





'''simple docstring'''


import argparse

import torch

from diffusers import UNetaDConditionModel
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]			,__lowerCamelCase      :      str			,__lowerCamelCase      :      List[str]			,__lowerCamelCase      :      List[str]=None			,__lowerCamelCase      :      Any=None			,__lowerCamelCase      :      str=None				):
   assert isinstance(a_			,a_				), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         lowercase_    :Tuple					    =				old_checkpoint[path]
         lowercase_    :Optional[int]					    =				old_tensor.shape[0] // 3

         lowercase_    :Any					    =				(-1, channels) if len(old_tensor.shape				) == 3 else (-1)

         lowercase_    :Tuple					    =				old_tensor.shape[0] // config["num_head_channels"] // 3

         lowercase_    :Any					    =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
         lowercase_    ,							lowercase_    ,							lowercase_    :Any					    =				old_tensor.split(channels // num_heads			,dim=1				)

         lowercase_    :Dict					    =				query.reshape(a_				)
         lowercase_    :Tuple					    =				key.reshape(a_				)
         lowercase_    :Optional[int]					    =				value.reshape(a_				)

   for path in paths:
      lowercase_    :List[str]					    =				path["new"]

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      if additional_replacements is not None:
         for replacement in additional_replacements:
            lowercase_    :Union[str, Any]					    =				new_path.replace(replacement["old"]			,replacement["new"]				)

        # proj_attn.weight has to be converted from conv 1D to linear
      lowercase_    :Tuple					    =				old_checkpoint[path["old"]]
      lowercase_    :List[str]					    =				["proj_attn.weight"]
      lowercase_    :Dict					    =				["proj_out.weight", "proj_in.weight"]
      if any(k in new_path for k in names				):
         lowercase_    :Tuple					    =				weight[:, :, 0]
      elif any(k in new_path for k in names_a				) and len(weight.shape				) > 2 and ".attentions." not in new_path:
         lowercase_    :List[Any]					    =				weight[:, :, 0]
      else:
         lowercase_    :int					    =				weight
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Tuple			,__lowerCamelCase      :      Any=0				):
   lowercase_    :List[Any]					    =				[]
   for old_item in old_list:
      lowercase_    :Tuple					    =				old_item

      #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
      #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

      #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
      #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

      #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

      mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]			,__lowerCamelCase      :      Dict=1				):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("."				)[n_shave_prefix_segments:]				)
   else:
      return ".".join(path.split("."				)[:n_shave_prefix_segments]				)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      int=0				):
   lowercase_    :List[str]					    =				[]
   for old_item in old_list:
      mapping.append({"old": old_item, "new": old_item}				)

   return mapping
def 							UpperCAmelCase_						(		__lowerCamelCase      :      List[Any]			,__lowerCamelCase      :      Any=0				):
   lowercase_    :List[str]					    =				[]
   for old_item in old_list:
      lowercase_    :List[str]					    =				old_item.replace("in_layers.0"			,"norm1"				)
      lowercase_    :Union[str, Any]					    =				new_item.replace("in_layers.2"			,"conv1"				)

      lowercase_    :List[Any]					    =				new_item.replace("out_layers.0"			,"norm2"				)
      lowercase_    :List[str]					    =				new_item.replace("out_layers.3"			,"conv2"				)

      lowercase_    :Union[str, Any]					    =				new_item.replace("emb_layers.1"			,"time_emb_proj"				)
      lowercase_    :str					    =				new_item.replace("skip_connection"			,"conv_shortcut"				)

      lowercase_    :Union[str, Any]					    =				shave_segments(a_			,n_shave_prefix_segments=a_				)

      if "temopral_conv" not in old_item:
         mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str			,__lowerCamelCase      :      str			,__lowerCamelCase      :      str=None			,__lowerCamelCase      :      Tuple=False				):
   lowercase_    :Optional[int]					    =				{}
   lowercase_    :List[Any]					    =				list(checkpoint.keys()				)

   lowercase_    :Optional[Any]					    =				"model.diffusion_model."

   # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
   if sum(k.startswith("model_ema"				) for k in keys				) > 1_00 and extract_ema:
      print(F'Checkpoint {path} has both EMA and non-EMA weights.'				)
      print(
          "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
          " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."				)
      for key in keys:
         if key.startswith("model.diffusion_model"				):
            lowercase_    :Tuple					    =				"model_ema." + "".join(key.split("."				)[1:]				)
            lowercase_    :List[str]					    =				checkpoint.pop(a_				)
   else:
      if sum(k.startswith("model_ema"				) for k in keys				) > 1_00:
         print(
             "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
             " weights (usually better for inference), please make sure to add the `--extract_ema` flag."				)

      for key in keys:
         lowercase_    :Any					    =				checkpoint.pop(a_				)

