

"""simple docstring"""


import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
			import torch

			from transformers import (
			    MarkupLMForQuestionAnswering,
			    MarkupLMForSequenceClassification,
			    MarkupLMForTokenClassification,
			    MarkupLMModel,
			)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer





class 					snake_case			:

				def __init__(			self							:       Any  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       Dict=1_3  ,						UpperCamelCase__							:       List[str]=7  ,						UpperCamelCase__							:       Any=True  ,						UpperCamelCase__							:       Union[str, Any]=True  ,						UpperCamelCase__							:       Dict=True  ,						UpperCamelCase__							:       str=True  ,						UpperCamelCase__							:       Optional[Any]=9_9  ,						UpperCamelCase__							:       Optional[int]=3_2  ,						UpperCamelCase__							:       str=5  ,						UpperCamelCase__							:       Dict=4  ,						UpperCamelCase__							:       Tuple=3_7  ,						UpperCamelCase__							:       Dict="gelu"  ,						UpperCamelCase__							:       Optional[int]=0.1  ,						UpperCamelCase__							:       List[Any]=0.1  ,						UpperCamelCase__							:       str=5_1_2  ,						UpperCamelCase__							:       int=1_6  ,						UpperCamelCase__							:       Dict=2  ,						UpperCamelCase__							:       Union[str, Any]=0.02  ,						UpperCamelCase__							:       Union[str, Any]=3  ,						UpperCamelCase__							:       Union[str, Any]=None  ,						UpperCamelCase__							:       str=2_0  ,						UpperCamelCase__							:       Union[str, Any]=3_0  ,						UpperCamelCase__							:       Dict=2  ,						UpperCamelCase__							:       Optional[int]=2  ,						UpperCamelCase__							:       Any=1_0  ,						)->     Any:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    parent
						__lowerCAmelCase: List[Any]					   =    batch_size
						__lowerCAmelCase: Tuple					   =    seq_length
						__lowerCAmelCase: Union[str, Any]					   =    is_training
						__lowerCAmelCase: Any					   =    use_input_mask
						__lowerCAmelCase: List[Any]					   =    use_token_type_ids
						__lowerCAmelCase: int					   =    use_labels
						__lowerCAmelCase: Union[str, Any]					   =    vocab_size
						__lowerCAmelCase: Union[str, Any]					   =    hidden_size
						__lowerCAmelCase: str					   =    num_hidden_layers
						__lowerCAmelCase: Optional[Any]					   =    num_attention_heads
						__lowerCAmelCase: List[str]					   =    intermediate_size
						__lowerCAmelCase: List[str]					   =    hidden_act
						__lowerCAmelCase: Optional[int]					   =    hidden_dropout_prob
						__lowerCAmelCase: Dict					   =    attention_probs_dropout_prob
						__lowerCAmelCase: Optional[Any]					   =    max_position_embeddings
						__lowerCAmelCase: Any					   =    type_vocab_size
						__lowerCAmelCase: Union[str, Any]					   =    type_sequence_label_size
						__lowerCAmelCase: List[str]					   =    initializer_range
						__lowerCAmelCase: Dict					   =    num_labels
						__lowerCAmelCase: Tuple					   =    scope
						__lowerCAmelCase: Dict					   =    max_xpath_tag_unit_embeddings
						__lowerCAmelCase: Optional[int]					   =    max_xpath_subs_unit_embeddings
						__lowerCAmelCase: Tuple					   =    tag_pad_id
						__lowerCAmelCase: str					   =    subs_pad_id
						__lowerCAmelCase: str					   =    max_depth

				def 							lowercase_  (			self							:       int)->     str:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size)

						__lowerCAmelCase: Optional[int]					   =    ids_tensor(
						    [self.batch_size, self.seq_length, self.max_depth]  ,						self.max_xpath_tag_unit_embeddings)

						__lowerCAmelCase: int					   =    ids_tensor(
						    [self.batch_size, self.seq_length, self.max_depth]  ,						self.max_xpath_subs_unit_embeddings)

						__lowerCAmelCase: int					   =    None
						if self.use_input_mask:
								__lowerCAmelCase: Tuple					   =    ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2)

