							str	=						"""I was born in 92000, and this is falsé."""

							UpperCamelCase_:							str	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[str]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Tuple	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							Tuple	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       int      ):
							# fmt: off
							UpperCamelCase_:							Optional[Any]	=						{"""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.
							UpperCamelCase_:							str	=						[
							    """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_						,	)






