

"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A		     =    logging.get_logger(__name__)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=False      ,				__SCREAMING_SNAKE_CASE=False				)			->			List[Any]:
		__lowerCAmelCase: Union[str, Any]					   =    "backbone." if is_semantic else ""

		__lowerCAmelCase: List[Any]					   =    []
		for i in range(config.num_hidden_layers				):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")				)
				rename_keys.append(
				    (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")				)
				rename_keys.append(
				    (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")				)
				rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")				)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (F"{prefix}cls_token", "beit.embeddings.cls_token"),
		        (F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
		        (F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
		    ]				)

		if has_lm_head:
				# mask token + shared relative position bias + layernorm
				rename_keys.extend(
				    [
				        ("mask_token", "beit.embeddings.mask_token"),
				        (
				            "rel_pos_bias.relative_position_bias_table",
				            "beit.encoder.relative_position_bias.relative_position_bias_table",
				        ),
				        (
				            "rel_pos_bias.relative_position_index",
				            "beit.encoder.relative_position_bias.relative_position_index",
				        ),
				        ("norm.weight", "layernorm.weight"),
				        ("norm.bias", "layernorm.bias"),
				    ]				)
		elif is_semantic:
				# semantic segmentation classification heads
				rename_keys.extend(
				    [
				        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
				        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
				        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
				        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
				    ]				)
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
				        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
				        ("head.weight", "classifier.weight"),
				        ("head.bias", "classifier.bias"),
				    ]				)

		return rename_keys






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=False      ,				__SCREAMING_SNAKE_CASE=False				)			->			Any:
		for i in range(config.num_hidden_layers				):
				__lowerCAmelCase: List[str]					   =    "backbone." if is_semantic else ""
				# queries, keys and values
				__lowerCAmelCase: int					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"				)
				__lowerCAmelCase: List[str]					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"				)
				__lowerCAmelCase: Optional[Any]					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"				)

				__lowerCAmelCase: List[str]					   =    in_proj_weight[
				    : config.hidden_size, :
				]
				__lowerCAmelCase: Optional[Any]					   =    q_bias
				__lowerCAmelCase: Union[str, Any]					   =    in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				__lowerCAmelCase: Tuple					   =    in_proj_weight[
				    -config.hidden_size :, :
				]
				__lowerCAmelCase: Optional[int]					   =    v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				__lowerCAmelCase: Optional[int]					   =    state_dict.pop(F"{prefix}blocks.{i}.gamma_1"				)
				__lowerCAmelCase: Any					   =    state_dict.pop(F"{prefix}blocks.{i}.gamma_2"				)

				__lowerCAmelCase: Optional[Any]					   =    gamma_a
				__lowerCAmelCase: Optional[int]					   =    gamma_a

				# relative_position bias table + index
				if not has_lm_head:
						# each layer has its own relative position bias
						__lowerCAmelCase: Tuple					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"				)
						__lowerCAmelCase: Dict					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"				)

						__lowerCAmelCase: Dict					   =    table
						__lowerCAmelCase: Any					   =    index






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Optional[int]:
		__lowerCAmelCase: Optional[int]					   =    dct.pop(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[Any]					   =    val






def 					a__					(					)			->			Optional[int]:
		__lowerCAmelCase: Union[str, Any]					   =    "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase: str					   =    Image.open(requests.get(__SCREAMING_SNAKE_CASE      ,				stream=__SCREAMING_SNAKE_CASE				).raw				)
		return im