						__lowerCAmelCase: Tuple					   =    None
						if self.use_token_type_ids:
								__lowerCAmelCase: Optional[Any]					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size)

						__lowerCAmelCase: Optional[Any]					   =    None
						__lowerCAmelCase: Optional[int]					   =    None
						if self.use_labels:
								__lowerCAmelCase: Tuple					   =    ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)
								__lowerCAmelCase: str					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels)

						__lowerCAmelCase: str					   =    self.get_config()

						return (
						    config,
						    input_ids,
						    xpath_tags_seq,
						    xpath_subs_seq,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						)

				def 							lowercase_  (			self							:       Tuple)->     Union[str, Any]:



						'''simple docstring'''


						return MarkupLMConfig(
						    vocab_size=self.vocab_size  ,						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  ,						max_position_embeddings=self.max_position_embeddings  ,						type_vocab_size=self.type_vocab_size  ,						initializer_range=self.initializer_range  ,						max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings  ,						max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings  ,						tag_pad_id=self.tag_pad_id  ,						subs_pad_id=self.subs_pad_id  ,						max_depth=self.max_depth  ,						)

				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[Any]  ,						UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Dict  ,						)->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    MarkupLMModel(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						print("Configs:"  ,						model.config.tag_pad_id  ,						model.config.subs_pad_id)
						__lowerCAmelCase: str					   =    model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__)
						__lowerCAmelCase: Optional[Any]					   =    model(UpperCamelCase__  ,						token_type_ids=UpperCamelCase__)
						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size))
						self.parent.assertEqual(result.pooler_output.shape  ,						(self.batch_size, self.hidden_size))

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Tuple  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       Any  ,						)->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    self.num_labels
						__lowerCAmelCase: Optional[Any]					   =    MarkupLMForSequenceClassification(UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase: str					   =    model(
						    UpperCamelCase__  ,						xpath_tags_seq=UpperCamelCase__  ,						xpath_subs_seq=UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  ,						)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels))

				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Optional[int]  ,						)->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    self.num_labels
						__lowerCAmelCase: Dict					   =    MarkupLMForTokenClassification(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase: Any					   =    model(
						    UpperCamelCase__  ,						xpath_tags_seq=UpperCamelCase__  ,						xpath_subs_seq=UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  ,						)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels))

				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       Dict  ,						)->     Any:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    MarkupLMForQuestionAnswering(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase: Tuple					   =    model(
						    UpperCamelCase__  ,						xpath_tags_seq=UpperCamelCase__  ,						xpath_subs_seq=UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						start_positions=UpperCamelCase__  ,						end_positions=UpperCamelCase__  ,						)
						self.parent.assertEqual(result.start_logits.shape  ,						(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape  ,						(self.batch_size, self.seq_length))




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



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				
						): Union[str, Any]					   =    config_and_inputs
						__lowerCAmelCase: Any					   =    {
						    "input_ids": input_ids,
						    "xpath_tags_seq": xpath_tags_seq,
						    "xpath_subs_seq": xpath_subs_seq,
						    "token_type_ids": token_type_ids,
						    "attention_mask": input_mask,
						}
						return config, inputs_dict





@require_torch
class 					snake_case			(       __snake_case,							__snake_case,							unittest.TestCase						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				SCREAMING_SNAKE_CASE_     :   List[str]        =						(
				    {
				        """feature-extraction""": MarkupLMModel,
				        """question-answering""": MarkupLMForQuestionAnswering,
				        """text-classification""": MarkupLMForSequenceClassification,
				        """token-classification""": MarkupLMForTokenClassification,
				        """zero-shot""": MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)

				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       Optional[int])->     List[str]:



						'''simple docstring'''


						return True

				def 							lowercase_  (			self							:       Dict)->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    MarkupLMModelTester(self)
						__lowerCAmelCase: Dict					   =    ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7)

				def 							lowercase_  (			self							:       Any)->     int:



