

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    LevitForImageClassification,
			    LevitForImageClassificationWithTeacher,
			    LevitModel,
			)
			from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			List[str]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.config_class(**self.inputs_dict						)
				self.parent.assertTrue(hasattr(a   ,   "hidden_sizes"						)						)
				self.parent.assertTrue(hasattr(a   ,   "num_attention_heads"						)						)






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			List[str]   ,   a				:			List[str]=13   ,   a				:			Union[str, Any]=64   ,   a				:			List[Any]=3   ,   a				:			Dict=3   ,   a				:			int=2   ,   a				:			str=1   ,   a				:			str=16   ,   a				:			Union[str, Any]=[128, 256, 384]   ,   a				:			int=[4, 6, 8]   ,   a				:			List[Any]=[2, 3, 4]   ,   a				:			Dict=[16, 16, 16]   ,   a				:			Union[str, Any]=0   ,   a				:			Any=[2, 2, 2]   ,   a				:			Tuple=[2, 2, 2]   ,   a				:			Optional[Any]=0.02   ,   a				:			Optional[int]=True   ,   a				:			List[str]=True   ,   a				:			Dict=2   ,   )				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							parent
				SCREAMING_SNAKE_CASE :   List[Any]					  =							batch_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							image_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_channels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							kernel_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							stride
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							padding
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hidden_sizes
				SCREAMING_SNAKE_CASE :   Dict					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   int					  =							depths
				SCREAMING_SNAKE_CASE :   Dict					  =							key_dim
				SCREAMING_SNAKE_CASE :   Tuple					  =							drop_path_rate
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							patch_size
				SCREAMING_SNAKE_CASE :   int					  =							attention_ratio
				SCREAMING_SNAKE_CASE :   int					  =							mlp_ratio
				SCREAMING_SNAKE_CASE :   Any					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							is_training
				SCREAMING_SNAKE_CASE :   str					  =							use_labels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_labels
				SCREAMING_SNAKE_CASE :   Any					  =							initializer_range
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							None
				if self.use_labels:
					SCREAMING_SNAKE_CASE :   Tuple					  =							ids_tensor([self.batch_size]   ,   self.num_labels						)

				SCREAMING_SNAKE_CASE :   Any					  =							self.get_config()
				return config, pixel_values, labels
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							int:

				"""simple docstring"""

				return LevitConfig(
				    image_size=self.image_size   ,   num_channels=self.num_channels   ,   kernel_size=self.kernel_size   ,   stride=self.stride   ,   padding=self.padding   ,   patch_size=self.patch_size   ,   hidden_sizes=self.hidden_sizes   ,   num_attention_heads=self.num_attention_heads   ,   depths=self.depths   ,   key_dim=self.key_dim   ,   drop_path_rate=self.drop_path_rate   ,   mlp_ratio=self.mlp_ratio   ,   attention_ratio=self.attention_ratio   ,   initializer_range=self.initializer_range   ,   down_ops=self.down_ops   ,   )
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Any   ,   a				:			Tuple   ,   a				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							LevitModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   str					  =							model(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							(self.image_size, self.image_size)
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							image_size[0], image_size[1]
				for _ in range(4						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
				self.parent.assertEqual(
				    result.last_hidden_state.shape   ,   (self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])   ,   )
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			List[Any]   ,   a				:			Union[str, Any]   ,   a				:			Tuple						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							LevitForImageClassification(a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Dict					  =							model(a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)						)






			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__							     =(
			    {
			        'feature-extraction': LevitModel,
			        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LevitModelTester(self						)
				SCREAMING_SNAKE_CASE :   Any					  =							ConfigTester(self   ,   config_class=a   ,   has_text_modality=a   ,   hidden_size=37						)
			def 				__UpperCamelCase			(      self				:			int						)				->							Union[str, Any]:

				"""simple docstring"""

				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				return
			@unittest.skip(reason="Levit does not use inputs_embeds"						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Levit does not support input and output embeddings"						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Levit does not output attentions"						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Dict:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Any					  =							model_class(a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							inspect.signature(model.forward						)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE :   Any					  =							[*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE :   Optional[int]					  =							["pixel_values"]
					self.assertListEqual(arg_names[:1]   ,   a						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							int:

