


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 A__     ( unittest.TestCase  ):



  """simple docstring"""



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

  def    _UpperCamelCase( self			:  Tuple       ):
        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 A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		DPTImageProcessor if is_vision_available() else None

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Tuple     =   DPTImageProcessingTester(self       )

  @property
  def    _UpperCamelCase( self			:  Dict       ):
        return self.image_processor_tester.prepare_image_processor_dict()

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  List[Any]     =   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"image_mean"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"image_std"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_normalize"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_resize"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"size"       )       )

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  List[Any]     =   self.image_processing_class.from_dict(self.image_processor_dict       )
        self.assertEqual(image_processor.size				,				{"height": 18, "width": 18}       )

        a__	:  List[Any]     =   self.image_processing_class.from_dict(self.image_processor_dict				,				size=42       )
        self.assertEqual(image_processor.size				,				{"height": 42, "width": 42}       )

  def    _UpperCamelCase( self			:  str       ):
        # Initialize image_processing
        a__	:  Tuple     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        a__	:  Optional[int]     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				Image.Image       )

        # Test not batched input
        a__	:  int     =   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
        a__	:  int     =   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.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

  def    _UpperCamelCase( self			:  Optional[int]       ):
        # Initialize image_processing
        a__	:  str     =   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        a__	:  Any     =   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
        a__	:  Union[str, 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
        a__	:  int     =   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.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

  def    _UpperCamelCase( self			:  List[Any]       ):
        # Initialize image_processing
        a__	:  str     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        a__	:  Optional[Any]     =   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
        a__	:  Dict     =   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
        a__	:  Optional[Any]     =   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.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)









from typing import TYPE_CHECKING

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


UpperCAmelCase__      			=						{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						['''PoolFormerFeatureExtractor''']
			UpperCAmelCase__      			=						['''PoolFormerImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PoolFormerForImageClassification''',
			    '''PoolFormerModel''',
			    '''PoolFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_poolformer import (
			    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    PoolFormerConfig,
			    PoolFormerOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_poolformer import PoolFormerFeatureExtractor
						from .image_processing_poolformer import PoolFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_poolformer import (
						    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PoolFormerForImageClassification,
						    PoolFormerModel,
						    PoolFormerPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)





