






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 LevitImageProcessor

class 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=1_8 ,						_lowerCAmelCase=3_0 ,						_lowerCAmelCase=4_0_0 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						_lowerCAmelCase=[0.5, 0.5, 0.5] ,						_lowerCAmelCase=[0.5, 0.5, 0.5] ,						):
											_lowercase							:   Optional[Any] 							=   size if size is not None else {'shortest_edge': 1_8}
											_lowercase							:   str 							=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
											_lowercase							:   int 							=   parent
											_lowercase							:   List[str] 							=   batch_size
											_lowercase							:   List[Any] 							=   num_channels
											_lowercase							:   Any 							=   image_size
											_lowercase							:   List[Any] 							=   min_resolution
											_lowercase							:   List[Any] 							=   max_resolution
											_lowercase							:   Dict 							=   do_resize
											_lowercase							:   Optional[int] 							=   size
											_lowercase							:   int 							=   do_center_crop
											_lowercase							:   Optional[Any] 							=   crop_size
											_lowercase							:   Any 							=   do_normalize
											_lowercase							:   Union[str, Any] 							=   image_mean
											_lowercase							:   List[str] 							=   image_std


				def 						__a					(					self      ):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "do_center_crop": self.do_center_crop,
											    "size": self.size,
											    "crop_size": self.crop_size,
											}




@require_torch
@require_vision
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Tuple							       = LevitImageProcessor if is_vision_available() else None


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   LevitImageProcessingTester(self      )


				@property
				def 						__a					(					self      ):
											return self.image_processor_tester.prepare_image_processor_dict()


				def 						__a					(					self      ):
											_lowercase							:   Optional[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 ,						'do_center_crop'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'size'      )      )


				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   self.image_processing_class.from_dict(self.image_processor_dict      )
											self.assertEqual(image_processor.size ,						{'shortest_edge': 1_8}      )
											self.assertEqual(image_processor.crop_size ,						{'height': 1_8, 'width': 1_8}      )

											_lowercase							:   Optional[int] 							=   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 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   List[str] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PIL images
											_lowercase							:   str 							=   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_lowerCAmelCase      )
											for image in image_inputs:
																		self.assertIsInstance(_lowerCAmelCase ,						Image.Image      )

											# Test not batched input
											_lowercase							:   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
											_lowercase							:   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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   List[str] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random numpy tensors
											_lowercase							:   Tuple 							=   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
											_lowercase							:   Optional[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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)

											# Test batched
											_lowercase							:   Optional[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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   Optional[Any] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PyTorch tensors
											_lowercase							:   List[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
											_lowercase							:   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
											_lowercase							:   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'],
											    ) ,						)











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
