
import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A__      :	List[str]          =    {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}



def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     return torch.atana(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		) / math.pi * 2



def 							a__ (				lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						torch.sin(t * math.pi / 2		) ** 2
     UpperCAmelCase__							: int 							=						(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     pass







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase  )->					Optional[Any]:
          super().__init__()

          UpperCAmelCase__							: List[Any] 							=						DiffusionAttnUnetaD(__UpperCamelCase ,			n_attn_layers=4  )
          UpperCAmelCase__							: Any 							=						deepcopy(self.diffusion  )
          UpperCAmelCase__							: Optional[int] 							=						torch.quasirandom.SobolEngine(1 ,			scramble=__UpperCamelCase  )





def 							a__ (				lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[Any] 							=						MODELS_MAP[model_name]["url"]
     os.system(F"wget {url} ./"		)

     return F"./{model_name}.ckpt"


A__      :	Tuple          =    {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
A__      :	str          =    {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
A__      :	int          =    {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
A__      :	List[Any]          =    {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

A__      :	Any          =    {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

A__      :	List[str]          =    {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}



def 							a__ (				lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''



     if name.startswith("skip"		):
          return name.replace("skip"       ,    RES_CONV_MAP["skip"]		)

     # name has to be of format main.{digit}
     if not name.startswith("main."		):
          raise ValueError(F"ResConvBlock error with {name}"		)

     return name.replace(name[:6]       ,    RES_CONV_MAP[name[:6]]		)



def 							a__ (				lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''



     for key, value in ATTN_MAP.items():
          if name.startswith(_SCREAMING_SNAKE_CASE		) and not isinstance(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		):
               return name.replace(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		)
          elif name.startswith(_SCREAMING_SNAKE_CASE		):
               return [name.replace(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		) for v in value]
     raise ValueError(F"Attn error with {name}"		)



def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       List[str]=13		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						input_string

     if string.split("."		)[0] == "timestep_embed":
          return string.replace("timestep_embed"       ,    "time_proj"		)

     UpperCAmelCase__							: Optional[Any] 							=						0
     if string.startswith("net.3."		):
          depth += 1
          UpperCAmelCase__							: Union[str, Any] 							=						string[6:]
     elif string.startswith("net."		):
          UpperCAmelCase__							: List[Any] 							=						string[4:]

     while string.startswith("main.7."		):
          depth += 1
          UpperCAmelCase__							: Any 							=						string[7:]

     if string.startswith("main."		):
          UpperCAmelCase__							: Optional[Any] 							=						string[5:]

     # mid block
     if string[:2].isdigit():
          UpperCAmelCase__							: Optional[int] 							=						string[:2]
          UpperCAmelCase__							: str 							=						string[2:]
     else:
          UpperCAmelCase__							: int 							=						string[0]
          UpperCAmelCase__							: Dict 							=						string[1:]

     if depth == max_depth:
          UpperCAmelCase__							: List[Any] 							=						MID_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__							: List[str] 							=						"mid_block"
     elif depth > 0 and int(_SCREAMING_SNAKE_CASE		) < 7:
          UpperCAmelCase__							: str 							=						DOWN_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__							: List[str] 							=						F"down_blocks.{depth}"
     elif depth > 0 and int(_SCREAMING_SNAKE_CASE		) > 7:
          UpperCAmelCase__							: Union[str, Any] 							=						UP_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__							: Union[str, Any] 							=						F"up_blocks.{max_depth - depth - 1}"
     elif depth == 0:
          UpperCAmelCase__							: Optional[Any] 							=						DEPTH_0_TO_LAYER[layer_num]
          UpperCAmelCase__							: int 							=						F"up_blocks.{max_depth - 1}" if int(_SCREAMING_SNAKE_CASE		) > 3 else "down_blocks.0"

     if not string_left.startswith("."		):
          raise ValueError(F"Naming error with {input_string} and string_left: {string_left}."		)

     UpperCAmelCase__							: Union[str, Any] 							=						string_left[1:]

     if "resnets" in new_layer:
          UpperCAmelCase__							: Optional[Any] 							=						convert_resconv_naming(_SCREAMING_SNAKE_CASE		)
     elif "attentions" in new_layer:
          UpperCAmelCase__							: Dict 							=						convert_attn_naming(_SCREAMING_SNAKE_CASE		)
          UpperCAmelCase__							: Any 							=						new_string_left

     if not isinstance(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		):
          UpperCAmelCase__							: List[Any] 							=						prefix + "." + new_layer + "." + string_left
     else:
          UpperCAmelCase__							: Optional[Any] 							=						[prefix + "." + new_layer + "." + s for s in string_left]
     return new_string



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						{}
     for k, v in state_dict.items():
          if k.endswith("kernel"		):
               # up- and downsample layers, don't have trainable weights
               continue

