




import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


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







def lowerCamelCase__       (      _lowercase			,						_lowercase=False				):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      OrderedDict()
     for key, value in state_dict.items():
          if encoder_only and not key.startswith('''head'''				):
               UpperCAmelCase_      :							int       =      "segformer.encoder." + key
          if key.startswith('''backbone'''				):
               UpperCAmelCase_      :							Any       =      key.replace('''backbone'''			,						'''segformer.encoder'''				)
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               UpperCAmelCase_      :							Union[str, Any]       =      key[key.find('''patch_embed'''				) + len('''patch_embed'''				)]
               UpperCAmelCase_      :							List[str]       =      key.replace(f'''patch_embed{idx}'''			,						f'''patch_embeddings.{int(_lowercase				)-1}'''				)
          if "norm" in key:
               UpperCAmelCase_      :							str       =      key.replace('''norm'''			,						'''layer_norm'''				)
          if "segformer.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               UpperCAmelCase_      :							str       =      key[key.find('''segformer.encoder.layer_norm'''				) + len('''segformer.encoder.layer_norm'''				)]
               UpperCAmelCase_      :							List[str]       =      key.replace(f'''layer_norm{idx}'''			,						f'''layer_norm.{int(_lowercase				)-1}'''				)
          if "layer_norm1" in key:
               UpperCAmelCase_      :							Tuple       =      key.replace('''layer_norm1'''			,						'''layer_norm_1'''				)
          if "layer_norm2" in key:
               UpperCAmelCase_      :							Dict       =      key.replace('''layer_norm2'''			,						'''layer_norm_2'''				)
          if "block" in key:
               # replace for example block1 by block.0
               UpperCAmelCase_      :							Tuple       =      key[key.find('''block'''				) + len('''block'''				)]
               UpperCAmelCase_      :							Optional[int]       =      key.replace(f'''block{idx}'''			,						f'''block.{int(_lowercase				)-1}'''				)
          if "attn.q" in key:
               UpperCAmelCase_      :							Union[str, Any]       =      key.replace('''attn.q'''			,						'''attention.self.query'''				)
          if "attn.proj" in key:
               UpperCAmelCase_      :							Optional[int]       =      key.replace('''attn.proj'''			,						'''attention.output.dense'''				)
          if "attn" in key:
               UpperCAmelCase_      :							Any       =      key.replace('''attn'''			,						'''attention.self'''				)
          if "fc1" in key:
               UpperCAmelCase_      :							Dict       =      key.replace('''fc1'''			,						'''dense1'''				)
          if "fc2" in key:
               UpperCAmelCase_      :							Dict       =      key.replace('''fc2'''			,						'''dense2'''				)
          if "linear_pred" in key:
               UpperCAmelCase_      :							Optional[int]       =      key.replace('''linear_pred'''			,						'''classifier'''				)
          if "linear_fuse" in key:
               UpperCAmelCase_      :							int       =      key.replace('''linear_fuse.conv'''			,						'''linear_fuse'''				)
               UpperCAmelCase_      :							Any       =      key.replace('''linear_fuse.bn'''			,						'''batch_norm'''				)
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               UpperCAmelCase_      :							Tuple       =      key[key.find('''linear_c'''				) + len('''linear_c'''				)]
               UpperCAmelCase_      :							int       =      key.replace(f'''linear_c{idx}'''			,						f'''linear_c.{int(_lowercase				)-1}'''				)
          if key.startswith('''head'''				):
               UpperCAmelCase_      :							Optional[int]       =      key.replace('''head'''			,						'''classifier'''				)
          UpperCAmelCase_      :							List[Any]       =      value

     return new_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for i in range(config.num_encoder_blocks				):
          for j in range(config.depths[i]				):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               UpperCAmelCase_      :							Any       =      state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
               UpperCAmelCase_      :							List[str]       =      state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''				)
               # next, add keys and values (in that order) to the state dict
               UpperCAmelCase_      :							Optional[Any]       =      kv_weight[
                   : config.hidden_sizes[i], :
               ]
               UpperCAmelCase_      :							str       =      kv_bias[: config.hidden_sizes[i]]
               UpperCAmelCase_      :							str       =      kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               UpperCAmelCase_      :							Tuple       =      kv_bias[
                   config.hidden_sizes[i] :
               ]







