





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


A   : int             =			get_tests_dir('fixtures/test_sentencepiece.model')

A   : Optional[Any]             =			{'target_lang': 'fi', 'source_lang': 'en'}
A   : Union[str, Any]             =			'>>zh<<'
A   : Dict             =			'Helsinki-NLP/'

if is_torch_available():
		A   : Union[str, Any]             =			'pt'
elif is_tf_available():
		A   : Union[str, Any]             =			'tf'
else:
		A   : Optional[Any]             =			'jax'







@require_sentencepiece
class 			lowerCamelCase					(						lowerCamelCase__     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    MarianTokenizer
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    True


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											super().setUp()
											_snake_case:   Union[str, Any]  					=		['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
											_snake_case:   Union[str, Any]  					=		dict(zip(__lowerCamelCase       ,     range(len(__lowerCamelCase       )       )       )       )
											_snake_case:   Any  					=		Path(self.tmpdirname       )
											save_json(__lowerCamelCase       ,     save_dir / VOCAB_FILES_NAMES['vocab']       )
											save_json(__lowerCamelCase       ,     save_dir / VOCAB_FILES_NAMES['tokenizer_config_file']       )
											if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																	copyfile(__lowerCamelCase       ,     save_dir / VOCAB_FILES_NAMES['source_spm']       )
																	copyfile(__lowerCamelCase       ,     save_dir / VOCAB_FILES_NAMES['target_spm']       )

											_snake_case:   List[str]  					=		MarianTokenizer.from_pretrained(self.tmpdirname       )
											tokenizer.save_pretrained(self.tmpdirname       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     **__snake_case	:  Optional[int]       ):





											'''simple docstring'''





											return MarianTokenizer.from_pretrained(self.tmpdirname       ,     **__lowerCamelCase       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											return (
											    "This is a test",
											    "This is a test",
											)


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   str  					=		'''</s>'''
											_snake_case:   List[str]  					=		0

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		list(self.get_tokenizer().get_vocab().keys()       )

											self.assertEqual(vocab_keys[0]       ,     '</s>'       )
											self.assertEqual(vocab_keys[1]       ,     '<unk>'       )
											self.assertEqual(vocab_keys[-1]       ,     '<pad>'       )
											self.assertEqual(len(__lowerCamelCase       )       ,     9       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											self.assertEqual(self.get_tokenizer().vocab_size       ,     9       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''       )
											_snake_case:   List[str]  					=		en_de_tokenizer(['I am a small frog']       ,     return_tensors=__lowerCamelCase       )
											self.assertIsInstance(__lowerCamelCase       ,     __lowerCamelCase       )
											_snake_case:   int  					=		[38, 1_21, 14, 6_97, 3_88_48, 0]
											self.assertListEqual(__lowerCamelCase       ,     batch.input_ids[0]       )

											_snake_case:   Tuple  					=		tempfile.mkdtemp()
											en_de_tokenizer.save_pretrained(__lowerCamelCase       )
											_snake_case:   Optional[int]  					=		[x.name for x in Path(__lowerCamelCase       ).glob('*'       )]
											self.assertIn('source.spm'       ,     __lowerCamelCase       )
											MarianTokenizer.from_pretrained(__lowerCamelCase       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.get_tokenizer()

											_snake_case:   Union[str, Any]  					=		tok(
											    ['I am a small frog' * 10_00, 'I am a small frog']       ,     padding=__lowerCamelCase       ,     truncation=__lowerCamelCase       ,     return_tensors=__lowerCamelCase       )
											self.assertIsInstance(__lowerCamelCase       ,     __lowerCamelCase       )
											self.assertEqual(batch.input_ids.shape       ,     (2, 5_12)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.get_tokenizer()
											_snake_case:   Dict  					=		tok(['I am a tiny frog', 'I am a small frog']       ,     padding=__lowerCamelCase       ,     return_tensors=__lowerCamelCase       )
											self.assertIsInstance(__lowerCamelCase       ,     __lowerCamelCase       )
											self.assertEqual(batch_smaller.input_ids.shape       ,     (2, 10)       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   int  					=		{'''input_ids''': [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], '''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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=__lowerCamelCase       ,     model_name='Helsinki-NLP/opus-mt-en-de'       ,     revision='1a8c2263da11e68e50938f97e10cd57820bd504c'       ,     decode_kwargs={'use_source_tokenizer': True}       ,     )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'       )

