
"""simple docstring"""




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
A__      :	Optional[Any]          =    logging.get_logger(__name__)


A__      :	Dict          =    """https://openaipublic.azureedge.net/jukebox/models/"""
A__      :	Any          =    {
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}



def 							a__ (				lowerCAmelCase	:       Any		):






     '''simple docstring'''
     if key.endswith(".model.1.bias"		) and len(key.split("."		)		) > 10:
          UpperCAmelCase__							: Union[str, Any] 							=						key.replace(".model.1.bias"       ,    ".conv1d_1.bias"		)
     elif key.endswith(".model.1.weight"		) and len(key.split("."		)		) > 10:
          UpperCAmelCase__							: Optional[int] 							=						key.replace(".model.1.weight"       ,    ".conv1d_1.weight"		)
     elif key.endswith(".model.3.bias"		) and len(key.split("."		)		) > 10:
          UpperCAmelCase__							: Dict 							=						key.replace(".model.3.bias"       ,    ".conv1d_2.bias"		)
     elif key.endswith(".model.3.weight"		) and len(key.split("."		)		) > 10:
          UpperCAmelCase__							: List[Any] 							=						key.replace(".model.3.weight"       ,    ".conv1d_2.weight"		)

     if "conditioner_blocks.0." in key:
          UpperCAmelCase__							: Union[str, Any] 							=						key.replace("conditioner_blocks.0"       ,    "conditioner_blocks"		)

     if "prime_prior" in key:
          UpperCAmelCase__							: Tuple 							=						key.replace("prime_prior"       ,    "encoder"		)

     if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
          UpperCAmelCase__							: Optional[Any] 							=						key.replace(".emb."       ,    "."		)

     if key.endswith("k"		):  # replace vqvae.X.k with vqvae.X.codebook
          return key.replace(".k"       ,    ".codebook"		)
     if "y_emb." in key:
          return key.replace("y_emb."       ,    "metadata_embedding."		)

     if "x_emb.emb." in key:
          UpperCAmelCase__							: Dict 							=						key.replace("0.x_emb.emb"       ,    "embed_tokens"		)

     if "prime_state_ln" in key:
          return key.replace("prime_state_ln"       ,    "encoder.final_layer_norm"		)
     if ".ln" in key:
          return key.replace(".ln"       ,    ".layer_norm"		)
     if "_ln" in key:
          return key.replace("_ln"       ,    "_layer_norm"		)

     if "prime_state_proj" in key:
          return key.replace("prime_state_proj"       ,    "encoder.proj_in"		)
     if "prime_x_out" in key:
          return key.replace("prime_x_out"       ,    "encoder.lm_head"		)
     if "prior.x_out" in key:
          return key.replace("x_out"       ,    "fc_proj_out"		)
     if "x_emb" in key:
          return key.replace("x_emb"       ,    "embed_tokens"		)

     return key



def 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						{}
     import re

     UpperCAmelCase__							: Tuple 							=						re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"		)
     UpperCAmelCase__							: str 							=						re.compile(
         R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"		)
     UpperCAmelCase__							: Tuple 							=						re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"		)

     UpperCAmelCase__							: Optional[Any] 							=						re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"		)
     UpperCAmelCase__							: List[str] 							=						re.compile(
         R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"		)
     UpperCAmelCase__							: Optional[Any] 							=						re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"		)

     UpperCAmelCase__							: Optional[int] 							=						re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"		)
     UpperCAmelCase__							: Dict 							=						re.compile(
         R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"		)
     UpperCAmelCase__							: Optional[Any] 							=						re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"		)