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_      :							Dict       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)

     return image







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      SegformerConfig()
     UpperCAmelCase_      :							List[str]       =      False

     # set attributes based on model_name
     UpperCAmelCase_      :							Any       =      "huggingface/label-files"
     if "segformer" in model_name:
          UpperCAmelCase_      :							str       =      model_name[len('''segformer.'''				) : len('''segformer.'''				) + 2]
          if "ade" in model_name:
               UpperCAmelCase_      :							str       =      150
               UpperCAmelCase_      :							Optional[int]       =      "ade20k-id2label.json"
               UpperCAmelCase_      :							str       =      (1, 150, 128, 128)
          elif "city" in model_name:
               UpperCAmelCase_      :							int       =      19
               UpperCAmelCase_      :							Any       =      "cityscapes-id2label.json"
               UpperCAmelCase_      :							Tuple       =      (1, 19, 128, 128)
          else:
               raise ValueError(f'''Model {model_name} not supported'''				)
     elif "mit" in model_name:
          UpperCAmelCase_      :							Tuple       =      True
          UpperCAmelCase_      :							Union[str, Any]       =      model_name[4:6]
          UpperCAmelCase_      :							Optional[int]       =      1000
          UpperCAmelCase_      :							Dict       =      "imagenet-1k-id2label.json"
          UpperCAmelCase_      :							Optional[Any]       =      (1, 1000)
     else:
          raise ValueError(f'''Model {model_name} not supported'''				)

     # set config attributes
     UpperCAmelCase_      :							Any       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
     UpperCAmelCase_      :							int       =      {int(_lowercase				): v for k, v in idalabel.items()}
     UpperCAmelCase_      :							List[Any]       =      idalabel
     UpperCAmelCase_      :							List[str]       =      {v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          UpperCAmelCase_      :							Optional[Any]       =      [64, 128, 320, 512]
          UpperCAmelCase_      :							str       =      256
     elif size == "b2":
          UpperCAmelCase_      :							str       =      [64, 128, 320, 512]
          UpperCAmelCase_      :							int       =      768
          UpperCAmelCase_      :							List[Any]       =      [3, 4, 6, 3]
     elif size == "b3":
          UpperCAmelCase_      :							Tuple       =      [64, 128, 320, 512]
          UpperCAmelCase_      :							Any       =      768
          UpperCAmelCase_      :							List[Any]       =      [3, 4, 18, 3]
     elif size == "b4":
          UpperCAmelCase_      :							str       =      [64, 128, 320, 512]
          UpperCAmelCase_      :							Union[str, Any]       =      768
          UpperCAmelCase_      :							str       =      [3, 8, 27, 3]
     elif size == "b5":
          UpperCAmelCase_      :							Tuple       =      [64, 128, 320, 512]
          UpperCAmelCase_      :							Optional[int]       =      768
          UpperCAmelCase_      :							Any       =      [3, 6, 40, 3]
     else:
          raise ValueError(f'''Size {size} not supported'''				)

     # load image processor (only resize + normalize)
     UpperCAmelCase_      :							Union[str, Any]       =      SegformerImageProcessor(
         image_scale=(512, 512)			,						keep_ratio=_lowercase			,						align=_lowercase			,						do_random_crop=_lowercase				)

     # prepare image
     UpperCAmelCase_      :							List[str]       =      prepare_img()
     UpperCAmelCase_      :							Dict       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				).pixel_values

     logger.info(f'''Converting model {model_name}...'''				)

     # load original state dict
     if encoder_only:
          UpperCAmelCase_      :							Optional[int]       =      torch.load(_lowercase			,						map_location=torch.device('''cpu'''				)				)
     else:
          UpperCAmelCase_      :							Any       =      torch.load(_lowercase			,						map_location=torch.device('''cpu'''				)				)["state_dict"]

