






"""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 ViTImageProcessor


class 						__lowerCamelCase							(					unittest.TestCase     ):





		'''simple docstring'''






		def __init__(  self				:      Any		,     a_				:      int		,     a_				:      Dict=13		,     a_				:      Tuple=3		,     a_				:      List[Any]=2_24		,     a_				:      Dict=30		,     a_				:      int=4_00		,     a_				:      Tuple=True		,     a_				:      str=None		,     a_				:      Any=True		,     a_				:      Any=[0.5, 0.5, 0.5]		,     a_				:      Dict=[0.5, 0.5, 0.5]		,     ):
				lowerCAmelCase_  :				str   							=     size if size is not None else {'''height''': 18, '''width''': 18}
				lowerCAmelCase_  :				Dict   							=     parent
				lowerCAmelCase_  :				str   							=     batch_size
				lowerCAmelCase_  :				List[str]   							=     num_channels
				lowerCAmelCase_  :				Optional[Any]   							=     image_size
				lowerCAmelCase_  :				Union[str, Any]   							=     min_resolution
				lowerCAmelCase_  :				List[Any]   							=     max_resolution
				lowerCAmelCase_  :				Any   							=     do_resize
				lowerCAmelCase_  :				str   							=     size
				lowerCAmelCase_  :				str   							=     do_normalize
				lowerCAmelCase_  :				Union[str, Any]   							=     image_mean
				lowerCAmelCase_  :				Union[str, Any]   							=     image_std





		def   lowerCamelCase			(  self				:      int      ):
				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 						__lowerCamelCase							(					snake_case__					,				unittest.TestCase     ):





		'''simple docstring'''






		a_      :							Any  =				ViTImageProcessor if is_vision_available() else None





		def   lowerCamelCase			(  self				:      Tuple      ):
				lowerCAmelCase_  :				List[Any]   							=     EfficientFormerImageProcessorTester(self      )





		@property
		def   lowerCamelCase			(  self				:      Tuple      ):
				return self.image_proc_tester.prepare_image_processor_dict()





		def   lowerCamelCase			(  self				:      Tuple      ):
				lowerCAmelCase_  :				Tuple   							=     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   lowerCamelCase			(  self				:      Optional[Any]      ):
				pass





		def   lowerCamelCase			(  self				:      Tuple      ):
				# Initialize image_processor
				lowerCAmelCase_  :				str   							=     self.image_processing_class(**self.image_processor_dict      )
				# create random PIL images
				lowerCAmelCase_  :				Optional[int]   							=     prepare_image_inputs(self.image_proc_tester		,     equal_resolution=__lowercase      )
				for image in image_inputs:
						self.assertIsInstance(__lowercase		,     Image.Image      )

				# Test not batched input
				lowerCAmelCase_  :				str   							=     image_processor(image_inputs[0]		,     return_tensors="pt"      ).pixel_values
				self.assertEqual(
				    encoded_images.shape		,     (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )		,     )

				# Test batched
				lowerCAmelCase_  :				List[Any]   							=     image_processor(__lowercase		,     return_tensors="pt"      ).pixel_values
				self.assertEqual(
				    encoded_images.shape		,     (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )		,     )





		def   lowerCamelCase			(  self				:      str      ):
				# Initialize image_processor
				lowerCAmelCase_  :				int   							=     self.image_processing_class(**self.image_processor_dict      )
				# create random numpy tensors
				lowerCAmelCase_  :				Optional[int]   							=     prepare_image_inputs(self.image_proc_tester		,     equal_resolution=__lowercase		,     numpify=__lowercase      )
				for image in image_inputs:
						self.assertIsInstance(__lowercase		,     np.ndarray      )

				# Test not batched input
				lowerCAmelCase_  :				Any   							=     image_processor(image_inputs[0]		,     return_tensors="pt"      ).pixel_values
				self.assertEqual(
				    encoded_images.shape		,     (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )		,     )

				# Test batched
				lowerCAmelCase_  :				List[str]   							=     image_processor(__lowercase		,     return_tensors="pt"      ).pixel_values
				self.assertEqual(
				    encoded_images.shape		,     (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )		,     )





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

				# Test not batched input
				lowerCAmelCase_  :				Optional[Any]   							=     image_processor(image_inputs[0]		,     return_tensors="pt"      ).pixel_values
				self.assertEqual(
				    encoded_images.shape		,     (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )		,     )

				# Test batched
				lowerCAmelCase_  :				Optional[Any]   							=     image_processor(__lowercase		,     return_tensors="pt"      ).pixel_values
				self.assertEqual(
				    encoded_images.shape		,     (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_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__)