											_snake_case:   Any  					=		'''Tämä on testi'''
											_snake_case:   List[Any]  					=		'''This is a test'''

											_snake_case:   str  					=		[76, 7, 20_47, 2]
											_snake_case:   Dict  					=		[69, 12, 11, 9_40, 2]

											_snake_case:   Union[str, Any]  					=		tokenizer(__lowerCamelCase       ).input_ids
											self.assertListEqual(__lowerCamelCase       ,     __lowerCamelCase       )

											_snake_case:   Dict  					=		tokenizer(text_target=__lowerCamelCase       ).input_ids
											self.assertListEqual(__lowerCamelCase       ,     __lowerCamelCase       )

											_snake_case:   int  					=		tokenizer.decode(__lowerCamelCase       ,     skip_special_tokens=__lowerCamelCase       )
											self.assertEqual(__lowerCamelCase       ,     __lowerCamelCase       )












'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		tempfile.mkdtemp()

											# fmt: off
											_snake_case:   Union[str, Any]  					=		['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
											# fmt: on
											_snake_case:   str  					=		dict(zip(__snake_case       ,     range(len(__snake_case       )       )       )       )
											_snake_case:   str  					=		['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
											_snake_case:   Optional[int]  					=		{'unk_token': '<unk>'}

											_snake_case:   int  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['vocab_file']       )
											_snake_case:   Optional[int]  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	fp.write(json.dumps(__snake_case       ) + '\n'       )
											with open(self.merges_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	fp.write('\n'.join(__snake_case       )       )

											_snake_case:   Dict  					=		{
											    'do_resize': True,
											    'size': 20,
											    'do_center_crop': True,
											    'crop_size': 18,
											    'do_normalize': True,
											    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											_snake_case:   Union[str, Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return CLIPTokenizer.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											shutil.rmtree(self.tmpdirname       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		[np.random.randint(2_55       ,     size=(3, 30, 4_00)       ,     dtype=np.uinta       )]

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

											return image_inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_tokenizer()
											_snake_case:   List[str]  					=		self.get_rust_tokenizer()
											_snake_case:   List[str]  					=		self.get_image_processor()

											_snake_case:   Union[str, Any]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(self.tmpdirname       ,     use_fast=__snake_case       )

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											_snake_case:   int  					=		CLIPProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()       ,     tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()       ,     tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()       ,     tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer       ,     __snake_case       )
											self.assertIsInstance(processor_fast.tokenizer       ,     __snake_case       )

											self.assertEqual(processor_slow.image_processor.to_json_string()       ,     image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()       ,     image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor       ,     __snake_case       )
											self.assertIsInstance(processor_fast.image_processor       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		CLIPProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											_snake_case:   List[Any]  					=		self.get_tokenizer(bos_token='(BOS)'       ,     eos_token='(EOS)'       )
											_snake_case:   List[Any]  					=		self.get_image_processor(do_normalize=__snake_case       ,     padding_value=1.0       )

											_snake_case:   Any  					=		CLIPProcessor.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       ,     __snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   List[str]  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   List[str]  					=		self.prepare_image_inputs()

											_snake_case:   List[str]  					=		image_processor(__snake_case       ,     return_tensors='np'       )
											_snake_case:   Dict  					=		processor(images=__snake_case       ,     return_tensors='np'       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   List[str]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   int  					=		'lower newer'

											_snake_case:   str  					=		processor(text=__snake_case       )

											_snake_case:   List[Any]  					=		tokenizer(__snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.get_image_processor()
											_snake_case:   List[Any]  					=		self.get_tokenizer()

											_snake_case:   Tuple  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Optional[int]  					=		'lower newer'
											_snake_case:   Union[str, Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

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

											# test if it raises when no input is passed
											with pytest.raises(__snake_case       ):
																	processor()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_image_processor()
											_snake_case:   Dict  					=		self.get_tokenizer()

											_snake_case:   str  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   str  					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_snake_case:   List[Any]  					=		processor.batch_decode(__snake_case       )
											_snake_case:   Any  					=		tokenizer.batch_decode(__snake_case       )

											self.assertListEqual(__snake_case       ,     __snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Any  					=		'lower newer'
											_snake_case:   Optional[Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

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





