




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  A		(  __UpperCAmelCase     ):
		__snake_case									=   ['image_processor', 'tokenizer']
		__snake_case									=   'BlipImageProcessor'
		__snake_case									=   'AutoTokenizer'



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  False
								super().__init__(UpperCamelCase__,	UpperCamelCase__   )
								lowerCAmelCase_      			=  self.image_processor



		def __call__(     self,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = True,	UpperCamelCase__ = False,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = 0,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = True,	UpperCamelCase__ = None,	**UpperCamelCase__,	):







								"""simple docstring"""



								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:
														lowerCAmelCase_      			=  self.tokenizer
														lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  self.image_processor(UpperCamelCase__,	return_tensors=UpperCamelCase__   )

								if text is not None:
														lowerCAmelCase_      			=  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:
														lowerCAmelCase_      			=  None

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

								return encoding_image_processor



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



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







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer.model_input_names
								lowerCAmelCase_      			=  self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )










import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


@require_torch
@require_vision
class  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




