



'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor

class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=7     ,	A_=3     ,	A_=18     ,	A_=30     ,	A_=400     ,	A_=True     ,	A_=None     ,	A_=True     ,	A_=None     ,	A_=True     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     size if size is not None else {'''shortest_edge''': 20}
							lowerCAmelCase   =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     min_resolution
							lowerCAmelCase   =     max_resolution
							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     size
							lowerCAmelCase   =     do_center_crop
							lowerCAmelCase   =     crop_size
							lowerCAmelCase   =     do_flip_channel_order




						def     __snake_case   ( self				)     ->     List[Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}







@require_torch
@require_vision
class __snake_case(				__lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		MobileViTImageProcessor if is_vision_available() else None


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     MobileViTImageProcessingTester(self				)


						@property
						def     __snake_case   ( self				)     ->     Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowerCamelCase__     ,	"""do_resize"""				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,	"""size"""				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,	"""do_center_crop"""				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,	"""center_crop"""				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,	"""do_flip_channel_order"""				)				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 20}				)
							self.assertEqual(image_processor.crop_size     ,	{"""height""": 18, """width""": 18}				)

							lowerCAmelCase   =     self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42     ,	crop_size=84				)
							self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 42}				)
							self.assertEqual(image_processor.crop_size     ,	{"""height""": 84, """width""": 84}				)


						def     __snake_case   ( self				)     ->     List[str]:
							pass


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=lowerCamelCase__				)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase__     ,	Image.Image				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(lowerCamelCase__     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=lowerCamelCase__     ,	numpify=lowerCamelCase__				)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase__     ,	np.ndarray				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(lowerCamelCase__     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)




						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=lowerCamelCase__     ,	torchify=lowerCamelCase__				)
							for image in image_inputs:
								self.assertIsInstance(lowerCamelCase__     ,	torch.Tensor				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(lowerCamelCase__     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)




'''simple docstring'''

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 __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     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:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

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

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

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)