
"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


A__      :	Any          =    {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

A__      :	List[str]          =    {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

A__      :	Optional[int]          =    {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

A__      :	Dict          =    {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

A__      :	Dict          =    {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

A__      :	Optional[int]          =    {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}



def 							a__ (				lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError("boolean value expected"		)



def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Tuple=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						checkpoint[F"{old_prefix}.in_layers.0.weight"]
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint[F"{old_prefix}.in_layers.0.bias"]
     UpperCAmelCase__							: str 							=						checkpoint[F"{old_prefix}.in_layers.2.weight"]
     UpperCAmelCase__							: int 							=						checkpoint[F"{old_prefix}.in_layers.2.bias"]
     UpperCAmelCase__							: Optional[int] 							=						checkpoint[F"{old_prefix}.emb_layers.1.weight"]
     UpperCAmelCase__							: List[str] 							=						checkpoint[F"{old_prefix}.emb_layers.1.bias"]
     UpperCAmelCase__							: Dict 							=						checkpoint[F"{old_prefix}.out_layers.0.weight"]
     UpperCAmelCase__							: Tuple 							=						checkpoint[F"{old_prefix}.out_layers.0.bias"]
     UpperCAmelCase__							: str 							=						checkpoint[F"{old_prefix}.out_layers.3.weight"]
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint[F"{old_prefix}.out_layers.3.bias"]

     if has_skip:
          UpperCAmelCase__							: int 							=						checkpoint[F"{old_prefix}.skip_connection.weight"]
          UpperCAmelCase__							: str 							=						checkpoint[F"{old_prefix}.skip_connection.bias"]

     return new_checkpoint



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						checkpoint[F"{old_prefix}.qkv.weight"].chunk(3       ,    dim=0		)
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						checkpoint[F"{old_prefix}.qkv.bias"].chunk(3       ,    dim=0		)

     UpperCAmelCase__							: int 							=						checkpoint[F"{old_prefix}.norm.weight"]
     UpperCAmelCase__							: Any 							=						checkpoint[F"{old_prefix}.norm.bias"]

     UpperCAmelCase__							: int 							=						weight_q.squeeze(-1		).squeeze(-1		)
     UpperCAmelCase__							: Union[str, Any] 							=						bias_q.squeeze(-1		).squeeze(-1		)
     UpperCAmelCase__							: Union[str, Any] 							=						weight_k.squeeze(-1		).squeeze(-1		)
     UpperCAmelCase__							: Optional[Any] 							=						bias_k.squeeze(-1		).squeeze(-1		)
     UpperCAmelCase__							: int 							=						weight_v.squeeze(-1		).squeeze(-1		)
     UpperCAmelCase__							: Union[str, Any] 							=						bias_v.squeeze(-1		).squeeze(-1		)

     UpperCAmelCase__							: int 							=						(
         checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1		).squeeze(-1		)
     )
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1		).squeeze(-1		)

     return new_checkpoint



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						torch.load(lowerCAmelCase       ,    map_location="cpu"		)
     UpperCAmelCase__							: Optional[Any] 							=						{}

     UpperCAmelCase__							: Optional[int] 							=						checkpoint["time_embed.0.weight"]
     UpperCAmelCase__							: str 							=						checkpoint["time_embed.0.bias"]
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint["time_embed.2.weight"]
     UpperCAmelCase__							: int 							=						checkpoint["time_embed.2.bias"]

     if unet_config["num_class_embeds"] is not None:
          UpperCAmelCase__							: str 							=						checkpoint["label_emb.weight"]

     UpperCAmelCase__							: List[str] 							=						checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase__							: int 							=						checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase__							: Any 							=						unet_config["down_block_types"]
     UpperCAmelCase__							: List[Any] 							=						unet_config["layers_per_block"]
     UpperCAmelCase__							: str 							=						unet_config["attention_head_dim"]
     UpperCAmelCase__							: List[str] 							=						unet_config["block_out_channels"]
     UpperCAmelCase__							: Optional[Any] 							=						1
     UpperCAmelCase__							: List[Any] 							=						channels_list[0]