     for original_key, value in state_dict.items():
          # rename vqvae.encoder keys
          if re_encoder_block_conv_in.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: Dict 							=						re_encoder_block_conv_in.match(lowerCAmelCase		)
               UpperCAmelCase__							: str 							=						regex_match.groups()
               UpperCAmelCase__							: Optional[Any] 							=						int(groups[2]		) * 2 + int(groups[3]		)
               UpperCAmelCase__							: List[Any] 							=						F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
               UpperCAmelCase__							: Union[str, Any] 							=						re_encoder_block_conv_in.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          elif re_encoder_block_resnet.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: int 							=						re_encoder_block_resnet.match(lowerCAmelCase		)
               UpperCAmelCase__							: int 							=						regex_match.groups()
               UpperCAmelCase__							: int 							=						int(groups[2]		) * 2 + int(groups[3]		)
               UpperCAmelCase__							: Union[str, Any] 							=						{"1": 1, "3": 2}[groups[-2]]
               UpperCAmelCase__							: Any 							=						F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
               UpperCAmelCase__							: List[str] 							=						F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               UpperCAmelCase__							: str 							=						prefix + resnet_block
               UpperCAmelCase__							: Tuple 							=						re_encoder_block_resnet.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: Dict 							=						re_encoder_block_proj_out.match(lowerCAmelCase		)
               UpperCAmelCase__							: Dict 							=						regex_match.groups()
               UpperCAmelCase__							: Optional[int] 							=						F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
               UpperCAmelCase__							: Union[str, Any] 							=						re_encoder_block_proj_out.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          # rename vqvae.decoder keys
          elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: List[Any] 							=						re_decoder_block_conv_out.match(lowerCAmelCase		)
               UpperCAmelCase__							: Dict 							=						regex_match.groups()
               UpperCAmelCase__							: Union[str, Any] 							=						int(groups[2]		) * 2 + int(groups[3]		) - 2
               UpperCAmelCase__							: Any 							=						F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
               UpperCAmelCase__							: Tuple 							=						re_decoder_block_conv_out.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          elif re_decoder_block_resnet.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: Optional[int] 							=						re_decoder_block_resnet.match(lowerCAmelCase		)
               UpperCAmelCase__							: Optional[int] 							=						regex_match.groups()
               UpperCAmelCase__							: List[str] 							=						int(groups[2]		) * 2 + int(groups[3]		) - 2
               UpperCAmelCase__							: Tuple 							=						{"1": 1, "3": 2}[groups[-2]]
               UpperCAmelCase__							: Any 							=						F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
               UpperCAmelCase__							: int 							=						F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               UpperCAmelCase__							: Union[str, Any] 							=						prefix + resnet_block
               UpperCAmelCase__							: List[Any] 							=						re_decoder_block_resnet.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: Optional[int] 							=						re_decoder_block_proj_in.match(lowerCAmelCase		)
               UpperCAmelCase__							: List[str] 							=						regex_match.groups()
               UpperCAmelCase__							: Dict 							=						F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
               UpperCAmelCase__							: Optional[int] 							=						re_decoder_block_proj_in.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          # rename prior cond.model to upsampler.upsample_block and resnet
          elif re_prior_cond_conv_out.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: List[str] 							=						re_prior_cond_conv_out.match(lowerCAmelCase		)
               UpperCAmelCase__							: Tuple 							=						regex_match.groups()
               UpperCAmelCase__							: Tuple 							=						int(groups[1]		) * 2 + int(groups[2]		) - 2
               UpperCAmelCase__							: List[str] 							=						F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
               UpperCAmelCase__							: int 							=						re_prior_cond_conv_out.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          elif re_prior_cond_resnet.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: Tuple 							=						re_prior_cond_resnet.match(lowerCAmelCase		)
               UpperCAmelCase__							: Optional[Any] 							=						regex_match.groups()
               UpperCAmelCase__							: int 							=						int(groups[1]		) * 2 + int(groups[2]		) - 2
               UpperCAmelCase__							: Any 							=						{"1": 1, "3": 2}[groups[-2]]
               UpperCAmelCase__							: int 							=						F"conditioner_blocks.upsampler.upsample_block.{block_index}."
               UpperCAmelCase__							: Dict 							=						F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               UpperCAmelCase__							: int 							=						prefix + resnet_block
               UpperCAmelCase__							: int 							=						re_prior_cond_resnet.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase		):
               UpperCAmelCase__							: Tuple 							=						re_prior_cond_proj_in.match(lowerCAmelCase		)
               UpperCAmelCase__							: Tuple 							=						regex_match.groups()
               UpperCAmelCase__							: List[Any] 							=						F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
               UpperCAmelCase__							: Union[str, Any] 							=						re_prior_cond_proj_in.sub(lowerCAmelCase       ,    lowerCAmelCase		)

