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     _snake_case  (  unittest.TestCase ):
					def __init__(     self: Union[str, Any]		,     __lowerCamelCase: Any		,     __lowerCamelCase: List[str]=13		,     __lowerCamelCase: Optional[int]=30		,     __lowerCamelCase: Optional[int]=2		,     __lowerCamelCase: int=3		,     __lowerCamelCase: int=True		,     __lowerCamelCase: Optional[int]=True		,     __lowerCamelCase: List[Any]=32		,     __lowerCamelCase: List[Any]=5		,     __lowerCamelCase: str=4		,     __lowerCamelCase: Optional[Any]=37		,     __lowerCamelCase: Tuple="gelu"		,     __lowerCamelCase: Dict=0.1		,     __lowerCamelCase: List[Any]=0.1		,     __lowerCamelCase: List[Any]=10		,     __lowerCamelCase: Any=0.02		,     )			-> Any:
												__UpperCAmelCase  :	str   			=      parent
												__UpperCAmelCase  :	Optional[Any]   			=      batch_size
												__UpperCAmelCase  :	List[str]   			=      image_size
												__UpperCAmelCase  :	str   			=      patch_size
												__UpperCAmelCase  :	Union[str, Any]   			=      num_channels
												__UpperCAmelCase  :	int   			=      is_training
												__UpperCAmelCase  :	Optional[Any]   			=      use_labels
												__UpperCAmelCase  :	List[str]   			=      hidden_size
												__UpperCAmelCase  :	Tuple   			=      num_hidden_layers
												__UpperCAmelCase  :	List[Any]   			=      num_attention_heads
												__UpperCAmelCase  :	Union[str, Any]   			=      intermediate_size
												__UpperCAmelCase  :	str   			=      hidden_act
												__UpperCAmelCase  :	Optional[Any]   			=      hidden_dropout_prob
												__UpperCAmelCase  :	Optional[int]   			=      attention_probs_dropout_prob
												__UpperCAmelCase  :	Dict   			=      type_sequence_label_size
												__UpperCAmelCase  :	Union[str, Any]   			=      initializer_range

												# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												__UpperCAmelCase  :	Dict   			=      (image_size // patch_size) ** 2
												__UpperCAmelCase  :	Any   			=      num_patches + 1
					def       _lowerCamelCase       (     self: Dict   )			-> List[Any]:
												__UpperCAmelCase  :	List[Any]   			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

												__UpperCAmelCase  :	List[str]   			=      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=__lowerCamelCase		,     initializer_range=self.initializer_range		,     )

												return config, pixel_values
					def       _lowerCamelCase       (     self: Tuple		,     __lowerCamelCase: int		,     __lowerCamelCase: str   )			-> List[str]:
												__UpperCAmelCase  :	Tuple   			=      FlaxViTModel(config=__lowerCamelCase   )
												__UpperCAmelCase  :	str   			=      model(__lowerCamelCase   )
												# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
												__UpperCAmelCase  :	Optional[int]   			=      (self.image_size, self.image_size)
												__UpperCAmelCase  :	Optional[int]   			=      (self.patch_size, self.patch_size)
												__UpperCAmelCase  :	List[Any]   			=      (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       _lowerCamelCase       (     self: Any		,     __lowerCamelCase: Dict		,     __lowerCamelCase: List[str]   )			-> int:
												__UpperCAmelCase  :	Union[str, Any]   			=      self.type_sequence_label_size
												__UpperCAmelCase  :	int   			=      FlaxViTForImageClassification(config=__lowerCamelCase   )
												__UpperCAmelCase  :	str   			=      model(__lowerCamelCase   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)   )

												# test greyscale images
												__UpperCAmelCase  :	Optional[int]   			=      1
												__UpperCAmelCase  :	Union[str, Any]   			=      FlaxViTForImageClassification(__lowerCamelCase   )

												__UpperCAmelCase  :	Dict   			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
												__UpperCAmelCase  :	Union[str, Any]   			=      model(__lowerCamelCase   )





					def       _lowerCamelCase       (     self: int   )			-> Dict:
												__UpperCAmelCase  :	Optional[Any]   			=      self.prepare_config_and_inputs()
												(
												    (
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,							
												)  :	str   			=      config_and_inputs
												__UpperCAmelCase  :	Optional[int]   			=      {"pixel_values": pixel_values}
												return config, inputs_dict