     for i, layer_type in enumerate(lowerCAmelCase		):
          UpperCAmelCase__							: Optional[Any] 							=						channels_list[i]
          UpperCAmelCase__							: Tuple 							=						current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(lowerCAmelCase		):
                    UpperCAmelCase__							: str 							=						F"down_blocks.{i}.resnets.{j}"
                    UpperCAmelCase__							: List[str] 							=						F"input_blocks.{current_layer}.0"
                    UpperCAmelCase__							: Tuple 							=						True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__							: int 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    has_skip=lowerCAmelCase		)
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(lowerCAmelCase		):
                    UpperCAmelCase__							: str 							=						F"down_blocks.{i}.resnets.{j}"
                    UpperCAmelCase__							: List[str] 							=						F"input_blocks.{current_layer}.0"
                    UpperCAmelCase__							: Optional[int] 							=						True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__							: Any 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    has_skip=lowerCAmelCase		)
                    UpperCAmelCase__							: Optional[Any] 							=						F"down_blocks.{i}.attentions.{j}"
                    UpperCAmelCase__							: int 							=						F"input_blocks.{current_layer}.1"
                    UpperCAmelCase__							: Any 							=						convert_attention(
                        lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
                    current_layer += 1

          if i != len(lowerCAmelCase		) - 1:
               UpperCAmelCase__							: Dict 							=						F"down_blocks.{i}.downsamplers.0"
               UpperCAmelCase__							: Any 							=						F"input_blocks.{current_layer}.0"
               UpperCAmelCase__							: str 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
               current_layer += 1

          UpperCAmelCase__							: Union[str, Any] 							=						current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase__							: Dict 							=						"mid_block.resnets.0"
     UpperCAmelCase__							: Any 							=						"middle_block.0"
     UpperCAmelCase__							: str 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
     UpperCAmelCase__							: List[str] 							=						"mid_block.attentions.0"
     UpperCAmelCase__							: List[str] 							=						"middle_block.1"
     UpperCAmelCase__							: Optional[int] 							=						convert_attention(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						"mid_block.resnets.1"
     UpperCAmelCase__							: Optional[int] 							=						"middle_block.2"
     UpperCAmelCase__							: Optional[int] 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     UpperCAmelCase__							: str 							=						0
     UpperCAmelCase__							: Tuple 							=						unet_config["up_block_types"]

     for i, layer_type in enumerate(lowerCAmelCase		):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1		):
                    UpperCAmelCase__							: int 							=						F"up_blocks.{i}.resnets.{j}"
                    UpperCAmelCase__							: int 							=						F"output_blocks.{current_layer}.0"
                    UpperCAmelCase__							: Optional[Any] 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    has_skip=lowerCAmelCase		)
                    current_layer += 1

               if i != len(lowerCAmelCase		) - 1:
                    UpperCAmelCase__							: str 							=						F"up_blocks.{i}.upsamplers.0"
                    UpperCAmelCase__							: Any 							=						F"output_blocks.{current_layer-1}.1"
                    UpperCAmelCase__							: Optional[Any] 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1		):
                    UpperCAmelCase__							: Union[str, Any] 							=						F"up_blocks.{i}.resnets.{j}"
                    UpperCAmelCase__							: Tuple 							=						F"output_blocks.{current_layer}.0"
                    UpperCAmelCase__							: Optional[Any] 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    has_skip=lowerCAmelCase		)
                    UpperCAmelCase__							: List[str] 							=						F"up_blocks.{i}.attentions.{j}"
                    UpperCAmelCase__							: Dict 							=						F"output_blocks.{current_layer}.1"
                    UpperCAmelCase__							: int 							=						convert_attention(
                        lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
                    current_layer += 1

               if i != len(lowerCAmelCase		) - 1:
                    UpperCAmelCase__							: int 							=						F"up_blocks.{i}.upsamplers.0"
                    UpperCAmelCase__							: Tuple 							=						F"output_blocks.{current_layer-1}.2"
                    UpperCAmelCase__							: int 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Union[str, Any] 							=						checkpoint["out.0.weight"]
     UpperCAmelCase__							: Optional[int] 							=						checkpoint["out.0.bias"]
     UpperCAmelCase__							: Any 							=						checkpoint["out.2.weight"]
     UpperCAmelCase__							: str 							=						checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
    A__      :	Union[str, Any]          =    argparse.ArgumentParser()

    parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
    parser.add_argument(
        """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
    )
    parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

    A__      :	List[str]          =    parser.parse_args()
    A__      :	Union[str, Any]          =    strabool(args.class_cond)

    A__      :	List[str]          =    os.path.basename(args.unet_path)
    print(f"""Checkpoint: {ckpt_name}""")