				"""simple docstring"""

				def check_hidden_states_output(a				:			Dict   ,   a				:			List[Any]   ,   a				:			str						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							model_class(a						)
					model.to(a						)
					model.eval()

					with torch.no_grad():
						SCREAMING_SNAKE_CASE :   Tuple					  =							model(**self._prepare_for_class(a   ,   a						)						)

					SCREAMING_SNAKE_CASE :   Tuple					  =							outputs.hidden_states

					SCREAMING_SNAKE_CASE :   Tuple					  =							len(self.model_tester.depths						) + 1
					self.assertEqual(len(a						)   ,   a						)

					SCREAMING_SNAKE_CASE :   List[Any]					  =							(self.model_tester.image_size, self.model_tester.image_size)
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							image_size[0], image_size[1]
					for _ in range(4						):
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							floor(
						    (
						        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
						        / self.model_tester.stride
						    )
						    + 1						)
						SCREAMING_SNAKE_CASE :   Any					  =							floor(
						    (
						        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
						        / self.model_tester.stride
						    )
						    + 1						)
					# verify the first hidden states (first block)
					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]						)   ,   [
					        height * width,
					        self.model_tester.hidden_sizes[0],
					    ]   ,   )

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   str					  =							True
					check_hidden_states_output(a   ,   a   ,   a						)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE :   str					  =							True

					check_hidden_states_output(a   ,   a   ,   a						)
			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				pass
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			int   ,   a				:			str   ,   a				:			List[str]=False						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							super()._prepare_for_class(a   ,   a   ,   return_labels=a						)

				if return_labels:
					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
						del inputs_dict["labels"]

				return inputs_dict
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Tuple:

				"""simple docstring"""

				if not self.model_tester.is_training:
					return

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE :   Tuple					  =							True

				for model_class in self.all_model_classes:
					# LevitForImageClassificationWithTeacher supports inference-only
					if (
					    model_class in get_values(a						)
					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
					):
						continue
					SCREAMING_SNAKE_CASE :   str					  =							model_class(a						)
					model.to(a						)
					model.train()
					SCREAMING_SNAKE_CASE :   List[str]					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(**a						).loss
					loss.backward()
			def 				__UpperCamelCase			(      self				:			Dict						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
					return

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							False
				SCREAMING_SNAKE_CASE :   str					  =							True

				for model_class in self.all_model_classes:
					if model_class in get_values(a						) or not model_class.supports_gradient_checkpointing:
						continue
					# LevitForImageClassificationWithTeacher supports inference-only
					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
						continue
					SCREAMING_SNAKE_CASE :   Any					  =							model_class(a						)
					model.gradient_checkpointing_enable()
					model.to(a						)
					model.train()
					SCREAMING_SNAKE_CASE :   str					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)
					SCREAMING_SNAKE_CASE :   Dict					  =							model(**a						).loss
					loss.backward()
			def 				__UpperCamelCase			(      self				:			Any						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							self.model_tester.prepare_config_and_inputs_for_common()

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[
				    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
				    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
				    {"title": "regression", "num_labels": 1, "dtype": torch.float},
				]

				for model_class in self.all_model_classes:
					if (
					    model_class
					    not in [
					        *get_values(a						),
					    ]
					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
					):
						continue

					for problem_type in problem_types:
						with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"						):
							SCREAMING_SNAKE_CASE :   str					  =							problem_type["title"]
							SCREAMING_SNAKE_CASE :   List[str]					  =							problem_type["num_labels"]

							SCREAMING_SNAKE_CASE :   str					  =							model_class(a						)
							model.to(a						)
							model.train()

							SCREAMING_SNAKE_CASE :   Dict					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)

							if problem_type["num_labels"] > 1:
								SCREAMING_SNAKE_CASE :   List[str]					  =							inputs["labels"].unsqueeze(1						).repeat(1   ,   problem_type["num_labels"]						)

							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							inputs["labels"].to(problem_type["dtype"]						)

							# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
							# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
							# they have the same size." which is a symptom something in wrong for the regression problem.
							# See https://github.com/huggingface/transformers/issues/11780
							with warnings.catch_warnings(record=a						) as warning_list:
								SCREAMING_SNAKE_CASE :   List[Any]					  =							model(**a						).loss
							for w in warning_list:
								if "Using a target size that is different to the input size" in str(w.message						):
									raise ValueError(
									    F"Something is going wrong in the regression problem: intercepted {w.message}"						)

							loss.backward()