@torch.no_grad()
def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
		__lowerCAmelCase: List[Any]					   =    BeitConfig()
		__lowerCAmelCase: Optional[int]					   =    False
		__lowerCAmelCase: str					   =    False
		__lowerCAmelCase: List[str]					   =    "huggingface/label-files"
		# set config parameters based on URL
		if checkpoint_url[-9:-4] == "pt22k":
				# masked image modeling
				__lowerCAmelCase: Optional[int]					   =    True
				__lowerCAmelCase: Tuple					   =    True
				__lowerCAmelCase: List[str]					   =    True
		elif checkpoint_url[-9:-4] == "ft22k":
				# intermediate fine-tuning on ImageNet-22k
				__lowerCAmelCase: Optional[Any]					   =    True
				__lowerCAmelCase: Any					   =    2_1_8_4_1
				__lowerCAmelCase: Any					   =    "imagenet-22k-id2label.json"
				__lowerCAmelCase: Any					   =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				repo_type="dataset"				)      ,				"r"				)				)
				__lowerCAmelCase: List[str]					   =    {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[9_2_0_5]
				del idalabel[1_5_0_2_7]
				__lowerCAmelCase: Optional[int]					   =    idalabel
				__lowerCAmelCase: str					   =    {v: k for k, v in idalabel.items()}
		elif checkpoint_url[-8:-4] == "to1k":
				# fine-tuning on ImageNet-1k
				__lowerCAmelCase: int					   =    True
				__lowerCAmelCase: List[Any]					   =    1_0_0_0
				__lowerCAmelCase: str					   =    "imagenet-1k-id2label.json"
				__lowerCAmelCase: Any					   =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				repo_type="dataset"				)      ,				"r"				)				)
				__lowerCAmelCase: Tuple					   =    {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				__lowerCAmelCase: Union[str, Any]					   =    idalabel
				__lowerCAmelCase: Optional[int]					   =    {v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						__lowerCAmelCase: Any					   =    3_8_4
				if "512" in checkpoint_url:
						__lowerCAmelCase: Optional[int]					   =    5_1_2
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				__lowerCAmelCase: str					   =    True
				__lowerCAmelCase: Optional[int]					   =    1_5_0
				__lowerCAmelCase: Optional[int]					   =    "ade20k-id2label.json"
				__lowerCAmelCase: Any					   =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				repo_type="dataset"				)      ,				"r"				)				)
				__lowerCAmelCase: Union[str, Any]					   =    {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				__lowerCAmelCase: List[Any]					   =    idalabel
				__lowerCAmelCase: Optional[Any]					   =    {v: k for k, v in idalabel.items()}
				__lowerCAmelCase: str					   =    6_4_0
				__lowerCAmelCase: Optional[Any]					   =    True
		else:
				raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"				)

		# size of the architecture
		if "base" in checkpoint_url:
				pass
		elif "large" in checkpoint_url:
				__lowerCAmelCase: Dict					   =    1_0_2_4
				__lowerCAmelCase: Union[str, Any]					   =    4_0_9_6
				__lowerCAmelCase: Dict					   =    2_4
				__lowerCAmelCase: Union[str, Any]					   =    1_6
				if "ade20k" in checkpoint_url:
						__lowerCAmelCase: int					   =    6_4_0
						__lowerCAmelCase: Optional[Any]					   =    [7, 1_1, 1_5, 2_3]
		else:
				raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

		# load state_dict of original model, remove and rename some keys
		__lowerCAmelCase: Optional[int]					   =    torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE      ,				map_location="cpu"      ,				check_hash=__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[Any]					   =    state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

		__lowerCAmelCase: List[Any]					   =    create_rename_keys(__SCREAMING_SNAKE_CASE      ,				has_lm_head=__SCREAMING_SNAKE_CASE      ,				is_semantic=__SCREAMING_SNAKE_CASE				)
		for src, dest in rename_keys:
				rename_key(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		read_in_q_k_v(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				has_lm_head=__SCREAMING_SNAKE_CASE      ,				is_semantic=__SCREAMING_SNAKE_CASE				)
		if is_semantic:
				# add prefix to decoder keys
				for key, val in state_dict.copy().items():
						__lowerCAmelCase: List[str]					   =    state_dict.pop(__SCREAMING_SNAKE_CASE				)
						if key.startswith("backbone.fpn"				):
								__lowerCAmelCase: Tuple					   =    key.replace("backbone.fpn"      ,				"fpn"				)
						__lowerCAmelCase: List[str]					   =    val

    # load HuggingFace model
		if checkpoint_url[-9:-4] == "pt22k":
				__lowerCAmelCase: str					   =    BeitForMaskedImageModeling(__SCREAMING_SNAKE_CASE				)
		elif "ade20k" in checkpoint_url:
				__lowerCAmelCase: Optional[int]					   =    BeitForSemanticSegmentation(__SCREAMING_SNAKE_CASE				)
		else:
				__lowerCAmelCase: Optional[int]					   =    BeitForImageClassification(__SCREAMING_SNAKE_CASE				)
		model.eval()
		model.load_state_dict(__SCREAMING_SNAKE_CASE				)

		# Check outputs on an image
		if is_semantic:
				__lowerCAmelCase: Union[str, Any]					   =    BeitImageProcessor(size=config.image_size      ,				do_center_crop=__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Any					   =    load_dataset("hf-internal-testing/fixtures_ade20k"      ,				split="test"				)
				__lowerCAmelCase: int					   =    Image.open(ds[0]["file"]				)
		else:
				__lowerCAmelCase: Tuple					   =    BeitImageProcessor(
				    size=config.image_size      ,				resample=PILImageResampling.BILINEAR      ,				do_center_crop=__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Union[str, Any]					   =    prepare_img()

