from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLMForMultipleChoice,
				    TFXLMForQuestionAnsweringSimple,
				    TFXLMForSequenceClassification,
				    TFXLMForTokenClassification,
				    TFXLMModel,
				    TFXLMWithLMHeadModel,
				    XLMConfig,
				)



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				):
													_snake_case						:     Tuple				=    parent
													_snake_case						:     Tuple				=    13
													_snake_case						:     Any				=    7
													_snake_case						:     Tuple				=    True
													_snake_case						:     Union[str, Any]				=    True
													_snake_case						:     str				=    True
													_snake_case						:     Union[str, Any]				=    True
													_snake_case						:     Union[str, Any]				=    True
													_snake_case						:     Union[str, Any]				=    False
													_snake_case						:     Optional[int]				=    False
													_snake_case						:     List[str]				=    False
													_snake_case						:     Optional[Any]				=    2
													_snake_case						:     Tuple				=    99
													_snake_case						:     Dict				=    0
													_snake_case						:     List[Any]				=    32
													_snake_case						:     Dict				=    2
													_snake_case						:     Union[str, Any]				=    4
													_snake_case						:     int				=    0.1
													_snake_case						:     str				=    0.1
													_snake_case						:     int				=    512
													_snake_case						:     Optional[Any]				=    16
													_snake_case						:     int				=    2
													_snake_case						:     Tuple				=    0.02
													_snake_case						:     str				=    3
													_snake_case						:     Union[str, Any]				=    4
													_snake_case						:     Optional[Any]				=    "last"
													_snake_case						:     Any				=    True
													_snake_case						:     List[str]				=    None
													_snake_case						:     Dict				=    0



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size				)
													_snake_case						:     str				=    random_attention_mask([self.batch_size, self.seq_length]      ,				dtype=tf.floataa				)

													_snake_case						:     List[Any]				=    None
													if self.use_input_lengths:
																			_snake_case						:     List[Any]				=    (
																			    ids_tensor([self.batch_size]      ,				vocab_size=2				) + self.seq_length - 2
																			)  # small variation of seq_length

													_snake_case						:     Any				=    None
													if self.use_token_type_ids:
																			_snake_case						:     Dict				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.n_langs				)

													_snake_case						:     List[str]				=    None
													_snake_case						:     List[Any]				=    None
													_snake_case						:     Optional[Any]				=    None
													if self.use_labels:
																			_snake_case						:     Optional[Any]				=    ids_tensor([self.batch_size]      ,				self.type_sequence_label_size				)
																			_snake_case						:     Tuple				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.num_labels				)
																			_snake_case						:     Any				=    ids_tensor([self.batch_size]      ,				2      ,				dtype=tf.floataa				)
																			_snake_case						:     int				=    ids_tensor([self.batch_size]      ,				self.num_choices				)

													_snake_case						:     Optional[Any]				=    XLMConfig(
													    vocab_size=self.vocab_size      ,				n_special=self.n_special      ,				emb_dim=self.hidden_size      ,				n_layers=self.num_hidden_layers      ,				n_heads=self.num_attention_heads      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				gelu_activation=self.gelu_activation      ,				sinusoidal_embeddings=self.sinusoidal_embeddings      ,				asm=self.asm      ,				causal=self.causal      ,				n_langs=self.n_langs      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				summary_type=self.summary_type      ,				use_proj=self.use_proj      ,				bos_token_id=self.bos_token_id      ,				)

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    input_lengths,
													    sequence_labels,
													    token_labels,
													    is_impossible_labels,
													    choice_labels,
													    input_mask,
													)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     List[str]				=    TFXLMModel(config=lowercase_				)
													_snake_case						:     Optional[int]				=    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
													_snake_case						:     List[Any]				=    model(lowercase_				)

													_snake_case						:     Tuple				=    [input_ids, input_mask]
													_snake_case						:     List[str]				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     str				=    TFXLMWithLMHeadModel(lowercase_				)

													_snake_case						:     Optional[int]				=    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
													_snake_case						:     Any				=    model(lowercase_				)

													_snake_case						:     Any				=    outputs

													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     int				=    TFXLMForQuestionAnsweringSimple(lowercase_				)

													_snake_case						:     List[str]				=    {"input_ids": input_ids, "lengths": input_lengths}

													_snake_case						:     List[str]				=    model(lowercase_				)

													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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     str				=    TFXLMForSequenceClassification(lowercase_				)

													_snake_case						:     Optional[Any]				=    {"input_ids": input_ids, "lengths": input_lengths}

													_snake_case						:     str				=    model(lowercase_				)

													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Optional[int]				=    self.num_labels
													_snake_case						:     Any				=    TFXLMForTokenClassification(config=lowercase_				)
													_snake_case						:     int				=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													_snake_case						:     Dict				=    model(lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.num_labels)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     str				=    self.num_choices
													_snake_case						:     List[Any]				=    TFXLMForMultipleChoice(config=lowercase_				)
													_snake_case						:     int				=    tf.tile(tf.expand_dims(lowercase_      ,				1				)      ,				(1, self.num_choices, 1)				)
													_snake_case						:     str				=    tf.tile(tf.expand_dims(lowercase_      ,				1				)      ,				(1, self.num_choices, 1)				)
													_snake_case						:     str				=    tf.tile(tf.expand_dims(lowercase_      ,				1				)      ,				(1, self.num_choices, 1)				)
													_snake_case						:     Union[str, Any]				=    {
													    "input_ids": multiple_choice_inputs_ids,
													    "attention_mask": multiple_choice_input_mask,
													    "token_type_ids": multiple_choice_token_type_ids,
													}
													_snake_case						:     Optional[Any]				=    model(lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.prepare_config_and_inputs()
													(
													    (
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,
													)						:     str				=    config_and_inputs
													_snake_case						:     List[str]				=    {
													    "input_ids": input_ids,
													    "token_type_ids": token_type_ids,
													    "langs": token_type_ids,
													    "lengths": input_lengths,
													}
													return config, inputs_dict