						'''simple docstring'''


						self.config_tester.run_common_tests()

				def 							lowercase_  (			self							:       int)->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase__)

				def 							lowercase_  (			self							:       int)->     int:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Dict)->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__)




				def 							lowercase_  (			self							:       str)->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__)






def 					a__					(					)			->			int:
		__lowerCAmelCase: Dict					   =    "\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

		return html_string





@require_torch
class 					snake_case			(       unittest.TestCase						):

				@cached_property
				def 							lowercase_  (			self							:       Tuple)->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    MarkupLMFeatureExtractor()
						__lowerCAmelCase: Optional[int]					   =    MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")

						return MarkupLMProcessor(UpperCamelCase__  ,						UpperCamelCase__)




				@slow
				def 							lowercase_  (			self							:       Optional[int])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(UpperCamelCase__)

						__lowerCAmelCase: int					   =    self.default_processor

						__lowerCAmelCase: List[Any]					   =    processor(prepare_html_string()  ,						return_tensors="pt")
						__lowerCAmelCase: Tuple					   =    inputs.to(UpperCamelCase__)

						# forward pass
						with torch.no_grad():
								__lowerCAmelCase: Any					   =    model(**UpperCamelCase__)

						# verify the last hidden states
						__lowerCAmelCase: List[Any]					   =    torch.Size([1, 1_4, 7_6_8])
						self.assertEqual(outputs.last_hidden_state.shape  ,						UpperCamelCase__)

						__lowerCAmelCase: str					   =    torch.tensor(
						    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]).to(UpperCamelCase__)

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





"""simple docstring"""


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__A		     =    logging.get_logger(__name__)


if is_vision_available():
			import PIL





class 					snake_case			(       __snake_case						):
				SCREAMING_SNAKE_CASE_     :   Union[str, Any]        =						["""pixel_values"""]

				def __init__(			self							:       int  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						**UpperCamelCase__							:       Tuple  ,						)->     None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase: List[str]					   =    size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
						if isinstance(UpperCamelCase__  ,						(tuple, list)):
								# The previous feature extractor size parameter was in (width, height) format
								__lowerCAmelCase: str					   =    size[::-1]
						__lowerCAmelCase: int					   =    get_size_dict(UpperCamelCase__)

						__lowerCAmelCase: Union[str, Any]					   =    do_resize
						__lowerCAmelCase: Optional[Any]					   =    size
						__lowerCAmelCase: List[str]					   =    resample
						__lowerCAmelCase: Tuple					   =    do_thumbnail
						__lowerCAmelCase: int					   =    do_align_long_axis
						__lowerCAmelCase: Any					   =    do_pad
						__lowerCAmelCase: Any					   =    do_rescale
						__lowerCAmelCase: int					   =    rescale_factor
						__lowerCAmelCase: str					   =    do_normalize
						__lowerCAmelCase: List[str]					   =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase: List[Any]					   =    image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    get_image_size(UpperCamelCase__)
						__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    size["height"], size["width"]

						if (output_width < output_height and input_width > input_height) or (
						    output_width > output_height and input_width < input_height
						):
								__lowerCAmelCase: List[Any]					   =    np.rotaa(UpperCamelCase__  ,						3)

						if data_format is not None:
								__lowerCAmelCase: Dict					   =    to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__)

						return image

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    size["height"], size["width"]
						__lowerCAmelCase   ,				__lowerCAmelCase: Optional[Any]					   =    get_image_size(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    output_width - input_width
						__lowerCAmelCase: str					   =    output_height - input_height

						if random_padding:
								__lowerCAmelCase: Tuple					   =    np.random.randint(low=0  ,						high=delta_height + 1)
								__lowerCAmelCase: Union[str, Any]					   =    np.random.randint(low=0  ,						high=delta_width + 1)
						else:
								__lowerCAmelCase: str					   =    delta_height // 2
								__lowerCAmelCase: Optional[Any]					   =    delta_width // 2

						__lowerCAmelCase: List[Any]					   =    delta_height - pad_top
						__lowerCAmelCase: Dict					   =    delta_width - pad_left

