





"""simple docstring"""





import unittest

import numpy as np

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

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 DPTImageProcessor
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:List[str]    ,     __lowercase	:Union[str, Any]=7    ,     __lowercase	:Any=3    ,     __lowercase	:List[str]=18    ,     __lowercase	:Dict=30    ,     __lowercase	:Optional[Any]=400    ,     __lowercase	:Tuple=True    ,     __lowercase	:Dict=None    ,     __lowercase	:List[Any]=True    ,     __lowercase	:Optional[Any]=[0.5, 0.5, 0.5]    ,     __lowercase	:Tuple=[0.5, 0.5, 0.5]    ,     ):
											__lowerCamelCase   :   List[str]       =size if size is not None else {'''height''': 18, '''width''': 18}
											__lowerCamelCase   :   Optional[int]       =parent
											__lowerCamelCase   :   Optional[int]       =batch_size
											__lowerCamelCase   :   Union[str, Any]       =num_channels
											__lowerCamelCase   :   List[Any]       =image_size
											__lowerCamelCase   :   List[Any]       =min_resolution
											__lowerCamelCase   :   Dict       =max_resolution
											__lowerCamelCase   :   Optional[Any]       =do_resize
											__lowerCamelCase   :   Optional[Any]       =size
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   Tuple       =image_mean
											__lowerCamelCase   :   List[Any]       =image_std




				def        __lowercase	(  self	:str      ):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Tuple									=		DPTImageProcessor if is_vision_available() else None




				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   List[str]       =DPTImageProcessingTester(self      )




				@property
				def        __lowercase	(  self	:Optional[int]      ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Any       =self.image_processing_class(**self.image_processor_dict      )
											self.assertTrue(hasattr(__lowercase    ,     '''image_mean'''      )      )
											self.assertTrue(hasattr(__lowercase    ,     '''image_std'''      )      )
											self.assertTrue(hasattr(__lowercase    ,     '''do_normalize'''      )      )
											self.assertTrue(hasattr(__lowercase    ,     '''do_resize'''      )      )
											self.assertTrue(hasattr(__lowercase    ,     '''size'''      )      )




				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   List[Any]       =self.image_processing_class.from_dict(self.image_processor_dict      )
											self.assertEqual(image_processor.size    ,     {'''height''': 18, '''width''': 18}      )

											__lowerCamelCase   :   Dict       =self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42      )
											self.assertEqual(image_processor.size    ,     {'''height''': 42, '''width''': 42}      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											# Initialize image_processing
											__lowerCamelCase   :   int       =self.image_processing_class(**self.image_processor_dict      )
											# create random PIL images
											__lowerCamelCase   :   List[Any]       =prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=__lowercase      )
											for image in image_inputs:
																		self.assertIsInstance(__lowercase    ,     Image.Image      )

											# Test not batched input
											__lowerCamelCase   :   Any       =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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )    ,     )

											# Test batched
											__lowerCamelCase   :   Union[str, Any]       =image_processing(__lowercase    ,     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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )    ,     )




				def        __lowercase	(  self	:Optional[Any]      ):
											# Initialize image_processing
											__lowerCamelCase   :   Optional[int]       =self.image_processing_class(**self.image_processor_dict      )
											# create random numpy tensors
											__lowerCamelCase   :   Optional[Any]       =prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=__lowercase    ,     numpify=__lowercase      )
											for image in image_inputs:
																		self.assertIsInstance(__lowercase    ,     np.ndarray      )

											# Test not batched input
											__lowerCamelCase   :   List[Any]       =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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )    ,     )

											# Test batched
											__lowerCamelCase   :   str       =image_processing(__lowercase    ,     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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )    ,     )




				def        __lowercase	(  self	:List[str]      ):
											# Initialize image_processing
											__lowerCamelCase   :   Optional[int]       =self.image_processing_class(**self.image_processor_dict      )
											# create random PyTorch tensors
											__lowerCamelCase   :   Any       =prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=__lowercase    ,     torchify=__lowercase      )
											for image in image_inputs:
																		self.assertIsInstance(__lowercase    ,     torch.Tensor      )

											# Test not batched input
											__lowerCamelCase   :   Tuple       =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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )    ,     )

											# Test batched
											__lowerCamelCase   :   List[Any]       =image_processing(__lowercase    ,     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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )    ,     )











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   ['Pix2StructImageProcessor']


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Pix2StructPreTrainedModel',
							    'Pix2StructForConditionalGeneration',
							    'Pix2StructVisionModel',
							    'Pix2StructTextModel',
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_UpperCamelCase					      =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




