





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.generation import (
				    FlaxForcedBOSTokenLogitsProcessor,
				    FlaxForcedEOSTokenLogitsProcessor,
				    FlaxLogitsProcessorList,
				    FlaxMinLengthLogitsProcessor,
				    FlaxTemperatureLogitsWarper,
				    FlaxTopKLogitsWarper,
				    FlaxTopPLogitsWarper,
				)



@require_flax
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict							,	a				:     int							,	a				:     int    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.ones((batch_size, length)    ) / length
													return scores

							def      A_  (      self				:     Any    )	->str:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					None
													SCREAMING_SNAKE_CASE__     :				int   =					20

													SCREAMING_SNAKE_CASE__     :				int   =					self._get_uniform_logits(batch_size=2							,	length=a    )

													# tweak scores to not be uniform anymore
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					scores.at[1, 5].set((1 / length) + 0.1    )  # peak, 1st batch
													SCREAMING_SNAKE_CASE__     :				str   =					scores.at[1, 10].set((1 / length) - 0.4    )  # valley, 1st batch

													# compute softmax
													SCREAMING_SNAKE_CASE__     :				int   =					jax.nn.softmax(a							,	axis=-1    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxTemperatureLogitsWarper(temperature=0.5    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					FlaxTemperatureLogitsWarper(temperature=1.3    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					jax.nn.softmax(temp_dist_warper_sharper(a							,	scores.copy()							,	cur_len=a    )							,	axis=-1    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					jax.nn.softmax(temp_dist_warper_smoother(a							,	scores.copy()							,	cur_len=a    )							,	axis=-1    )

													# uniform distribution stays uniform
													self.assertTrue(jnp.allclose(probs[0, :]							,	warped_prob_sharp[0, :]							,	atol=1E-3    )    )
													self.assertTrue(jnp.allclose(probs[0, :]							,	warped_prob_smooth[0, :]							,	atol=1E-3    )    )

													# sharp peaks get higher, valleys get lower
													self.assertLess(probs[1, :].max()							,	warped_prob_sharp[1, :].max()    )
													self.assertGreater(probs[1, :].min()							,	warped_prob_sharp[1, :].min()    )

													# smooth peaks get lower, valleys get higher
													self.assertGreater(probs[1, :].max()							,	warped_prob_smooth[1, :].max()    )
													self.assertLess(probs[1, :].min()							,	warped_prob_smooth[1, :].min()    )

							def      A_  (      self				:     int    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None
													SCREAMING_SNAKE_CASE__     :				Dict   =					10
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					2

													# create ramp distribution
													SCREAMING_SNAKE_CASE__     :				str   =					np.broadcast_to(np.arange(a    )[None, :]							,	(batch_size, vocab_size)    ).copy()
													SCREAMING_SNAKE_CASE__     :				List[str]   =					ramp_logits[1:, : vocab_size // 2] + vocab_size

													SCREAMING_SNAKE_CASE__     :				Any   =					FlaxTopKLogitsWarper(3    )

													SCREAMING_SNAKE_CASE__     :				Any   =					top_k_warp(a							,	a							,	cur_len=a    )

													# check that correct tokens are filtered
													self.assertListEqual(jnp.isinf(scores[0]    ).tolist()							,	7 * [True] + 3 * [False]    )
													self.assertListEqual(jnp.isinf(scores[1]    ).tolist()							,	2 * [True] + 3 * [False] + 5 * [True]    )

													# check special case
													SCREAMING_SNAKE_CASE__     :				Dict   =					5
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					FlaxTopKLogitsWarper(top_k=1							,	filter_value=0.0							,	min_tokens_to_keep=3    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					np.broadcast_to(np.arange(a    )[None, :]							,	(batch_size, length)    ).copy()
													SCREAMING_SNAKE_CASE__     :				Dict   =					top_k_warp_safety_check(a							,	a							,	cur_len=a    )

													# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
													self.assertListEqual((scores == 0.0).sum(axis=-1    ).tolist()							,	[2, 2]    )

							def      A_  (      self				:     List[str]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				str   =					None
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					10
													SCREAMING_SNAKE_CASE__     :				Dict   =					2

													# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]    )    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxTopPLogitsWarper(0.8    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					np.exp(top_p_warp(a							,	a							,	cur_len=a    )    )

													# dist should be filtered to keep min num values so that sum is >= top_p
													# exp (-inf) => 0
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]    )
													self.assertTrue(np.allclose(a							,	a							,	atol=1E-3    )    )

													# check edge cases with negative and extreme logits
													SCREAMING_SNAKE_CASE__     :				str   =					np.broadcast_to(np.arange(a    )[None, :]							,	(batch_size, vocab_size)    ).copy() - (
													    vocab_size // 2
													)

