


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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														__snake_case						:     Dict									=  tempfile.mkdtemp()

														__snake_case						:     Union[str, Any]									=  BlipImageProcessor()
														__snake_case						:     Union[str, Any]									=  GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"							)

														__snake_case						:     Optional[int]									=  BlipaProcessor(lowerCamelCase       ,      lowerCamelCase							)

														processor.save_pretrained(self.tmpdirname							)

							def        __snake_case   (				self			:    Optional[Any]       ,      **lowerCamelCase			:    Optional[Any]							)      ->       str:
														return AutoProcessor.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							).tokenizer

							def        __snake_case   (				self			:    str       ,      **lowerCamelCase			:    List[str]							)      ->       str:
														return AutoProcessor.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							).image_processor

							def        __snake_case   (				self			:    Dict							)      ->       Tuple:
														shutil.rmtree(self.tmpdirname							)

							def        __snake_case   (				self			:    Dict							)      ->       int:
														__snake_case						:     Union[str, Any]									=  [np.random.randint(255       ,      size=(3, 30, 400)       ,      dtype=np.uinta							)]

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

														return image_inputs

							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case						:     Optional[int]									=  BlipaProcessor(tokenizer=self.get_tokenizer()       ,      image_processor=self.get_image_processor()							)
														processor.save_pretrained(self.tmpdirname							)

														__snake_case						:     Union[str, Any]									=  self.get_tokenizer(bos_token="(BOS)"       ,      eos_token="(EOS)"							)
														__snake_case						:     Dict									=  self.get_image_processor(do_normalize=lowerCamelCase       ,      padding_value=1.0							)

														__snake_case						:     List[Any]									=  BlipaProcessor.from_pretrained(
														    self.tmpdirname       ,      bos_token="(BOS)"       ,      eos_token="(EOS)"       ,      do_normalize=lowerCamelCase       ,      padding_value=1.0							)

														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        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_image_processor()
														__snake_case						:     Optional[int]									=  self.get_tokenizer()

														__snake_case						:     Dict									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  self.prepare_image_inputs()

														__snake_case						:     Any									=  image_processor(lowerCamelCase       ,      return_tensors="np"							)
														__snake_case						:     List[str]									=  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        __snake_case   (				self			:    Union[str, Any]							)      ->       List[str]:
														__snake_case						:     Any									=  self.get_image_processor()
														__snake_case						:     int									=  self.get_tokenizer()

														__snake_case						:     List[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  "lower newer"

														__snake_case						:     Union[str, Any]									=  processor(text=lowerCamelCase							)

														__snake_case						:     Tuple									=  tokenizer(lowerCamelCase       ,      return_token_type_ids=lowerCamelCase							)

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

							def        __snake_case   (				self			:    Any							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_image_processor()
														__snake_case						:     Union[str, Any]									=  self.get_tokenizer()

														__snake_case						:     Tuple									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     List[Any]									=  "lower newer"
														__snake_case						:     List[Any]									=  self.prepare_image_inputs()

														__snake_case						:     Optional[int]									=  processor(text=lowerCamelCase       ,      images=lowerCamelCase							)

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

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

							def        __snake_case   (				self			:    Dict							)      ->       Any:
														__snake_case						:     List[Any]									=  self.get_image_processor()
														__snake_case						:     str									=  self.get_tokenizer()

														__snake_case						:     Optional[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														__snake_case						:     Union[str, Any]									=  processor.batch_decode(lowerCamelCase							)
														__snake_case						:     List[str]									=  tokenizer.batch_decode(lowerCamelCase							)

														self.assertListEqual(lowerCamelCase       ,      lowerCamelCase							)






							def        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case						:     Any									=  self.get_image_processor()
														__snake_case						:     Any									=  self.get_tokenizer()

														__snake_case						:     List[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     str									=  "lower newer"
														__snake_case						:     Optional[Any]									=  self.prepare_image_inputs()

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

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





'''simple docstring'''






import warnings

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






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["image_processor", "tokenizer"]
       UpperCAmelCase		 ="CLIPImageProcessor"
       UpperCAmelCase		 =("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'    ,				snake_case    ,				)
             _UpperCAmelCase  :  str				       =kwargs.pop('feature_extractor')

          _UpperCAmelCase  :  List[str]				       =image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.')
          if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.')

          super().__init__(snake_case    ,				snake_case)






       def __call__(     self    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Optional[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  :  List[Any]				       =self.tokenizer(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  :  Any				       =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      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    int:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Optional[int]:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case    ,				**snake_case)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.tokenizer.model_input_names
          _UpperCAmelCase  :  Any				       =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






       @property
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				snake_case    ,				)
          return self.image_processor_class






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




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				snake_case    ,				)
          return self.image_processor

