
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        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=13		,    snake_case=30		,    snake_case=2		,    snake_case=3		,    snake_case=True		,    snake_case=True		,    snake_case=32		,    snake_case=5		,    snake_case=4		,    snake_case=37		,    snake_case="gelu"		,    snake_case=0.1		,    snake_case=0.1		,    snake_case=10		,    snake_case=0.02		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    image_size
		lowercase       =    patch_size
		lowercase       =    num_channels
		lowercase       =    is_training
		lowercase       =    use_labels
		lowercase       =    hidden_size
		lowercase       =    num_hidden_layers
		lowercase       =    num_attention_heads
		lowercase       =    intermediate_size
		lowercase       =    hidden_act
		lowercase       =    hidden_dropout_prob
		lowercase       =    attention_probs_dropout_prob
		lowercase       =    type_sequence_label_size
		lowercase       =    initializer_range

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		lowercase       =    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=snake_case		,    initializer_range=self.initializer_range		,    )

		return config, pixel_values





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    FlaxViTModel(config=snake_case       )
		lowercase       =    model(snake_case       )
		# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
		lowercase       =    (self.image_size, self.image_size)
		lowercase       =    (self.patch_size, self.patch_size)
		lowercase       =    (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		,    snake_case		,    snake_case       ):
		lowercase       =    self.type_sequence_label_size
		lowercase       =    FlaxViTForImageClassification(config=snake_case       )
		lowercase       =    model(snake_case       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)       )

		# test greyscale images
		lowercase       =    1
		lowercase       =    FlaxViTForImageClassification(snake_case       )

		lowercase       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		lowercase       =    model(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.prepare_config_and_inputs()
		(
		    (
		    lowercase
		)  ,					(
		    lowercase
		)  ,					
		)       =    config_and_inputs
		lowercase       =    {'pixel_values': pixel_values}
		return config, inputs_dict







@require_flax
class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    FlaxViTModelTester(self       )
		lowercase       =    ConfigTester(self		,    config_class=snake_case		,    has_text_modality=snake_case		,    hidden_size=37       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.config_tester.run_common_tests()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

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

			lowercase       =    ['pixel_values']
			self.assertListEqual(arg_names[:1]		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__       ):
				lowercase       =    self._prepare_for_class(snake_case		,    snake_case       )
				lowercase       =    model_class(snake_case       )

				@jax.jit
				def model_jitted(snake_case		,    **snake_case       ):
					return model(pixel_values=snake_case		,    **snake_case       )

				with self.subTest('JIT Enabled'       ):
					lowercase       =    model_jitted(**snake_case       ).to_tuple()

				with self.subTest('JIT Disabled'       ):
					with jax.disable_jit():
						lowercase       =    model_jitted(**snake_case       ).to_tuple()

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





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model_class_name in self.all_model_classes:
			lowercase       =    model_class_name.from_pretrained('google/vit-base-patch16-224'       )
			lowercase       =    model(np.ones((1, 3, 224, 224)       )       )
			self.assertIsNotNone(snake_case       )



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       )