													# make ramp_logits more extreme
													SCREAMING_SNAKE_CASE__     :				int   =					ramp_logits[1] * 100.0

													# make sure at least 2 tokens are kept
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxTopPLogitsWarper(0.9							,	min_tokens_to_keep=2							,	filter_value=0.0    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					top_p_warp(a							,	a							,	cur_len=a    )

													# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
													self.assertListEqual((filtered_dist != 0.0).sum(axis=-1    ).tolist()							,	[3, 2]    )

							def      A_  (      self				:     Any    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					20
													SCREAMING_SNAKE_CASE__     :				Tuple   =					4
													SCREAMING_SNAKE_CASE__     :				int   =					0

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					FlaxMinLengthLogitsProcessor(min_length=10							,	eos_token_id=a    )

													# check that min length is applied at length 5
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ids_tensor((batch_size, 20)							,	vocab_size=20    )
													SCREAMING_SNAKE_CASE__     :				int   =					5
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					min_dist_processor(a							,	a							,	cur_len=a    )
													self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()							,	4 * [-float("inf"    )]    )

													# check that min length is not applied anymore at length 15
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					15
													SCREAMING_SNAKE_CASE__     :				Dict   =					min_dist_processor(a							,	a							,	cur_len=a    )
													self.assertFalse(jnp.isinf(a    ).any()    )

							def      A_  (      self				:     Dict    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					20
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					4
													SCREAMING_SNAKE_CASE__     :				str   =					0

													SCREAMING_SNAKE_CASE__     :				Any   =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=a    )

													# check that all scores are -inf except the bos_token_id score
													SCREAMING_SNAKE_CASE__     :				Dict   =					ids_tensor((batch_size, 1)							,	vocab_size=20    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					1
													SCREAMING_SNAKE_CASE__     :				Dict   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					logits_processor(a							,	a							,	cur_len=a    )
													self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]    ).all()    )
													self.assertListEqual(scores[:, bos_token_id].tolist()							,	4 * [0]    )  # score for bos_token_id shold be zero

													# check that bos_token_id is not forced if current length is greater than 1
													SCREAMING_SNAKE_CASE__     :				Any   =					3
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					logits_processor(a							,	a							,	cur_len=a    )
													self.assertFalse(jnp.isinf(a    ).any()    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					20
													SCREAMING_SNAKE_CASE__     :				List[str]   =					4
													SCREAMING_SNAKE_CASE__     :				Dict   =					0
													SCREAMING_SNAKE_CASE__     :				Tuple   =					5

													SCREAMING_SNAKE_CASE__     :				Any   =					FlaxForcedEOSTokenLogitsProcessor(max_length=a							,	eos_token_id=a    )

													# check that all scores are -inf except the eos_token_id when max_length is reached
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					ids_tensor((batch_size, 4)							,	vocab_size=20    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					4
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				str   =					logits_processor(a							,	a							,	cur_len=a    )
													self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]    ).all()    )
													self.assertListEqual(scores[:, eos_token_id].tolist()							,	4 * [0]    )  # score for eos_token_id should be zero

													# check that eos_token_id is not forced if max_length is not reached
													SCREAMING_SNAKE_CASE__     :				str   =					3
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				int   =					logits_processor(a							,	a							,	cur_len=a    )
													self.assertFalse(jnp.isinf(a    ).any()    )

							def      A_  (      self				:     Optional[Any]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Dict   =					4
													SCREAMING_SNAKE_CASE__     :				List[str]   =					10
													SCREAMING_SNAKE_CASE__     :				Any   =					15
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					2
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					1
													SCREAMING_SNAKE_CASE__     :				str   =					15

													# dummy input_ids and scores
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ids_tensor((batch_size, sequence_length)							,	a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					input_ids.copy()

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					scores.copy()

													# instantiate all dist processors
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					FlaxTemperatureLogitsWarper(temperature=0.5    )
													SCREAMING_SNAKE_CASE__     :				str   =					FlaxTopKLogitsWarper(3    )
													SCREAMING_SNAKE_CASE__     :				str   =					FlaxTopPLogitsWarper(0.8    )

													# instantiate all logits processors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					FlaxMinLengthLogitsProcessor(min_length=10							,	eos_token_id=a    )
													SCREAMING_SNAKE_CASE__     :				int   =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxForcedEOSTokenLogitsProcessor(max_length=a							,	eos_token_id=a    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					10

													# no processor list
													SCREAMING_SNAKE_CASE__     :				Any   =					temp_dist_warp(a							,	a							,	cur_len=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					top_k_warp(a							,	a							,	cur_len=a    )
													SCREAMING_SNAKE_CASE__     :				int   =					top_p_warp(a							,	a							,	cur_len=a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					min_dist_proc(a							,	a							,	cur_len=a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					bos_dist_proc(a							,	a							,	cur_len=a    )
													SCREAMING_SNAKE_CASE__     :				int   =					eos_dist_proc(a							,	a							,	cur_len=a    )