			@slow
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							List[str]:

				"""simple docstring"""

				for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE :   Dict					  =							LevitModel.from_pretrained(a						)
					self.assertIsNotNone(a						)

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   str					  =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_torch
@require_vision
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@cached_property
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Dict:

				"""simple docstring"""

				return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)






			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
				    a						)

				SCREAMING_SNAKE_CASE :   Dict					  =							self.default_image_processor
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							prepare_img()
				SCREAMING_SNAKE_CASE :   Tuple					  =							image_processor(images=a   ,   return_tensors="pt"						).to(a						)

				# forward pass
				with torch.no_grad():
					SCREAMING_SNAKE_CASE :   List[str]					  =							model(**a						)

				# verify the logits
				SCREAMING_SNAKE_CASE :   Any					  =							torch.Size((1, 1000)						)
				self.assertEqual(outputs.logits.shape   ,   a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([1.0448, -0.3745, -1.8317]						).to(a						)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,   a   ,   atol=1e-4						)						)






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 convert_to_rgb, 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


if is_vision_available():
		import PIL


_lowercase		:  Tuple			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						List[str]						    =					['pixel_values']



					def __init__(		self     :     List[Any]       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Dict[str, int] = None       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Union[int, float] = 1 / 2_55       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     bool = True       ,      **lowerCamelCase     :     Optional[Any]       ,      ):
							super().__init__(**lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      size if size is not None else {'height': 3_84, 'width': 3_84}
							lowerCamelCase_	:     List[str] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      do_resize
							lowerCamelCase_	:     int 						=      size
							lowerCamelCase_	:     Union[str, Any] 						=      resample
							lowerCamelCase_	:     Tuple 						=      do_rescale
							lowerCamelCase_	:     Dict 						=      rescale_factor
							lowerCamelCase_	:     int 						=      do_normalize
							lowerCamelCase_	:     Any 						=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowerCamelCase_	:     str 						=      image_std if image_std is not None else OPENAI_CLIP_STD
							lowerCamelCase_	:     int 						=      do_convert_rgb



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Dict[str, int]       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     List[Any]       ,      ):
							lowerCamelCase_	:     List[Any] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)
							if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"						)
							lowerCamelCase_	:     Optional[Any] 						=      (size['height'], size['width'])
							return resize(lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Union[int, float]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Union[str, Any]       ,      ):
							return rescale(lowerCamelCase       ,      scale=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     str       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							return normalize(lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)





					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     ImageInput       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[Dict[str, int]] = None       ,      lowerCamelCase     :     PILImageResampling = None       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[float] = None       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[str, TensorType]] = None       ,      lowerCamelCase     :     bool = None       ,      lowerCamelCase     :     ChannelDimension = ChannelDimension.FIRST       ,      **lowerCamelCase     :     Any       ,      ):
							lowerCamelCase_	:     Optional[Any] 						=      do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_	:     int 						=      resample if resample is not None else self.resample
							lowerCamelCase_	:     Dict 						=      do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_	:     Any 						=      rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_	:     str 						=      do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_	:     List[str] 						=      image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_	:     str 						=      image_std if image_std is not None else self.image_std
							lowerCamelCase_	:     int 						=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowerCamelCase_	:     Union[str, Any] 						=      size if size is not None else self.size
							lowerCamelCase_	:     Optional[int] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      make_list_of_images(lowerCamelCase						)

							if not valid_images(lowerCamelCase						):
									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_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:
									lowerCamelCase_	:     int 						=      [convert_to_rgb(lowerCamelCase						) for image in images]

							# All transformations expect numpy arrays.
							lowerCamelCase_	:     List[Any] 						=      [to_numpy_array(lowerCamelCase						) for image in images]

							if do_resize:
									lowerCamelCase_	:     Optional[int] 						=      [self.resize(image=lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase						) for image in images]

							if do_rescale:
									lowerCamelCase_	:     Dict 						=      [self.rescale(image=lowerCamelCase       ,      scale=lowerCamelCase						) for image in images]

							if do_normalize:
									lowerCamelCase_	:     Optional[int] 						=      [self.normalize(image=lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Optional[int] 						=      [to_channel_dimension_format(lowerCamelCase       ,      lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Optional[int] 						=      BatchFeature(data={'pixel_values': images}       ,      tensor_type=lowerCamelCase						)

							return encoded_outputs