   lowercase_    :int					    =				{}

   lowercase_    :Optional[Any]					    =				unet_state_dict["time_embed.0.weight"]
   lowercase_    :int					    =				unet_state_dict["time_embed.0.bias"]
   lowercase_    :Any					    =				unet_state_dict["time_embed.2.weight"]
   lowercase_    :Dict					    =				unet_state_dict["time_embed.2.bias"]

   if config["class_embed_type"] is None:
      # No parameters to port
      ...
   elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
      lowercase_    :Optional[Any]					    =				unet_state_dict["label_emb.0.0.weight"]
      lowercase_    :List[Any]					    =				unet_state_dict["label_emb.0.0.bias"]
      lowercase_    :Union[str, Any]					    =				unet_state_dict["label_emb.0.2.weight"]
      lowercase_    :List[Any]					    =				unet_state_dict["label_emb.0.2.bias"]
   else:
      raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'				)

   lowercase_    :Any					    =				unet_state_dict["input_blocks.0.0.weight"]
   lowercase_    :Dict					    =				unet_state_dict["input_blocks.0.0.bias"]

   lowercase_    :Tuple					    =				[v for v in unet_state_dict if v.startswith("input_blocks.0.1"				)]
   lowercase_    :Any					    =				renew_attention_paths(a_				)
   lowercase_    :Union[str, Any]					    =				{"old": "input_blocks.0.1", "new": "transformer_in"}
   assign_to_checkpoint(a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

   lowercase_    :Any					    =				unet_state_dict["out.0.weight"]
   lowercase_    :Any					    =				unet_state_dict["out.0.bias"]
   lowercase_    :Optional[Any]					    =				unet_state_dict["out.2.weight"]
   lowercase_    :Dict					    =				unet_state_dict["out.2.bias"]

   # Retrieves the keys for the input blocks only
   lowercase_    :str					    =				len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "input_blocks" in layer}				)
   lowercase_    :Dict					    =				{
       layer_id: [key for key in unet_state_dict if F'input_blocks.{layer_id}' in key]
       for layer_id in range(a_				)
   }

   # Retrieves the keys for the middle blocks only
   lowercase_    :Optional[Any]					    =				len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "middle_block" in layer}				)
   lowercase_    :Dict					    =				{
       layer_id: [key for key in unet_state_dict if F'middle_block.{layer_id}' in key]
       for layer_id in range(a_				)
   }

   # Retrieves the keys for the output blocks only
   lowercase_    :int					    =				len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "output_blocks" in layer}				)
   lowercase_    :Optional[int]					    =				{
       layer_id: [key for key in unet_state_dict if F'output_blocks.{layer_id}' in key]
       for layer_id in range(a_				)
   }

   for i in range(1			,a_				):
      lowercase_    :Any					    =				(i - 1) // (config["layers_per_block"] + 1)
      lowercase_    :List[Any]					    =				(i - 1) % (config["layers_per_block"] + 1)

      lowercase_    :List[Any]					    =				[
          key for key in input_blocks[i] if F'input_blocks.{i}.0' in key and F'input_blocks.{i}.0.op' not in key
      ]
      lowercase_    :List[Any]					    =				[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
      lowercase_    :Tuple					    =				[key for key in input_blocks[i] if F'input_blocks.{i}.2' in key]

      if F'input_blocks.{i}.op.weight' in unet_state_dict:
         lowercase_    :Union[str, Any]					    =				unet_state_dict.pop(
             F'input_blocks.{i}.op.weight'				)
         lowercase_    :Dict					    =				unet_state_dict.pop(
             F'input_blocks.{i}.op.bias'				)

      lowercase_    :List[str]					    =				renew_resnet_paths(a_				)
      lowercase_    :int					    =				{"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
      assign_to_checkpoint(
          a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

      lowercase_    :Tuple					    =				[key for key in resnets if "temopral_conv" in key]
      lowercase_    :Optional[Any]					    =				renew_temp_conv_paths(a_				)
      lowercase_    :Dict					    =				{
          "old": F'input_blocks.{i}.0.temopral_conv',
          "new": F'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
      }
      assign_to_checkpoint(
          a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

      if len(a_				):
         lowercase_    :int					    =				renew_attention_paths(a_				)
         lowercase_    :List[Any]					    =				{"old": F'input_blocks.{i}.1', "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
         assign_to_checkpoint(
             a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

      if len(a_				):
         lowercase_    :Tuple					    =				renew_attention_paths(a_				)
         lowercase_    :Optional[int]					    =				{
             "old": F'input_blocks.{i}.2',
             "new": F'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
         }
         assign_to_checkpoint(
             a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

