



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Any		       =							["image_processor", "tokenizer"]
	a__				: Optional[int]		       =							"BlipImageProcessor"
	a__				: Union[str, Any]		       =							"AutoTokenizer"




	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Any						,  __UpperCAmelCase     :						int					)      ->			int:
							UpperCAmelCase_=       False
							super().__init__(__UpperCAmelCase						,  __UpperCAmelCase					)
							UpperCAmelCase_=       self.image_processor




	def __call__(     self     :						Dict						,  __UpperCAmelCase     :						ImageInput = None						,  __UpperCAmelCase     :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						Union[bool, str, PaddingStrategy] = False						,  __UpperCAmelCase     :						Union[bool, str, TruncationStrategy] = None						,  __UpperCAmelCase     :						Optional[int] = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						Optional[int] = None						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						Optional[Union[str, TensorType]] = None						,  **__UpperCAmelCase     :						Optional[Any]						,  )      ->			BatchEncoding:
							if images is None and text is None:
													raise ValueError("""You have to specify either images or text."""					)

							# Get only text
							if images is None:
													UpperCAmelCase_=       self.tokenizer
													UpperCAmelCase_=       self.tokenizer(
													    text=__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase						,  padding=__UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  stride=__UpperCAmelCase						,  pad_to_multiple_of=__UpperCAmelCase						,  return_attention_mask=__UpperCAmelCase						,  return_overflowing_tokens=__UpperCAmelCase						,  return_special_tokens_mask=__UpperCAmelCase						,  return_offsets_mapping=__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase						,  return_length=__UpperCAmelCase						,  verbose=__UpperCAmelCase						,  return_tensors=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
													return text_encoding

							# add pixel_values
							UpperCAmelCase_=       self.image_processor(__UpperCAmelCase						,  return_tensors=__UpperCAmelCase					)

							if text is not None:
													UpperCAmelCase_=       self.tokenizer(
													    text=__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase						,  padding=__UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  stride=__UpperCAmelCase						,  pad_to_multiple_of=__UpperCAmelCase						,  return_attention_mask=__UpperCAmelCase						,  return_overflowing_tokens=__UpperCAmelCase						,  return_special_tokens_mask=__UpperCAmelCase						,  return_offsets_mapping=__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase						,  return_length=__UpperCAmelCase						,  verbose=__UpperCAmelCase						,  return_tensors=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							else:
													UpperCAmelCase_=       None

							if text_encoding is not None:
													encoding_image_processor.update(__UpperCAmelCase					)

							return encoding_image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  *__UpperCAmelCase     :						Any						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			Dict:
							return self.tokenizer.batch_decode(*__UpperCAmelCase						,  **__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  *__UpperCAmelCase     :						List[str]						,  **__UpperCAmelCase     :						Dict					)      ->			List[Any]:
							return self.tokenizer.decode(*__UpperCAmelCase						,  **__UpperCAmelCase					)




	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							UpperCAmelCase_=       self.tokenizer.model_input_names
							UpperCAmelCase_=       self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









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 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

							UpperCAmelCase_=       [Image.fromarray(np.moveaxis(__UpperCAmelCase						,  0						,  -1					)					) for x in image_inputs]

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

							UpperCAmelCase_=       self.get_tokenizer(bos_token="""(BOS)"""						,  eos_token="""(EOS)"""					)
							UpperCAmelCase_=       self.get_image_processor(do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

							UpperCAmelCase_=       BlipaProcessor.from_pretrained(
							    self.tmpdirname						,  bos_token="""(BOS)"""						,  eos_token="""(EOS)"""						,  do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processor(images=__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




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

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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




