

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 MobileNetVaImageProcessor



class     lowercase_       (    unittest.TestCase	):



      def __init__(     self :							str     ,   A__ :							Optional[int]     ,   A__ :							str=7     ,   A__ :							Optional[Any]=3     ,   A__ :							int=18     ,   A__ :							Tuple=30     ,   A__ :							Optional[Any]=400     ,   A__ :							str=True     ,   A__ :							List[str]=None     ,   A__ :							Union[str, Any]=True     ,   A__ :							int=None     ,   )       ->	Tuple:
        _snake_case		=				size if size is not None else {"shortest_edge": 20}
        _snake_case		=				crop_size if crop_size is not None else {"height": 18, "width": 18}
        _snake_case		=				parent
        _snake_case		=				batch_size
        _snake_case		=				num_channels
        _snake_case		=				image_size
        _snake_case		=				min_resolution
        _snake_case		=				max_resolution
        _snake_case		=				do_resize
        _snake_case		=				size
        _snake_case		=				do_center_crop
        _snake_case		=				crop_size







      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class     lowercase_       (    snake_case__  ,				unittest.TestCase	):
      UpperCamelCase_     :						Optional[int]		 =							MobileNetVaImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Union[str, Any]:
        _snake_case		=				MobileNetVaImageProcessingTester(self						)



      @property
      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Union[str, Any]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 		UpperCamelCase_						(     self :							int						)       ->	Union[str, Any]:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,   '''do_resize'''						)						)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,   '''size'''						)						)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,   '''do_center_crop'''						)						)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,   '''center_crop'''						)						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Any:
        _snake_case		=				self.image_processing_class.from_dict(self.image_processor_dict						)
        self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 20}						)
        self.assertEqual(image_processor.crop_size     ,   {'''height''': 18, '''width''': 18}						)

        _snake_case		=				self.image_processing_class.from_dict(self.image_processor_dict     ,   size=42     ,   crop_size=84						)
        self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 42}						)
        self.assertEqual(image_processor.crop_size     ,   {'''height''': 84, '''width''': 84}						)



      def 		UpperCamelCase_						(     self :							Any						)       ->	Union[str, Any]:
        pass



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Optional[Any]:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _snake_case		=				prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__SCREAMING_SNAKE_CASE						)
        for image in image_inputs:
          self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,   Image.Image						)

        # Test not batched input
        _snake_case		=				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
        _snake_case		=				image_processing(__SCREAMING_SNAKE_CASE     ,   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 		UpperCamelCase_						(     self :							str						)       ->	Union[str, Any]:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _snake_case		=				prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__SCREAMING_SNAKE_CASE     ,   numpify=__SCREAMING_SNAKE_CASE						)
        for image in image_inputs:
          self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,   np.ndarray						)

        # Test not batched input
        _snake_case		=				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
        _snake_case		=				image_processing(__SCREAMING_SNAKE_CASE     ,   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 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Any:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _snake_case		=				prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__SCREAMING_SNAKE_CASE     ,   torchify=__SCREAMING_SNAKE_CASE						)
        for image in image_inputs:
          self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,   torch.Tensor						)

        # Test not batched input
        _snake_case		=				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
        _snake_case		=				image_processing(__SCREAMING_SNAKE_CASE     ,   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_torch_available


__UpperCAmelCase      			=  {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SEWDForCTC""",
         """SEWDForSequenceClassification""",
         """SEWDModel""",
         """SEWDPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sew_d import (
              SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
              SEWDForCTC,
              SEWDForSequenceClassification,
              SEWDModel,
              SEWDPreTrainedModel,
          )


else:
     import sys

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