          UpperCAmelCase__							: Any 							=						rename(_SCREAMING_SNAKE_CASE		)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		):
               UpperCAmelCase__							: Union[str, Any] 							=						transform_conv_attns(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE		)
          else:
               UpperCAmelCase__							: Dict 							=						v

     return new_state_dict



def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Tuple		):






     '''simple docstring'''



     if len(_SCREAMING_SNAKE_CASE		) == 1:
          if len(v.shape		) == 3:
               # weight
               UpperCAmelCase__							: Any 							=						v[:, :, 0]
          else:
               # bias
               UpperCAmelCase__							: Any 							=						v
     else:
          # qkv matrices
          UpperCAmelCase__							: Any 							=						v.shape[0]
          UpperCAmelCase__							: Tuple 							=						trippled_shape // 3
          for i in range(3		):
               if len(v.shape		) == 3:
                    UpperCAmelCase__							: Optional[int] 							=						v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    UpperCAmelCase__							: str 							=						v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict



def 							a__ (				lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						torch.device("cuda" if torch.cuda.is_available() else "cpu"		)

     UpperCAmelCase__							: Optional[Any] 							=						args.model_path.split("/"		)[-1].split("."		)[0]
     if not os.path.isfile(args.model_path		):
          assert (
              model_name == args.model_path
          ), F"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
          UpperCAmelCase__							: str 							=						download(_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: List[Any] 							=						MODELS_MAP[model_name]["sample_rate"]
     UpperCAmelCase__							: Dict 							=						MODELS_MAP[model_name]["sample_size"]

     UpperCAmelCase__							: int 							=						Object()
     UpperCAmelCase__							: List[Any] 							=						sample_size
     UpperCAmelCase__							: List[str] 							=						sample_rate
     UpperCAmelCase__							: Optional[int] 							=						0

     UpperCAmelCase__							: Tuple 							=						UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE       ,    sample_rate=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase__							: Tuple 							=						diffusers_model.state_dict()

     UpperCAmelCase__							: Dict 							=						DiffusionUncond(_SCREAMING_SNAKE_CASE		)
     orig_model.load_state_dict(torch.load(args.model_path       ,    map_location=_SCREAMING_SNAKE_CASE		)["state_dict"]		)
     UpperCAmelCase__							: Tuple 							=						orig_model.diffusion_ema.eval()
     UpperCAmelCase__							: Optional[int] 							=						orig_model.state_dict()
     UpperCAmelCase__							: Optional[Any] 							=						rename_orig_weights(_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: List[str] 							=						set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
     UpperCAmelCase__							: Union[str, Any] 							=						set(diffusers_state_dict.keys()		) - set(renamed_state_dict.keys()		)

     assert len(_SCREAMING_SNAKE_CASE		) == 0, F"Problem with {renamed_minus_diffusers}"
     assert all(k.endswith("kernel"		) for k in list(_SCREAMING_SNAKE_CASE		)		), F"Problem with {diffusers_minus_renamed}"

     for key, value in renamed_state_dict.items():
          assert (
              diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
          ), F"Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
          if key == "time_proj.weight":
               UpperCAmelCase__							: Optional[int] 							=						value.squeeze()

          UpperCAmelCase__							: str 							=						value

     diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: List[Any] 							=						100
     UpperCAmelCase__							: List[str] 							=						33

     UpperCAmelCase__							: Any 							=						IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: Dict 							=						torch.manual_seed(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase__							: Optional[int] 							=						torch.randn([1, 2, config.sample_size]       ,    generator=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: Optional[int] 							=						torch.linspace(1       ,    0       ,    steps + 1       ,    device=_SCREAMING_SNAKE_CASE		)[:-1]
     UpperCAmelCase__							: Dict 							=						get_crash_schedule(_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: Optional[Any] 							=						DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE       ,    scheduler=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase__							: str 							=						torch.manual_seed(33		)
     UpperCAmelCase__							: str 							=						pipe(num_inference_steps=_SCREAMING_SNAKE_CASE       ,    generator=_SCREAMING_SNAKE_CASE		).audios

     UpperCAmelCase__							: Optional[int] 							=						sampling.iplms_sample(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    {}		)
     UpperCAmelCase__							: Any 							=						generated.clamp(-1       ,    1		)

     UpperCAmelCase__							: List[str] 							=						(generated - audio).abs().sum()
     UpperCAmelCase__							: Optional[int] 							=						(generated - audio).abs().max()

     if args.save:
          pipe.save_pretrained(args.checkpoint_path		)

     print("Diff sum"       ,    _SCREAMING_SNAKE_CASE		)
     print("Diff max"       ,    _SCREAMING_SNAKE_CASE		)

     assert diff_max < 1E-3, F"Diff max: {diff_max} is too much :-/"

     print(F"Conversion for {model_name} successful!"		)


if __name__ == "__main__":
    A__      :	Dict          =    argparse.ArgumentParser()

    parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
    parser.add_argument(
        """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
    )
    parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__      :	str          =    parser.parse_args()

    main(args)





"""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  )



