


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a				:							Tuple					=			{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Dict					=			['SegformerFeatureExtractor']
  a				:							Any					=			['SegformerImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Dict					=			[
      'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SegformerDecodeHead',
      'SegformerForImageClassification',
      'SegformerForSemanticSegmentation',
      'SegformerLayer',
      'SegformerModel',
      'SegformerPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Optional[int]					=			[
      'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFSegformerDecodeHead',
      'TFSegformerForImageClassification',
      'TFSegformerForSemanticSegmentation',
      'TFSegformerModel',
      'TFSegformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

  a				:							List[str]					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










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      lowerCamelCase	(  unittest.TestCase     ):

       def __init__(			self	,						a__	,						a__=7	,						a__=3	,						a__=18	,						a__=30	,						a__=4_00	,						a__=True	,						a__=None	,						a__=True	,						a__=None	,						):






        '''simple docstring'''






        snake_case   : str	  =      size if size is not None else {'''shortest_edge''': 20}
        snake_case   : List[str]	  =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        snake_case   : Optional[Any]	  =      parent
        snake_case   : Dict	  =      batch_size
        snake_case   : List[str]	  =      num_channels
        snake_case   : Union[str, Any]	  =      image_size
        snake_case   : Optional[Any]	  =      min_resolution
        snake_case   : Union[str, Any]	  =      max_resolution
        snake_case   : Optional[int]	  =      do_resize
        snake_case   : Optional[int]	  =      size
        snake_case   : Optional[Any]	  =      do_center_crop
        snake_case   : Optional[int]	  =      crop_size



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        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      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      MobileNetVaImageProcessor if is_vision_available() else None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      MobileNetVaImageProcessingTester(self					)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(a__	,						'''do_resize'''					)					)
        self.assertTrue(hasattr(a__	,						'''size'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_center_crop'''					)					)
        self.assertTrue(hasattr(a__	,						'''center_crop'''					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      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   : Dict	  =      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					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						Image.Image					)

        # Test not batched input
        snake_case   : List[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
        snake_case   : List[Any]	  =      image_processing(a__	,						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					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						numpify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						np.ndarray					)

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

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						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					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        snake_case   : List[Any]	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						torchify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						torch.Tensor					)

        # Test not batched input
        snake_case   : List[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
        snake_case   : Any	  =      image_processing(a__	,						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'''],
            )	,						)