		__lowerCAmelCase: Union[str, Any]					   =    image_processor(images=__SCREAMING_SNAKE_CASE      ,				return_tensors="pt"				)
		__lowerCAmelCase: Tuple					   =    encoding["pixel_values"]

		__lowerCAmelCase: str					   =    model(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Tuple					   =    outputs.logits

		# verify logits
		__lowerCAmelCase: Optional[Any]					   =    torch.Size([1, 1_0_0_0]				)
		if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"				):
				__lowerCAmelCase: Optional[Any]					   =    torch.Size([1, 1_9_6, 8_1_9_2]				)
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"				):
				__lowerCAmelCase: Optional[int]					   =    torch.Size([1, 1_9_6, 8_1_9_2]				)
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"				):
				__lowerCAmelCase: Dict					   =    torch.Size([1, 2_1_8_4_1]				)
				__lowerCAmelCase: Optional[int]					   =    torch.tensor([2.2288, 2.4671, 0.7395]				)
				__lowerCAmelCase: str					   =    2_3_9_7
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"				):
				__lowerCAmelCase: Optional[Any]					   =    torch.Size([1, 2_1_8_4_1]				)
				__lowerCAmelCase: Optional[Any]					   =    torch.tensor([1.6881, -0.2787, 0.5901]				)
				__lowerCAmelCase: Any					   =    2_3_9_6
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"				):
				__lowerCAmelCase: int					   =    torch.tensor([0.1241, 0.0798, -0.6569]				)
				__lowerCAmelCase: str					   =    2_8_5
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Optional[int]					   =    torch.tensor([-1.2385, -1.0987, -1.0108]				)
				__lowerCAmelCase: str					   =    2_8_1
		elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"				):
				__lowerCAmelCase: List[Any]					   =    torch.tensor([-1.5303, -0.9484, -0.3147]				)
				__lowerCAmelCase: Union[str, Any]					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"				):
				__lowerCAmelCase: Tuple					   =    torch.tensor([0.4610, -0.0928, 0.2086]				)
				__lowerCAmelCase: str					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Dict					   =    torch.tensor([-0.4804, 0.6257, -0.1837]				)
				__lowerCAmelCase: List[str]					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Union[str, Any]					   =    torch.tensor([[-0.5122, 0.5117, -0.2113]]				)
				__lowerCAmelCase: Any					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Union[str, Any]					   =    torch.tensor([-0.3062, 0.7261, 0.4852]				)
				__lowerCAmelCase: Optional[Any]					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"				):
				__lowerCAmelCase: Dict					   =    (1, 1_5_0, 1_6_0, 1_6_0)
				__lowerCAmelCase: Dict					   =    torch.tensor(
				    [
				        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
				        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
				        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
				    ]				)
		elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"				):
				__lowerCAmelCase: List[Any]					   =    (1, 1_5_0, 1_6_0, 1_6_0)
				__lowerCAmelCase: Any					   =    torch.tensor(
				    [
				        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
				        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
				        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
				    ]				)
		else:
				raise ValueError("Can't verify logits as model is not supported"				)

		assert logits.shape == expected_shape, "Shape of logits not as expected"
		if not has_lm_head:
				if is_semantic:
						assert torch.allclose(
						    logits[0, :3, :3, :3]      ,				__SCREAMING_SNAKE_CASE      ,				atol=1E-3				), "First elements of logits not as expected"
				else:
						print("Predicted class idx:"      ,				logits.argmax(-1				).item()				)
						assert torch.allclose(
						    logits[0, :3]      ,				__SCREAMING_SNAKE_CASE      ,				atol=1E-3				), "First elements of logits not as expected"
						assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

		Path(__SCREAMING_SNAKE_CASE				).mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)
		print(F"Saving model to {pytorch_dump_folder_path}"				)
		model.save_pretrained(__SCREAMING_SNAKE_CASE				)
		print(F"Saving image processor to {pytorch_dump_folder_path}"				)
		image_processor.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			__A		     =    argparse.ArgumentParser()

			parser.add_argument(
			    "--checkpoint_url",
			    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
			    type=str,
			    help="URL to the original PyTorch checkpoint (.pth file).",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
			)
			__A		     =    parser.parse_args()
			convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""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__)


