



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 ViltImageProcessor

class 			__snake_case   ( unittest.TestCase):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=7	,	a_=3	,	a_=18	,	a_=30	,	a_=400	,	a_=True	,	a_=None	,	a_=2	,	a_=True	,	a_=[0.5, 0.5, 0.5]	,	a_=[0.5, 0.5, 0.5]	,	):
											a__					   =		size if size is not None else {"""shortest_edge""": 30}
											a__					   =		parent
											a__					   =		batch_size
											a__					   =		num_channels
											a__					   =		image_size
											a__					   =		min_resolution
											a__					   =		max_resolution
											a__					   =		do_resize
											a__					   =		size
											a__					   =		size_divisor
											a__					   =		do_normalize
											a__					   =		image_mean
											a__					   =		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,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}







							def _a   (				self	,	a_	,	a_=False		):
											if not batched:
															a__					   =		self.size["""shortest_edge"""]
															a__					   =		image_inputs[0]
															if isinstance(a_	,	Image.Image		):
																			a__   ,      a__					   =		image.size
															else:
																			a__   ,      a__					   =		image.shape[1], image.shape[2]
															a__					   =		size / min(a_	,	a_		)
															if h < w:
																			a__   ,      a__					   =		size, scale * w
															else:
																			a__   ,      a__					   =		scale * h, size

															a__					   =		int((1_333 / 800) * size		)
															if max(a_	,	a_		) > max_size:
																			a__					   =		max_size / max(a_	,	a_		)
																			a__					   =		newh * scale
																			a__					   =		neww * scale

															a__   ,      a__					   =		int(newh + 0.5		), int(neww + 0.5		)
															a__   ,      a__					   =		(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

											else:
															a__					   =		[]
															for image in image_inputs:
																			a__   ,      a__					   =		self.get_expected_values([image]		)
																			expected_values.append((expected_height, expected_width)		)
															a__					   =		max(a_	,	key=lambda a_		: item[0]		)[0]
															a__					   =		max(a_	,	key=lambda a_		: item[1]		)[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[str]       =       ViltImageProcessor if is_vision_available() else None


							def _a   (				self		):
											a__					   =		ViltImageProcessingTester(self		)


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


							def _a   (				self		):
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(a_	,	"""image_mean"""		)		)
											self.assertTrue(hasattr(a_	,	"""image_std"""		)		)
											self.assertTrue(hasattr(a_	,	"""do_normalize"""		)		)
											self.assertTrue(hasattr(a_	,	"""do_resize"""		)		)
											self.assertTrue(hasattr(a_	,	"""size"""		)		)
											self.assertTrue(hasattr(a_	,	"""size_divisor"""		)		)


							def _a   (				self		):
											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 30}		)

											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict	,	size=42		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}		)


							def _a   (				self		):
											pass


							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											a__					   =		prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_	,	Image.Image		)

											# Test not batched input
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)


							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											a__					   =		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
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)







							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											a__					   =		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
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)









import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class 							__snake_case(       enum.Enum   ):
 lowercase			:							List[str]		   =		'''timm'''
 lowercase			:							Any		   =		'''transformers'''
def    a__    (	A		,					A		,					A       ):
   if stage_names is None:
      raise ValueError("""Stage_names must be set for transformers backbones"""       )

   if out_features is not None:
      if not isinstance(A		,					(list,)       ):
         raise ValueError(F'''out_features must be a list {type(A       )}'''       )
      if any(feat not in stage_names for feat in out_features       ):
         raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''       )

   if out_indices is not None:
      if not isinstance(A		,					(list, tuple)       ):
         raise ValueError(F'''out_indices must be a list or tuple, got {type(A       )}'''       )
      if any(idx >= len(A       ) for idx in out_indices       ):
         raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""       )