    # Get U-Net config
    if "imagenet64" in ckpt_name:
        A__      :	Optional[Any]          =    IMAGENET_64_UNET_CONFIG
    elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
        A__      :	Any          =    LSUN_256_UNET_CONFIG
    elif "test" in ckpt_name:
        A__      :	str          =    TEST_UNET_CONFIG
    else:
        raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

    if not args.class_cond:
        A__      :	Optional[int]          =    None

    A__      :	List[Any]          =    con_pt_to_diffuser(args.unet_path, unet_config)

    A__      :	str          =    UNetaDModel(**unet_config)
    image_unet.load_state_dict(converted_unet_ckpt)

    # Get scheduler config
    if "cd" in ckpt_name or "test" in ckpt_name:
        A__      :	Optional[Any]          =    CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        A__      :	List[Any]          =    CT_IMAGENET_64_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
        A__      :	Any          =    CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

    A__      :	List[Any]          =    CMStochasticIterativeScheduler(**scheduler_config)

    A__      :	Any          =    ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)





"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
    import PIL

A__      :	Dict          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    (list, tuple)		) and isinstance(videos[0]       ,    (list, tuple)		) and is_valid_image(videos[0][0]		):
          return videos

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

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

     raise ValueError(F"Could not make batched video from {videos}"		)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['pixel_values']



     def __init__(      self ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = 1 / 2_55 ,			__UpperCamelCase = True ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          super().__init__(**__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						size if size is not None else {"shortest_edge": 2_56}
          UpperCAmelCase__							: List[Any] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCAmelCase__							: int 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          if "shortest_edge" in size:
               UpperCAmelCase__							: Union[str, Any] 							=						get_resize_output_image_size(__UpperCamelCase ,			size["shortest_edge"] ,			default_to_square=__UpperCamelCase  )
          elif "height" in size and "width" in size:
               UpperCAmelCase__							: Any 							=						(size["height"], size["width"])
          else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
          return resize(__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[Any] 							=						get_size_dict(__UpperCamelCase  )
          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(__UpperCamelCase ,			size=(size["height"], size["width"]) ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					Tuple:
          UpperCAmelCase__							: str 							=						image.astype(np.floataa  )
          if offset:
               UpperCAmelCase__							: Tuple 							=						image - (scale / 2)
          return rescale(__UpperCamelCase ,			scale=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = 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."  )

          if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."  )

          # All transformations expect numpy arrays.
          UpperCAmelCase__							: Optional[Any] 							=						to_numpy_array(__UpperCamelCase  )

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

          if do_center_crop:
               UpperCAmelCase__							: int 							=						self.center_crop(__UpperCamelCase ,			size=__UpperCamelCase  )

          if do_rescale:
               UpperCAmelCase__							: List[str] 							=						self.rescale(image=__UpperCamelCase ,			scale=__UpperCamelCase ,			offset=__UpperCamelCase  )

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

          UpperCAmelCase__							: Dict 							=						to_channel_dimension_format(__UpperCamelCase ,			__UpperCamelCase  )
          return image



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			**__UpperCamelCase ,			)->					PIL.Image.Image:
          UpperCAmelCase__							: Optional[int] 							=						do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__							: int 							=						resample if resample is not None else self.resample
          UpperCAmelCase__							: Tuple 							=						do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__							: int 							=						do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__							: Any 							=						rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__							: Optional[int] 							=						offset if offset is not None else self.offset
          UpperCAmelCase__							: Dict 							=						do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__							: Dict 							=						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(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__							: Tuple 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

          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."  )

          UpperCAmelCase__							: List[str] 							=						make_batched(__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						[
              [
                  self._preprocess_image(
                      image=__UpperCamelCase ,			do_resize=__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			do_center_crop=__UpperCamelCase ,			crop_size=__UpperCamelCase ,			do_rescale=__UpperCamelCase ,			rescale_factor=__UpperCamelCase ,			offset=__UpperCamelCase ,			do_normalize=__UpperCamelCase ,			image_mean=__UpperCamelCase ,			image_std=__UpperCamelCase ,			data_format=__UpperCamelCase ,			)
                  for img in video
              ]
              for video in videos
          ]

          UpperCAmelCase__							: Dict 							=						{"pixel_values": videos}
          return BatchFeature(data=__UpperCamelCase ,			tensor_type=__UpperCamelCase  )