     # rename keys
     UpperCAmelCase_      :							List[Any]       =      rename_keys(_lowercase			,						encoder_only=_lowercase				)
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(_lowercase			,						_lowercase				)

     # create HuggingFace model and load state dict
     if encoder_only:
          UpperCAmelCase_      :							List[Any]       =      False
          UpperCAmelCase_      :							Optional[int]       =      SegformerForImageClassification(_lowercase				)
     else:
          UpperCAmelCase_      :							List[str]       =      SegformerForSemanticSegmentation(_lowercase				)
     model.load_state_dict(_lowercase				)
     model.eval()

     # forward pass
     UpperCAmelCase_      :							Optional[int]       =      model(_lowercase				)
     UpperCAmelCase_      :							List[Any]       =      outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          UpperCAmelCase_      :							List[Any]       =      torch.tensor(
              [
                  [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                  [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                  [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
              ]				)
     elif model_name == "segformer.b1.512x512.ade.160k":
          UpperCAmelCase_      :							Any       =      torch.tensor(
              [
                  [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                  [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                  [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
              ]				)
     elif model_name == "segformer.b2.512x512.ade.160k":
          UpperCAmelCase_      :							List[str]       =      torch.tensor(
              [
                  [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                  [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                  [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
              ]				)
     elif model_name == "segformer.b3.512x512.ade.160k":
          UpperCAmelCase_      :							Tuple       =      torch.tensor(
              [
                  [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                  [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                  [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
              ]				)
     elif model_name == "segformer.b4.512x512.ade.160k":
          UpperCAmelCase_      :							Dict       =      torch.tensor(
              [
                  [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                  [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                  [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
              ]				)
     elif model_name == "segformer.b5.640x640.ade.160k":
          UpperCAmelCase_      :							Tuple       =      torch.tensor(
              [
                  [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                  [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                  [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
              ]				)
     # Cityscapes checkpoints
     elif model_name == "segformer.b0.1024x1024.city.160k":
          UpperCAmelCase_      :							Optional[int]       =      torch.tensor(
              [
                  [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                  [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                  [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
              ]				)
     elif model_name == "segformer.b0.512x1024.city.160k":
          UpperCAmelCase_      :							List[Any]       =      torch.tensor(
              [
                  [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                  [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                  [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
              ]				)
     elif model_name == "segformer.b0.640x1280.city.160k":
          UpperCAmelCase_      :							Optional[Any]       =      torch.tensor(
              [
                  [
                      [-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
                      [-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
                      [-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
                  ],
                  [
                      [-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
                      [-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
                      [-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
                  ],
                  [
                      [7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
                      [4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
                      [3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
                  ],
              ]				)
     elif model_name == "segformer.b0.768x768.city.160k":
          UpperCAmelCase_      :							Union[str, Any]       =      torch.tensor(
              [
                  [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                  [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                  [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
              ]				)
     elif model_name == "segformer.b1.1024x1024.city.160k":
          UpperCAmelCase_      :							Union[str, Any]       =      torch.tensor(
              [
                  [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                  [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                  [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
              ]				)
     elif model_name == "segformer.b2.1024x1024.city.160k":
          UpperCAmelCase_      :							Dict       =      torch.tensor(
              [
                  [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                  [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                  [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
              ]				)
     elif model_name == "segformer.b3.1024x1024.city.160k":
          UpperCAmelCase_      :							Optional[Any]       =      torch.tensor(
              [
                  [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                  [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                  [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
              ]				)
     elif model_name == "segformer.b4.1024x1024.city.160k":
          UpperCAmelCase_      :							List[Any]       =      torch.tensor(
              [
                  [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                  [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                  [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
              ]				)
     elif model_name == "segformer.b5.1024x1024.city.160k":
          UpperCAmelCase_      :							Dict       =      torch.tensor(
              [
                  [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                  [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                  [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
              ]				)
     else:
          UpperCAmelCase_      :							Optional[int]       =      logits.argmax(-1				).item()
          print('''Predicted class:'''			,						model.config.idalabel[predicted_class_idx]				)

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]			,						_lowercase			,						atol=1E-2				)

