




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__a							     =					logging.get_logger(__name__)

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = ['''pixel_values''']





    def __init__( self  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 1 / 255  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						None:
         super().__init__(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     size if size is not None else {'''height''': 256, '''width''': 256}
         UpperCAmelCase_     :				Tuple  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_     :				Dict  =     get_size_dict(_SCREAMING_SNAKE_CASE  ,param_name='''crop_size'''    )

         UpperCAmelCase_     :				Optional[int]  =     do_resize
         UpperCAmelCase_     :				Dict  =     size
         UpperCAmelCase_     :				Tuple  =     resample
         UpperCAmelCase_     :				Dict  =     do_center_crop
         UpperCAmelCase_     :				Optional[int]  =     crop_size
         UpperCAmelCase_     :				Union[str, Any]  =     do_rescale
         UpperCAmelCase_     :				Any  =     rescale_factor
         UpperCAmelCase_     :				List[Any]  =     do_normalize
         UpperCAmelCase_     :				List[str]  =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_     :				Optional[int]  =     image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						np.ndarray:
         UpperCAmelCase_     :				Optional[int]  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
         return resize(
             _SCREAMING_SNAKE_CASE  ,size=(size['''height'''], size['''width'''])  ,resample=_SCREAMING_SNAKE_CASE  ,data_format=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						np.ndarray:
         UpperCAmelCase_     :				int  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
         return center_crop(_SCREAMING_SNAKE_CASE  ,size=(size['''height'''], size['''width'''])  ,data_format=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						List[str]:
         return rescale(_SCREAMING_SNAKE_CASE  ,scale=_SCREAMING_SNAKE_CASE  ,data_format=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						np.ndarray:
         return normalize(_SCREAMING_SNAKE_CASE  ,mean=_SCREAMING_SNAKE_CASE  ,std=_SCREAMING_SNAKE_CASE  ,data_format=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST  ,**_SCREAMING_SNAKE_CASE  ,)					->						PIL.Image.Image:
         UpperCAmelCase_     :				Any  =     do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_     :				List[Any]  =     resample if resample is not None else self.resample
         UpperCAmelCase_     :				str  =     do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_     :				Any  =     do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_     :				str  =     rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_     :				Tuple  =     do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_     :				List[Any]  =     image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_     :				List[str]  =     image_std if image_std is not None else self.image_std

         UpperCAmelCase_     :				List[str]  =     size if size is not None else self.size
         UpperCAmelCase_     :				int  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_     :				List[str]  =     get_size_dict(_SCREAMING_SNAKE_CASE  ,param_name='''crop_size'''    )

         UpperCAmelCase_     :				Tuple  =     make_list_of_images(_SCREAMING_SNAKE_CASE    )

         if not valid_images(_SCREAMING_SNAKE_CASE    ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

         if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

         if do_center_crop and crop_size is None:
              raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

         if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

         # All transformations expect numpy arrays.
         UpperCAmelCase_     :				Optional[int]  =     [to_numpy_array(_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_resize:
              UpperCAmelCase_     :				int  =     [self.resize(image=_SCREAMING_SNAKE_CASE  ,size=_SCREAMING_SNAKE_CASE  ,resample=_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_center_crop:
              UpperCAmelCase_     :				int  =     [self.center_crop(image=_SCREAMING_SNAKE_CASE  ,size=_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_rescale:
              UpperCAmelCase_     :				Optional[int]  =     [self.rescale(image=_SCREAMING_SNAKE_CASE  ,scale=_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_normalize:
              UpperCAmelCase_     :				Dict  =     [self.normalize(image=_SCREAMING_SNAKE_CASE  ,mean=_SCREAMING_SNAKE_CASE  ,std=_SCREAMING_SNAKE_CASE    ) for image in images]

         UpperCAmelCase_     :				List[Any]  =     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) for image in images]

         UpperCAmelCase_     :				str  =     {'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE  ,tensor_type=_SCREAMING_SNAKE_CASE    )


"""simple docstring"""




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    List[Any]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]=1_3     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    str=2     ,					__UpperCAmelCase					:    List[Any]=3     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    Optional[Any]=3_2     ,					__UpperCAmelCase					:    Any=5     ,					__UpperCAmelCase					:    Optional[Any]=4     ,					__UpperCAmelCase					:    Tuple=3_7     ,					__UpperCAmelCase					:    List[str]="gelu"     ,					__UpperCAmelCase					:    Union[str, Any]=0.1     ,					__UpperCAmelCase					:    List[str]=0.1     ,					__UpperCAmelCase					:    int=1_0     ,					__UpperCAmelCase					:    Any=0.02     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   patch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   initializer_range

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__										=   (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__										=   num_patches + 1

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						SCREAMING_SNAKE_CASE__										=   ViTConfig(
						    image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					hidden_size=self.hidden_size     ,					num_hidden_layers=self.num_hidden_layers     ,					num_attention_heads=self.num_attention_heads     ,					intermediate_size=self.intermediate_size     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					is_decoder=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					)

						return config, pixel_values

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   FlaxViTModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__										=   (self.image_size, self.image_size)
						SCREAMING_SNAKE_CASE__										=   (self.patch_size, self.patch_size)
						SCREAMING_SNAKE_CASE__										=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, num_patches + 1, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[Any]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   FlaxViTForImageClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__										=   FlaxViTForImageClassification(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_flax
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     None:
						SCREAMING_SNAKE_CASE__										=   FlaxViTModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=3_7							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Dict:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     str:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   inspect.signature(model.__call__							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE__										=   [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE__										=   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												SCREAMING_SNAKE_CASE__										=   self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

												@jax.jit
												def model_jitted(__UpperCAmelCase					:    int     ,					**__UpperCAmelCase					:    Tuple							):
															return model(pixel_values=__UpperCAmelCase     ,					**__UpperCAmelCase							)

												with self.subTest("""JIT Enabled"""							):
															SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												with self.subTest("""JIT Disabled"""							):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												self.assertEqual(len(__UpperCAmelCase							)     ,					len(__UpperCAmelCase							)							)
												for jitted_output, output in zip(__UpperCAmelCase     ,					__UpperCAmelCase							):
															self.assertEqual(jitted_output.shape     ,					output.shape							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Dict:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class_name.from_pretrained("""google/vit-base-patch16-224"""							)
									SCREAMING_SNAKE_CASE__										=   model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
									self.assertIsNotNone(__UpperCAmelCase							)