@require_tf
class    lowercase_    (     __snake_case						, __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		(
							    (
							        TFXLMModel,
							        TFXLMWithLMHeadModel,
							        TFXLMForSequenceClassification,
							        TFXLMForQuestionAnsweringSimple,
							        TFXLMForTokenClassification,
							        TFXLMForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							_lowerCamelCase						=		(
							    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
							)  # TODO (PVP): Check other models whether language generation is also applicable
							_lowerCamelCase						=		(
							    {
							        'feature-extraction': TFXLMModel,
							        'fill-mask': TFXLMWithLMHeadModel,
							        'question-answering': TFXLMForQuestionAnsweringSimple,
							        'text-classification': TFXLMForSequenceClassification,
							        'text-generation': TFXLMWithLMHeadModel,
							        'token-classification': TFXLMForTokenClassification,
							        'zero-shot': TFXLMForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													if (
													    pipeline_test_casse_name == "QAPipelineTests"
													    and tokenizer_name is not None
													    and not tokenizer_name.endswith("Fast"				)
													):
																			# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																			# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																			# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																			return True

													return False



							def        UpperCamelCase       (						self				):
													_snake_case						:     str				=    TFXLMModelTester(self				)
													_snake_case						:     Optional[int]				=    ConfigTester(self      ,				config_class=lowercase_      ,				emb_dim=37				)



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



							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_model(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_lm_head(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_qa(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[int]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_sequence_classif(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Union[str, Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_for_token_classification(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase_				)



							@slow
							def        UpperCamelCase       (						self				):
													for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:     List[str]				=    TFXLMModel.from_pretrained(lowercase_				)
																			self.assertIsNotNone(lowercase_				)




@require_tf
class    lowercase_    (     unittest.TestCase		):



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"				)
													_snake_case						:     Union[str, Any]				=    tf.convert_to_tensor([[14, 447]]      ,				dtype=tf.intaa				)  # the president
													_snake_case						:     List[Any]				=    [
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													]  # the president the president the president the president the president the president the president the president the president the president
													# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
													_snake_case						:     Union[str, Any]				=    model.generate(lowercase_      ,				do_sample=lowercase_				)
													self.assertListEqual(output_ids[0].numpy().tolist()      ,				lowercase_				)







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


snake_case			 =						logging.get_logger(__name__)


if is_vision_available():
   import PIL


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		['''pixel_values''']


     def __init__( self				:		Tuple    ,       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 / 255    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       **UpperCAmelCase_				:		List[str]    ,       ):
       super().__init__(**UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size if size is not None else {"height": 2560, "width": 1920}
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # The previous feature extractor size parameter was in (width, height) format
         SCREAMING_SNAKE_CASE				:					List[str]		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_size_dict(UpperCAmelCase_					)

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


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[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
       ):
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.rotaa(UpperCAmelCase_    ,       3					)

       if data_format is not None:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					)

       return image


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			size["height"], size["width"]
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_image_size(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_width - input_width
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_height - input_height

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

       SCREAMING_SNAKE_CASE				:					Any		  =			delta_height - pad_top
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			delta_width - pad_left

       SCREAMING_SNAKE_CASE				:					Dict		  =			((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(UpperCAmelCase_    ,       UpperCAmelCase_    ,       data_format=UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]    ,       *UpperCAmelCase_				:		int    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       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  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		int    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					int		  =			size["height"], size["width"]

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

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

       if input_height > input_width:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			int(input_width * height / input_height					)
       elif input_width > input_height:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(input_height * width / input_width					)

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


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		Optional[int]    ,       ):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			min(size["height"]    ,       size["width"]					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_resize_output_image_size(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       default_to_square=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			resize(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       resample=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)
       return resized_image


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


     def  _A				( self				:		Tuple    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		List[Any]    ,       ):
       return normalize(UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)






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

       SCREAMING_SNAKE_CASE				:					Dict		  =			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.
       SCREAMING_SNAKE_CASE				:					int		  =			[to_numpy_array(UpperCAmelCase_					) for image in images]

       if do_align_long_axis:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.align_long_axis(UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

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

       if do_thumbnail:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[self.thumbnail(image=UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

       if do_pad:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[self.pad_image(image=UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       random_padding=UpperCAmelCase_					) for image in images]

       if do_rescale:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.rescale(image=UpperCAmelCase_    ,       scale=UpperCAmelCase_					) for image in images]

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

       SCREAMING_SNAKE_CASE				:					str		  =			[to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					) for image in images]

       SCREAMING_SNAKE_CASE				:					Tuple		  =			{"pixel_values": images}
       return BatchFeature(data=UpperCAmelCase_    ,       tensor_type=UpperCAmelCase_					)