          # keep original key
          else:
               UpperCAmelCase__							: Tuple 							=						original_key

          UpperCAmelCase__							: Optional[Any] 							=						replace_key(lowerCAmelCase		)

          if F"{key_prefix}.{key}" not in model_state_dict or key is None:
               print(F"failed converting {original_key} to {key}, does not match"		)

          # handle missmatched shape
          elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
               UpperCAmelCase__							: Any 							=						model_state_dict[F"{key_prefix}.{key}"]
               print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"		)
               UpperCAmelCase__							: str 							=						original_key

          UpperCAmelCase__							: Optional[int] 							=						original_key
          UpperCAmelCase__							: List[str] 							=						value

     return new_dict



@torch.no_grad()
def 							a__ (				lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     for file in MODEL_MAPPING[model_name]:
          if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'		)[-1]}"		):
               UpperCAmelCase__							: Optional[int] 							=						requests.get(F"{PREFIX}{file}"       ,    allow_redirects=lowerCAmelCase		)
               os.makedirs(F"{pytorch_dump_folder_path}/"       ,    exist_ok=lowerCAmelCase		)
               open(F"{pytorch_dump_folder_path}/{file.split('/'		)[-1]}"       ,    "wb"		).write(r.content		)

     UpperCAmelCase__							: Any 							=						MODEL_MAPPING[model_name.split("/"		)[-1]]

     UpperCAmelCase__							: Dict 							=						JukeboxConfig.from_pretrained(lowerCAmelCase		)
     UpperCAmelCase__							: int 							=						JukeboxModel(lowerCAmelCase		)

     UpperCAmelCase__							: str 							=						[]
     UpperCAmelCase__							: str 							=						{}
     for i, dict_name in enumerate(lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'		)[-1]}"		)["model"]

          UpperCAmelCase__							: Union[str, Any] 							=						{}
          for k in old_dic.keys():
               if k.endswith(".b"		):
                    UpperCAmelCase__							: Union[str, Any] 							=						old_dic[k]
               elif k.endswith(".w"		):
                    UpperCAmelCase__							: List[str] 							=						old_dic[k]
               elif "level_2" not in dict_name and "cond.model." in k:
                    UpperCAmelCase__							: List[str] 							=						old_dic[k]
               else:
                    UpperCAmelCase__							: Optional[int] 							=						old_dic[k]

          UpperCAmelCase__							: str 							=						"vqvae" if i == 0 else F"priors.{3 - i}"
          UpperCAmelCase__							: Tuple 							=						fix_jukebox_keys(lowerCAmelCase       ,    model.state_dict()       ,    lowerCAmelCase       ,    lowerCAmelCase		)
          weight_dict.append(lowerCAmelCase		)

     UpperCAmelCase__							: Union[str, Any] 							=						weight_dict.pop(0		)
     model.vqvae.load_state_dict(lowerCAmelCase		)
     for i in range(len(lowerCAmelCase		)		):
          model.priors[i].load_state_dict(weight_dict[2 - i]		)

     Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
     with open(F"{pytorch_dump_folder_path}/mapping.json"       ,    "w"		) as txtfile:
          json.dump(lowerCAmelCase       ,    lowerCAmelCase		)

     print(F"Saving model {model_name} to {pytorch_dump_folder_path}"		)
     model.save_pretrained(lowerCAmelCase		)

     return weight_dict


if __name__ == "__main__":
    A__      :	Dict          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--model_name""",
        default="""jukebox-5b-lyrics""",
        type=str,
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""jukebox-5b-lyrics-converted""",
        type=str,
        help="""Path to the output PyTorch model directory.""",
    )
    A__      :	str          =    parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_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  )



