






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      tempfile.mkdtemp()

								__magic_name__		:List[Any]        =      [
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''的''',
								    '''价''',
								    '''格''',
								    '''是''',
								    '''15''',
								    '''便''',
								    '''alex''',
								    '''##andra''',
								    '''，''',
								    '''。''',
								    '''-''',
								    '''t''',
								    '''shirt''',
								]
								__magic_name__		:Tuple        =      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]     )     )

								__magic_name__		:str        =      {
								    '''do_resize''': True,
								    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
								    '''do_center_crop''': True,
								    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
								    '''do_normalize''': True,
								    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
								    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
								    '''do_convert_rgb''': True,
								}
								__magic_name__		:Union[str, Any]        =      os.path.join(self.tmpdirname       ,					__lowerCAmelCase     )
								with open(self.image_processor_file       ,					'''w'''       ,					encoding='''utf-8'''     ) as fp:
												json.dump(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return BertTokenizer.from_pretrained(self.tmpdirname       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return BertTokenizerFast.from_pretrained(self.tmpdirname       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname       ,					**__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								shutil.rmtree(self.tmpdirname     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      [np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta     )]

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

								return image_inputs







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_tokenizer()
								__magic_name__		:List[str]        =      self.get_rust_tokenizer()
								__magic_name__		:List[Any]        =      self.get_image_processor()

								__magic_name__		:Optional[int]        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )
								processor_slow.save_pretrained(self.tmpdirname     )
								__magic_name__		:Any        =      ChineseCLIPProcessor.from_pretrained(self.tmpdirname       ,					use_fast=__lowerCAmelCase     )

								__magic_name__		:str        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )
								processor_fast.save_pretrained(self.tmpdirname     )
								__magic_name__		:Tuple        =      ChineseCLIPProcessor.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       ,					__lowerCAmelCase     )
								self.assertIsInstance(processor_fast.tokenizer       ,					__lowerCAmelCase     )

								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       ,					__lowerCAmelCase     )
								self.assertIsInstance(processor_fast.image_processor       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      ChineseCLIPProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()     )
								processor.save_pretrained(self.tmpdirname     )

								__magic_name__		:str        =      self.get_tokenizer(cls_token='''(CLS)'''       ,					sep_token='''(SEP)'''     )
								__magic_name__		:Optional[int]        =      self.get_image_processor(do_normalize=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      ChineseCLIPProcessor.from_pretrained(
								    self.tmpdirname       ,					cls_token='''(CLS)'''       ,					sep_token='''(SEP)'''       ,					do_normalize=__lowerCAmelCase     )

								self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab()     )
								self.assertIsInstance(processor.tokenizer       ,					__lowerCAmelCase     )

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.get_image_processor()
								__magic_name__		:Union[str, Any]        =      self.get_tokenizer()

								__magic_name__		:Optional[Any]        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )

								__magic_name__		:Any        =      self.prepare_image_inputs()

								__magic_name__		:Optional[int]        =      image_processor(__lowerCAmelCase       ,					return_tensors='''np'''     )
								__magic_name__		:Optional[int]        =      processor(images=__lowerCAmelCase       ,					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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.get_image_processor()
								__magic_name__		:Dict        =      self.get_tokenizer()

								__magic_name__		:List[str]        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )

								__magic_name__		:Tuple        =      '''Alexandra，T-shirt的价格是15便士。'''

								__magic_name__		:Tuple        =      processor(text=__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      tokenizer(__lowerCAmelCase     )

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.get_image_processor()
								__magic_name__		:int        =      self.get_tokenizer()

								__magic_name__		:Tuple        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      '''Alexandra，T-shirt的价格是15便士。'''
								__magic_name__		:int        =      self.prepare_image_inputs()

								__magic_name__		:int        =      processor(text=__lowerCAmelCase       ,					images=__lowerCAmelCase     )

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

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.get_image_processor()
								__magic_name__		:int        =      self.get_tokenizer()

								__magic_name__		:List[str]        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__magic_name__		:str        =      processor.batch_decode(__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      tokenizer.batch_decode(__lowerCAmelCase     )

								self.assertListEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_image_processor()
								__magic_name__		:Optional[Any]        =      self.get_tokenizer()

								__magic_name__		:Optional[int]        =      ChineseCLIPProcessor(tokenizer=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )

								__magic_name__		:Dict        =      '''Alexandra，T-shirt的价格是15便士。'''
								__magic_name__		:int        =      self.prepare_image_inputs()

								__magic_name__		:List[Any]        =      processor(text=__lowerCAmelCase       ,					images=__lowerCAmelCase     )

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      :			Tuple							=		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			List[str]							=		[
					    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TableTransformerForObjectDetection""",
					    """TableTransformerModel""",
					    """TableTransformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_table_transformer import (
					    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    TableTransformerConfig,
					    TableTransformerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_table_transformer import (
										    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TableTransformerForObjectDetection,
										    TableTransformerModel,
										    TableTransformerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