   if out_features is not None and out_indices is not None:
      if len(A       ) != len(A       ):
         raise ValueError("""out_features and out_indices should have the same length if both are set"""       )
      if out_features != [stage_names[idx] for idx in out_indices]:
         raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""       )
def    a__    (	A		,					A		,					A		,					):
   if out_indices is None and out_features is None:
      lowercase__    :						int       =			[len(A       ) - 1]
      lowercase__    :						Dict       =			[stage_names[-1]]
   elif out_indices is None and out_features is not None:
      lowercase__    :						Any       =			[stage_names.index(A       ) for layer in out_features]
   elif out_features is None and out_indices is not None:
      lowercase__    :						List[str]       =			[stage_names[idx] for idx in out_indices]
   return out_features, out_indices
def    a__    (	A		,					A		,					A		,					):
   verify_out_features_out_indices(out_features=A		,					out_indices=A		,					stage_names=A       )
   lowercase__  ,       lowercase__    :						Dict       =			_align_output_features_output_indices(
       out_features=A		,					out_indices=A		,					stage_names=A       )
   # Verify that the aligned out_features and out_indices are valid
   verify_out_features_out_indices(out_features=A		,					out_indices=A		,					stage_names=A       )
   return output_features, output_indices
class 							__snake_case:
 lowercase			:							Optional[BackboneType]		   =		None



 def     UpperCAmelCase     (						self : Dict			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if getattr(self			,		"""_backbone"""			,		UpperCAmelCase__						) is None:
       raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""						)

    # These will diagree with the defaults for the transformers models e.g. for resnet50
    # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
    # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
    lowercase__    :						List[str]       =			[stage["""module"""] for stage in self._backbone.feature_info.info]
    lowercase__    :						int       =			[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
    lowercase__    :						Optional[int]       =			self._backbone.feature_info.out_indices
    lowercase__    :						Optional[Any]       =			self._backbone.feature_info.module_name()

    # We verify the out indices and out features are valid
    verify_out_features_out_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)
    lowercase__  ,       lowercase__    :						int       =			out_features, out_indices



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : str						):

    """simple docstring"""






    lowercase__    :						List[str]       =			getattr(UpperCAmelCase__			,		"""stage_names"""						)
    lowercase__    :						Union[str, Any]       =			getattr(UpperCAmelCase__			,		"""out_features"""			,		UpperCAmelCase__						)
    lowercase__    :						List[str]       =			getattr(UpperCAmelCase__			,		"""out_indices"""			,		UpperCAmelCase__						)

    lowercase__    :						Tuple       =			stage_names
    lowercase__  ,       lowercase__    :						List[str]       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=UpperCAmelCase__						)
    # Number of channels for each stage. This is set in the transformer backbone model init
    lowercase__    :						Optional[int]       =			None



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			config

    lowercase__    :						Optional[Any]       =			getattr(UpperCAmelCase__			,		"""use_timm_backbone"""			,		UpperCAmelCase__						)
    lowercase__    :						Optional[int]       =			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

    if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(UpperCAmelCase__						)
    elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(UpperCAmelCase__						)
    else:
       raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''						)



 @property
 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    return self._out_features



 @out_features.setter
 def     UpperCAmelCase     (						self : Dict			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Optional[Any]       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)



 @property
 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    return self._out_indices



 @out_indices.setter
 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : Union[Tuple[int], List[int]]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Any       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)



 @property
 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}



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

    """simple docstring"""






    return [self.out_feature_channels[name] for name in self.out_features]



 def     UpperCAmelCase     (						self : Optional[Any]			,		*UpperCAmelCase__ : Tuple			,		**UpperCAmelCase__ : Dict						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			dict(inspect.signature(self.forward						).parameters						)
    lowercase__    :						Tuple       =			{k: v for k, v in kwargs.items() if k in signature}
    return self(*UpperCAmelCase__			,		**UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : int			,		UpperCAmelCase__ : List[str]			,		UpperCAmelCase__ : Optional[bool] = None			,		UpperCAmelCase__ : Optional[bool] = None			,		UpperCAmelCase__ : Optional[bool] = None			,		):

    """simple docstring"""






    raise NotImplementedError("""This method should be implemented by the derived class."""						)







 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			super().to_dict()
    lowercase__    :						Tuple       =			output.pop("""_out_features"""						)
    lowercase__    :						Union[str, Any]       =			output.pop("""_out_indices"""						)
    return output
class 							__snake_case:



 @property
 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    return self._out_features



 @out_features.setter
 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Dict       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)



 @property
 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    return self._out_indices



 @out_indices.setter
 def     UpperCAmelCase     (						self : int			,		UpperCAmelCase__ : Union[Tuple[int], List[int]]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Any       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)







 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Dict       =			super().to_dict()
    lowercase__    :						Union[str, Any]       =			output.pop("""_out_features"""						)
    lowercase__    :						Dict       =			output.pop("""_out_indices"""						)
    return output