													# with processor list
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					FlaxLogitsProcessorList(
													    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					processor(a							,	a							,	cur_len=a    )

													# scores should be equal
													self.assertTrue(jnp.allclose(a							,	a							,	atol=1E-3    )    )

													# input_ids should never be changed
													self.assertListEqual(input_ids.tolist()							,	input_ids_comp.tolist()    )






							def      A_  (      self				:     List[str]    )	->str:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					4
													SCREAMING_SNAKE_CASE__     :				Dict   =					10
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					15
													SCREAMING_SNAKE_CASE__     :				Tuple   =					2
													SCREAMING_SNAKE_CASE__     :				Dict   =					1
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					15

													# dummy input_ids and scores
													SCREAMING_SNAKE_CASE__     :				Tuple   =					ids_tensor((batch_size, sequence_length)							,	a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					input_ids.copy()

													SCREAMING_SNAKE_CASE__     :				Any   =					self._get_uniform_logits(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					scores.copy()

													# instantiate all dist processors
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					FlaxTemperatureLogitsWarper(temperature=0.5    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					FlaxTopKLogitsWarper(3    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					FlaxTopPLogitsWarper(0.8    )

													# instantiate all logits processors
													SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxMinLengthLogitsProcessor(min_length=10							,	eos_token_id=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					FlaxForcedEOSTokenLogitsProcessor(max_length=a							,	eos_token_id=a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					10

													# no processor list
													def run_no_processor_list(a				:     Union[str, Any]							,	a				:     Optional[int]							,	a				:     Union[str, Any]    ):
																			SCREAMING_SNAKE_CASE__     :				Any   =					temp_dist_warp(a							,	a							,	cur_len=a    )
																			SCREAMING_SNAKE_CASE__     :				str   =					top_k_warp(a							,	a							,	cur_len=a    )
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					top_p_warp(a							,	a							,	cur_len=a    )
																			SCREAMING_SNAKE_CASE__     :				int   =					min_dist_proc(a							,	a							,	cur_len=a    )
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					bos_dist_proc(a							,	a							,	cur_len=a    )
																			SCREAMING_SNAKE_CASE__     :				str   =					eos_dist_proc(a							,	a							,	cur_len=a    )
																			return scores

													# with processor list
													def run_processor_list(a				:     int							,	a				:     Union[str, Any]							,	a				:     Dict    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxLogitsProcessorList(
																			    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]    )
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					processor(a							,	a							,	cur_len=a    )
																			return scores

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.jit(a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.jit(a    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					jitted_run_no_processor_list(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					jitted_run_processor_list(a							,	a							,	a    )

													# scores should be equal
													self.assertTrue(jnp.allclose(a							,	a							,	atol=1E-3    )    )

													# input_ids should never be changed
													self.assertListEqual(input_ids.tolist()							,	input_ids_comp.tolist()    )






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


__lowercase       :int       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     int							,	a				:     bool = True							,	a				:     Optional[Dict[str, int]] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	**a				:     List[str]							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_56}
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				int   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					crop_size
													SCREAMING_SNAKE_CASE__     :				Any   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Any   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				int   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				int   =					image_std if image_std is not None else IMAGENET_STANDARD_STD

							def      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					get_size_dict(a							,	default_to_square=a    )
													if "shortest_edge" not in size:
																			raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					get_resize_output_image_size(a							,	size=size["shortest_edge"]							,	default_to_square=a    )
													return resize(a							,	size=a							,	resample=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     List[Any]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[Any]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					get_size_dict(a    )
													return center_crop(a							,	size=(size["height"], size["width"])							,	data_format=a							,	**a    )

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

							def      A_  (      self				:     Union[str, Any]							,	a				:     np.ndarray							,	a				:     Union[float, List[float]]							,	a				:     Union[float, List[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Union[str, Any]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Tuple							,	a				:     ImageInput							,	a				:     Optional[bool] = None							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = None							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[float] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**a				:     Any							,	)	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[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__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					make_list_of_images(a    )

													if not valid_images(a    ):
																			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."    )

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE__     :				List[str]   =					[to_numpy_array(a    ) for image in images]

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

													if do_center_crop:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[self.center_crop(image=a							,	size=a    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[self.rescale(image=a							,	scale=a    ) for image in images]

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[self.normalize(image=a							,	mean=a							,	std=a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Dict   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{"pixel_values": images}
													return BatchFeature(data=a							,	tensor_type=a    )