     # finally, save model and image processor
     logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
     Path(_lowercase				).mkdir(exist_ok=_lowercase				)
     model.save_pretrained(_lowercase				)
     image_processor.save_pretrained(_lowercase				)


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

       parser.add_argument(
           '--model_name',
           default='segformer.b0.512x512.ade.160k',
           type=str,
           help='Name of the model you\'d like to convert.',
       )
       parser.add_argument(
           '--checkpoint_path', default=None, type=str, help='Path 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_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if isinstance(_lowercase			,						(list, tuple)				) and isinstance(videos[0]			,						(list, tuple)				) and is_valid_image(videos[0][0]				):
          return videos

     elif isinstance(_lowercase			,						(list, tuple)				) and is_valid_image(videos[0]				):
          return [videos]

     elif is_valid_image(_lowercase				):
          return [[videos]]

     raise ValueError(f'''Could not make batched video from {videos}'''				)



class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''pixel_values''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = 1 / 255       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		None:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_      :							List[str]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         UpperCAmelCase_      :							str       =      do_resize
         UpperCAmelCase_      :							Union[str, Any]       =      size
         UpperCAmelCase_      :							int       =      do_center_crop
         UpperCAmelCase_      :							List[str]       =      crop_size
         UpperCAmelCase_      :							Optional[int]       =      resample
         UpperCAmelCase_      :							List[Any]       =      do_rescale
         UpperCAmelCase_      :							Tuple       =      rescale_factor
         UpperCAmelCase_      :							Optional[Any]       =      do_normalize
         UpperCAmelCase_      :							int       =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         if "shortest_edge" in size:
              UpperCAmelCase_      :							Dict       =      get_resize_output_image_size(_SCREAMING_SNAKE_CASE       ,size['''shortest_edge''']       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         elif "height" in size and "width" in size:
              UpperCAmelCase_      :							Tuple       =      (size['''height'''], size['''width'''])
         else:
              raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
         return resize(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							str       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
         return center_crop(_SCREAMING_SNAKE_CASE       ,size=(size['''height'''], size['''width'''])       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         return rescale(_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         return normalize(_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,)			->		np.ndarray:
         if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

         if do_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.
         UpperCAmelCase_      :							Any       =      to_numpy_array(_SCREAMING_SNAKE_CASE  )

         if do_resize:
              UpperCAmelCase_      :							Union[str, Any]       =      self.resize(image=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE  )

         if do_center_crop:
              UpperCAmelCase_      :							Optional[int]       =      self.center_crop(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE  )

         if do_rescale:
              UpperCAmelCase_      :							str       =      self.rescale(image=_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE  )

         if do_normalize:
              UpperCAmelCase_      :							List[Any]       =      self.normalize(image=_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return image





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,**_SCREAMING_SNAKE_CASE       ,)			->		PIL.Image.Image:
         UpperCAmelCase_      :							Dict       =      do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_      :							int       =      resample if resample is not None else self.resample
         UpperCAmelCase_      :							List[str]       =      do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_      :							Optional[Any]       =      do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_      :							List[Any]       =      rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_      :							Tuple       =      do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_      :							Optional[int]       =      image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else self.image_std

         UpperCAmelCase_      :							List[str]       =      size if size is not None else self.size
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         if not valid_images(_SCREAMING_SNAKE_CASE  ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

         UpperCAmelCase_      :							List[Any]       =      make_batched(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      [
             [
                 self._preprocess_image(
                     image=_SCREAMING_SNAKE_CASE       ,do_resize=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,do_center_crop=_SCREAMING_SNAKE_CASE       ,crop_size=_SCREAMING_SNAKE_CASE       ,do_rescale=_SCREAMING_SNAKE_CASE       ,rescale_factor=_SCREAMING_SNAKE_CASE       ,do_normalize=_SCREAMING_SNAKE_CASE       ,image_mean=_SCREAMING_SNAKE_CASE       ,image_std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,)
                 for img in video
             ]
             for video in videos
         ]

         UpperCAmelCase_      :							Any       =      {'''pixel_values''': videos}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )