
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 PoolFormerImageProcessor






class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def __init__(					self     :							Dict			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							Dict=7			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							int=3_0			,			lowerCAmelCase_     :							str=4_0_0			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							Tuple=None			,			lowerCAmelCase_     :							Optional[int]=0.9			,			lowerCAmelCase_     :							List[str]=None			,			lowerCAmelCase_     :							str=True			,			lowerCAmelCase_     :							List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase_     :							List[Any]=[0.5, 0.5, 0.5]			,			):

      """simple docstring"""
      _A:  Union[str, Any]					=       size if size is not None else {'''shortest_edge''': 3_0}
      _A:  List[Any]					=       crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
      _A:  Optional[Any]					=       parent
      _A:  Optional[Any]					=       batch_size
      _A:  Union[str, Any]					=       num_channels
      _A:  Union[str, Any]					=       min_resolution
      _A:  List[Any]					=       max_resolution
      _A:  Dict					=       do_resize_and_center_crop
      _A:  List[Any]					=       size
      _A:  Any					=       crop_pct
      _A:  Optional[int]					=       crop_size
      _A:  Any					=       do_normalize
      _A:  Optional[Any]					=       image_mean
      _A:  Any					=       image_std


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }






@require_torch
@require_vision
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    PoolFormerImageProcessor if is_vision_available() else None


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  Dict					=       PoolFormerImageProcessingTester(self     )


   @property
   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  int					=       self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_resize_and_center_crop'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''size'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''crop_pct'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_normalize'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''image_mean'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''image_std'''     )     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Dict					=       self.image_processing_class.from_dict(self.image_processor_dict     )
      self.assertEqual(image_processor.size			,			{'''shortest_edge''': 3_0}     )
      self.assertEqual(image_processor.crop_size			,			{'''height''': 3_0, '''width''': 3_0}     )

      _A:  str					=       self.image_processing_class.from_dict(self.image_processor_dict			,			size=4_2			,			crop_size=8_4     )
      self.assertEqual(image_processor.size			,			{'''shortest_edge''': 4_2}     )
      self.assertEqual(image_processor.crop_size			,			{'''height''': 8_4, '''width''': 8_4}     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      pass


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  Optional[Any]					=       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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)

      # Test batched
      _A:  Union[str, 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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  int					=       self.image_processing_class(**self.image_processor_dict     )
      # create random numpy tensors
      _A:  List[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:  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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)

      # Test batched
      _A:  List[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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  Optional[int]					=       self.image_processing_class(**self.image_processor_dict     )
      # create random PyTorch tensors
      _A:  Union[str, 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:  str					=       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
      _A:  str					=       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'''],
          )			,			)





from typing import TYPE_CHECKING

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


UpperCAmelCase__		:     Any     			=      {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Union[str, Any]     			=      [
       'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SwinForImageClassification',
       'SwinForMaskedImageModeling',
       'SwinModel',
       'SwinPreTrainedModel',
       'SwinBackbone',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     str     			=      [
       'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFSwinForImageClassification',
       'TFSwinForMaskedImageModeling',
       'TFSwinModel',
       'TFSwinPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase__		:     Tuple     			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