@require_flax
class     _snake_case  (  __lowerCamelCase    ,					unittest.TestCase ):
					lowerCamelCase__:						Dict     =   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
					def       _lowerCamelCase       (     self: Any   )			-> int:
												__UpperCAmelCase  :	Dict   			=      FlaxViTModelTester(self   )
												__UpperCAmelCase  :	Optional[Any]   			=      ConfigTester(self		,     config_class=__lowerCamelCase		,     has_text_modality=__lowerCamelCase		,     hidden_size=37   )
					def       _lowerCamelCase       (     self: Tuple   )			-> Optional[Any]:
												self.config_tester.run_common_tests()
					def       _lowerCamelCase       (     self: List[Any]   )			-> Optional[Any]:
												__UpperCAmelCase  :	Optional[int]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__lowerCamelCase   )
					def       _lowerCamelCase       (     self: int   )			-> List[Any]:
												__UpperCAmelCase  :	int   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )
					def       _lowerCamelCase       (     self: Optional[int]   )			-> Optional[Any]:
												__UpperCAmelCase			,		__UpperCAmelCase  :	str   			=      self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCAmelCase  :	Dict   			=      ["pixel_values"]
																			self.assertListEqual(arg_names[:1]		,     __lowerCamelCase   )
					def       _lowerCamelCase       (     self: Optional[Any]   )			-> Tuple:
												__UpperCAmelCase			,		__UpperCAmelCase  :	str   			=      self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__   ):
																										__UpperCAmelCase  :	Dict   			=      self._prepare_for_class(__lowerCamelCase		,     __lowerCamelCase   )
																										__UpperCAmelCase  :	Optional[Any]   			=      model_class(__lowerCamelCase   )

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

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

																										with self.subTest("JIT Disabled"   ):
																																	with jax.disable_jit():
																																								__UpperCAmelCase  :	List[str]   			=      model_jitted(**__lowerCamelCase   ).to_tuple()

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





					@slow
					def       _lowerCamelCase       (     self: List[Any]   )			-> Any:
												for model_class_name in self.all_model_classes:
																			__UpperCAmelCase  :	Any   			=      model_class_name.from_pretrained("google/vit-base-patch16-224"   )
																			__UpperCAmelCase  :	List[str]   			=      model(np.ones((1, 3, 2_24, 2_24)   )   )
																			self.assertIsNotNone(__lowerCamelCase   )






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase	      = logging.get_logger(__name__)


if is_vision_available():
	import PIL







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = True		,    snake_case = None		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'shortest_edge': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		lowercase       =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case		,    param_name='crop_size'       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_center_crop
		lowercase       =    crop_size
		lowercase       =    do_rescale
		lowercase       =    rescale_factor
		lowercase       =    do_normalize
		lowercase       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
		lowercase       =    image_std if image_std is not None else OPENAI_CLIP_STD
		lowercase       =    do_convert_rgb





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		if "shortest_edge" not in size:
			raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
		lowercase       =    get_resize_output_image_size(snake_case		,    size=size['shortest_edge']		,    default_to_square=snake_case       )
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
		return center_crop(snake_case		,    size=(size['height'], size['width'])		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case		,    param_name='size'		,    default_to_square=snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_center_crop if do_center_crop is not None else self.do_center_crop
		lowercase       =    crop_size if crop_size is not None else self.crop_size
		lowercase       =    get_size_dict(snake_case		,    param_name='crop_size'		,    default_to_square=snake_case       )
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(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:
			raise ValueError('Size 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.'       )

		# PIL RGBA images are converted to RGB
		if do_convert_rgb:
			lowercase       =    [convert_to_rgb(snake_case       ) for image in images]

		# All transformations expect numpy arrays.
		lowercase       =    [to_numpy_array(snake_case       ) for image in images]

		if do_resize:
			lowercase       =    [self.resize(image=snake_case		,    size=snake_case		,    resample=snake_case       ) for image in images]

		if do_center_crop:
			lowercase       =    [self.center_crop(image=snake_case		,    size=snake_case       ) for image in images]

		if do_rescale:
			lowercase       =    [self.rescale(image=snake_case		,    scale=snake_case       ) for image in images]

		if do_normalize:
			lowercase       =    [self.normalize(image=snake_case		,    mean=snake_case		,    std=snake_case       ) for image in images]

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    {'pixel_values': images}
		return BatchFeature(data=snake_case		,    tensor_type=snake_case       )

