
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class 		__lowerCamelCase     (   unittest.TestCase		):



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     tempfile.mkdtemp()

								UpperCAmelCase:					Optional[Any] 						=     CLIPImageProcessor()
								UpperCAmelCase:					Any 						=     BertTokenizer.from_pretrained(
								    "hf-internal-testing/tiny-random-BertModel"	,				model_input_names=["input_ids", "attention_mask"]							)

								UpperCAmelCase:					Any 						=     GitProcessor(__snake_case	,				__snake_case							)

								processor.save_pretrained(self.tmpdirname							)



							def     A__ (     self	,				**__snake_case							)						->     str:






								"""simple docstring"""



								return AutoProcessor.from_pretrained(self.tmpdirname	,				**__snake_case							).tokenizer



							def     A__ (     self	,				**__snake_case							)						->     Tuple:






								"""simple docstring"""



								return AutoProcessor.from_pretrained(self.tmpdirname	,				**__snake_case							).image_processor



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								shutil.rmtree(self.tmpdirname							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     [np.random.randint(2_5_5	,				size=(3, 3_0, 4_0_0)	,				dtype=np.uinta							)]

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

								return image_inputs



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     GitProcessor(tokenizer=self.get_tokenizer()	,				image_processor=self.get_image_processor()							)
								processor.save_pretrained(self.tmpdirname							)

								UpperCAmelCase:					List[str] 						=     self.get_tokenizer(bos_token="(BOS)"	,				eos_token="(EOS)"							)
								UpperCAmelCase:					Tuple 						=     self.get_image_processor(do_normalize=__snake_case	,				padding_value=1.0							)

								UpperCAmelCase:					List[Any] 						=     GitProcessor.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     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



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

								UpperCAmelCase:					Dict 						=     GitProcessor(tokenizer=__snake_case	,				image_processor=__snake_case							)

								UpperCAmelCase:					List[Any] 						=     self.prepare_image_inputs()

								UpperCAmelCase:					Any 						=     image_processor(__snake_case	,				return_tensors="np"							)
								UpperCAmelCase:					List[str] 						=     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     A__ (     self							)						->     List[str]:






								"""simple docstring"""



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

								UpperCAmelCase:					int 						=     GitProcessor(tokenizer=__snake_case	,				image_processor=__snake_case							)

								UpperCAmelCase:					Optional[int] 						=     "lower newer"

								UpperCAmelCase:					Dict 						=     processor(text=__snake_case							)

								UpperCAmelCase:					Union[str, Any] 						=     tokenizer(__snake_case	,				return_token_type_ids=__snake_case							)

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



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     self.get_image_processor()
								UpperCAmelCase:					Dict 						=     self.get_tokenizer()

								UpperCAmelCase:					Optional[int] 						=     GitProcessor(tokenizer=__snake_case	,				image_processor=__snake_case							)

								UpperCAmelCase:					Union[str, Any] 						=     "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", "attention_mask", "pixel_values"]							)

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



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self.get_image_processor()
								UpperCAmelCase:					int 						=     self.get_tokenizer()

								UpperCAmelCase:					Union[str, Any] 						=     GitProcessor(tokenizer=__snake_case	,				image_processor=__snake_case							)

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

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

								self.assertListEqual(__snake_case	,				__snake_case							)




							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.get_image_processor()
								UpperCAmelCase:					Optional[Any] 						=     self.get_tokenizer()

								UpperCAmelCase:					Union[str, Any] 						=     GitProcessor(tokenizer=__snake_case	,				image_processor=__snake_case							)

								UpperCAmelCase:					int 						=     "lower newer"
								UpperCAmelCase:					Optional[int] 						=     self.prepare_image_inputs()

								UpperCAmelCase:					Any 						=     processor(text=__snake_case	,				images=__snake_case							)

								# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
								self.assertListEqual(list(inputs.keys()							)	,				["input_ids", "attention_mask", "pixel_values"]							)


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none."							)

								if text is not None:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**__snake_case							)	,				tensor_type=__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								return self.tokenizer.batch_decode(*__snake_case	,				**__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



								return self.tokenizer.decode(*__snake_case	,				**__snake_case							)




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
