
'''simple docstring'''


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 ViTImageProcessor




class     __snake_case			(    unittest.TestCase	):




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Any			,    _SCREAMING_SNAKE_CASE					:Optional[int]=7			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=3			,    _SCREAMING_SNAKE_CASE					:int=1_8			,    _SCREAMING_SNAKE_CASE					:List[Any]=3_0			,    _SCREAMING_SNAKE_CASE					:Any=4_0_0			,    _SCREAMING_SNAKE_CASE					:List[Any]=True			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=True			,    _SCREAMING_SNAKE_CASE					:List[Any]=[0.5, 0.5, 0.5]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=[0.5, 0.5, 0.5]			,    )					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     size if size is not None else {"""height""": 1_8, """width""": 1_8}
								UpperCamelCase_     :	Dict					     =     parent
								UpperCamelCase_     :	Dict					     =     batch_size
								UpperCamelCase_     :	List[str]					     =     num_channels
								UpperCamelCase_     :	str					     =     image_size
								UpperCamelCase_     :	int					     =     min_resolution
								UpperCamelCase_     :	List[str]					     =     max_resolution
								UpperCamelCase_     :	Optional[Any]					     =     do_resize
								UpperCamelCase_     :	List[Any]					     =     size
								UpperCamelCase_     :	Any					     =     do_normalize
								UpperCamelCase_     :	List[str]					     =     image_mean
								UpperCamelCase_     :	Dict					     =     image_std





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






								'''simple docstring'''






								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}







@require_torch
@require_vision
class     __snake_case			(    _SCREAMING_SNAKE_CASE					,			unittest.TestCase	):
		a_          =					ViTImageProcessor if is_vision_available() else None




		def 	_snake_case	(  self					:int)					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     ViTImageProcessingTester(self)




		@property
		def 	_snake_case	(  self					:Dict)					->			Dict:






								'''simple docstring'''






								return self.image_processor_tester.prepare_image_processor_dict()




		def 	_snake_case	(  self					:str)					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,    """image_mean"""))
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,    """image_std"""))
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,    """do_normalize"""))
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,    """do_resize"""))
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,    """size"""))




		def 	_snake_case	(  self					:str)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size			,    {"""height""": 1_8, """width""": 1_8})

								UpperCamelCase_     :	str					     =     self.image_processing_class.from_dict(self.image_processor_dict			,    size=4_2)
								self.assertEqual(image_processor.size			,    {"""height""": 4_2, """width""": 4_2})




		def 	_snake_case	(  self					:Any)					->			List[Any]:






								'''simple docstring'''






								pass




		def 	_snake_case	(  self					:List[str])					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCamelCase_     :	List[str]					     =     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
								UpperCamelCase_     :	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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,    )

								# Test batched
								UpperCamelCase_     :	Tuple					     =     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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,    )




		def 	_snake_case	(  self					:Tuple)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCamelCase_     :	Any					     =     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
								UpperCamelCase_     :	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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,    )

								# Test batched
								UpperCamelCase_     :	Any					     =     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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,    )





		def 	_snake_case	(  self					:Union[str, Any])					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCamelCase_     :	Optional[int]					     =     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
								UpperCamelCase_     :	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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,    )

								# Test batched
								UpperCamelCase_     :	Union[str, Any]					     =     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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,    )








'''simple docstring'''






from typing import TYPE_CHECKING

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


a_       :     Any 							=     {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     int 							=     [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     Tuple 							=     ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 a_       :     Union[str, Any] 							=     _LazyModule(__name__, globals()["__file__"], _import_structure)