						__lowerCAmelCase: Optional[Any]					   =    ((pad_top, pad_bottom), (pad_left, pad_right))
						return pad(UpperCamelCase__  ,						UpperCamelCase__  ,						data_format=UpperCamelCase__)

				def 							lowercase_  (			self							:       Union[str, Any]  ,						*UpperCamelCase__							:       str  ,						**UpperCamelCase__							:       Optional[int])->     Optional[int]:



						'''simple docstring'''


						logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead.")
						return self.pad_image(*UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BICUBIC  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Optional[int]  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    get_image_size(UpperCamelCase__)
						__lowerCAmelCase   ,				__lowerCAmelCase: Tuple					   =    size["height"], size["width"]

						# We always resize to the smallest of either the input or output size.
						__lowerCAmelCase: Union[str, Any]					   =    min(UpperCamelCase__  ,						UpperCamelCase__)
						__lowerCAmelCase: List[Any]					   =    min(UpperCamelCase__  ,						UpperCamelCase__)

						if height == input_height and width == input_width:
								return image

						if input_height > input_width:
								__lowerCAmelCase: Dict					   =    int(input_width * height / input_height)
						elif input_width > input_height:
								__lowerCAmelCase: Any					   =    int(input_height * width / input_width)

						return resize(
						    UpperCamelCase__  ,						size=(height, width)  ,						resample=UpperCamelCase__  ,						reducing_gap=2.0  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BICUBIC  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Union[str, Any]  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Dict					   =    min(size["height"]  ,						size["width"])
						__lowerCAmelCase: List[str]					   =    get_resize_output_image_size(UpperCamelCase__  ,						size=UpperCamelCase__  ,						default_to_square=UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    resize(UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)
						return resized_image

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Union[int, float]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Dict  ,						)->     int:



						'''simple docstring'''


						return rescale(UpperCamelCase__  ,						scale=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Any  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       List[str]  ,						)->     np.ndarray:



						'''simple docstring'''


						return normalize(UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)




				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       ImageInput  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       float = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[str, TensorType]] = None  ,						UpperCamelCase__							:       Optional[ChannelDimension] = ChannelDimension.FIRST  ,						**UpperCamelCase__							:       Optional[Any]  ,						)->     PIL.Image.Image:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase: Any					   =    size if size is not None else self.size
						if isinstance(UpperCamelCase__  ,						(tuple, list)):
								# Previous feature extractor had size in (width, height) format
								__lowerCAmelCase: int					   =    size[::-1]
						__lowerCAmelCase: Dict					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Optional[Any]					   =    resample if resample is not None else self.resample
						__lowerCAmelCase: int					   =    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
						__lowerCAmelCase: Any					   =    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
						__lowerCAmelCase: str					   =    do_pad if do_pad is not None else self.do_pad
						__lowerCAmelCase: List[Any]					   =    do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase: List[Any]					   =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase: List[Any]					   =    do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase: Union[str, Any]					   =    image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase: List[str]					   =    image_std if image_std is not None else self.image_std

						__lowerCAmelCase: Union[str, Any]					   =    make_list_of_images(UpperCamelCase__)

						if not valid_images(UpperCamelCase__):
								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_rescale and rescale_factor is None:
								raise ValueError("Rescale factor must be specified if do_rescale is True.")

						if do_pad and size is None:
								raise ValueError("Size must be specified if do_pad 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.
						__lowerCAmelCase: int					   =    [to_numpy_array(UpperCamelCase__) for image in images]

						if do_align_long_axis:
								__lowerCAmelCase: Any					   =    [self.align_long_axis(UpperCamelCase__  ,						size=UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase: Tuple					   =    [self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__) for image in images]

						if do_thumbnail:
								__lowerCAmelCase: List[Any]					   =    [self.thumbnail(image=UpperCamelCase__  ,						size=UpperCamelCase__) for image in images]

						if do_pad:
								__lowerCAmelCase: Tuple					   =    [self.pad_image(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						random_padding=UpperCamelCase__) for image in images]

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

						if do_normalize:
								__lowerCAmelCase: Tuple					   =    [self.normalize(image=UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__) for image in images]

						__lowerCAmelCase: Union[str, Any]					   =    [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__) for image in images]

						__lowerCAmelCase: Optional[Any]					   =    {"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__)