   lowercase_    :Optional[Any]					    =				middle_blocks[0]
   lowercase_    :int					    =				[key for key in resnet_a if "temopral_conv" in key]
   lowercase_    :List[Any]					    =				middle_blocks[1]
   lowercase_    :List[Any]					    =				middle_blocks[2]
   lowercase_    :List[str]					    =				middle_blocks[3]
   lowercase_    :str					    =				[key for key in resnet_a if "temopral_conv" in key]

   lowercase_    :Optional[int]					    =				renew_resnet_paths(a_				)
   lowercase_    :Optional[Any]					    =				{"old": "middle_block.0", "new": "mid_block.resnets.0"}
   assign_to_checkpoint(
       a_			,a_			,a_			,config=a_			,additional_replacements=[meta_path]				)

   lowercase_    :List[Any]					    =				renew_temp_conv_paths(a_				)
   lowercase_    :int					    =				{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
   assign_to_checkpoint(
       a_			,a_			,a_			,config=a_			,additional_replacements=[meta_path]				)

   lowercase_    :Dict					    =				renew_resnet_paths(a_				)
   lowercase_    :List[str]					    =				{"old": "middle_block.3", "new": "mid_block.resnets.1"}
   assign_to_checkpoint(
       a_			,a_			,a_			,config=a_			,additional_replacements=[meta_path]				)

   lowercase_    :str					    =				renew_temp_conv_paths(a_				)
   lowercase_    :str					    =				{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
   assign_to_checkpoint(
       a_			,a_			,a_			,config=a_			,additional_replacements=[meta_path]				)

   lowercase_    :List[str]					    =				renew_attention_paths(a_				)
   lowercase_    :Optional[int]					    =				{"old": "middle_block.1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(
       a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

   lowercase_    :List[Any]					    =				renew_attention_paths(a_				)
   lowercase_    :Any					    =				{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
   assign_to_checkpoint(
       a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

   for i in range(a_				):
      lowercase_    :int					    =				i // (config["layers_per_block"] + 1)
      lowercase_    :Optional[int]					    =				i % (config["layers_per_block"] + 1)
      lowercase_    :Optional[int]					    =				[shave_segments(a_			,2				) for name in output_blocks[i]]
      lowercase_    :Tuple					    =				{}

      for layer in output_block_layers:
         lowercase_    ,							lowercase_    :Union[str, Any]					    =				layer.split("."				)[0], shave_segments(a_			,1				)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(a_				)
         else:
            lowercase_    :List[str]					    =				[layer_name]

      if len(a_				) > 1:
         lowercase_    :int					    =				[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
         lowercase_    :Optional[int]					    =				[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
         lowercase_    :Any					    =				[key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

         lowercase_    :Optional[Any]					    =				renew_resnet_paths(a_				)
         lowercase_    :int					    =				renew_resnet_paths(a_				)

         lowercase_    :str					    =				{"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
         assign_to_checkpoint(
             a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

         lowercase_    :Optional[Any]					    =				[key for key in resnets if "temopral_conv" in key]
         lowercase_    :Any					    =				renew_temp_conv_paths(a_				)
         lowercase_    :int					    =				{
             "old": F'output_blocks.{i}.0.temopral_conv',
             "new": F'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
         }
         assign_to_checkpoint(
             a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

         lowercase_    :Optional[int]					    =				{k: sorted(a_				) for k, v in output_block_list.items()}
         if ["conv.bias", "conv.weight"] in output_block_list.values():
            lowercase_    :Any					    =				list(output_block_list.values()				).index(["conv.bias", "conv.weight"]				)
            lowercase_    :Tuple					    =				unet_state_dict[
                F'output_blocks.{i}.{index}.conv.weight'
            ]
            lowercase_    :Any					    =				unet_state_dict[
                F'output_blocks.{i}.{index}.conv.bias'
            ]

            # Clear attentions as they have been attributed above.
            if len(a_				) == 2:
               lowercase_    :Any					    =				[]

         if len(a_				):
            lowercase_    :Union[str, Any]					    =				renew_attention_paths(a_				)
            lowercase_    :Dict					    =				{
                "old": F'output_blocks.{i}.1',
                "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)

         if len(a_				):
            lowercase_    :Optional[int]					    =				renew_attention_paths(a_				)
            lowercase_    :Optional[int]					    =				{
                "old": F'output_blocks.{i}.2',
                "new": F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                a_			,a_			,a_			,additional_replacements=[meta_path]			,config=a_				)
      else:
         lowercase_    :Union[str, Any]					    =				renew_resnet_paths(a_			,n_shave_prefix_segments=1				)
         for path in resnet_0_paths:
            lowercase_    :Tuple					    =				".".join(["output_blocks", str(a_				), path["old"]]				)
            lowercase_    :int					    =				".".join(["up_blocks", str(a_				), "resnets", str(a_				), path["new"]]				)
            lowercase_    :Tuple					    =				unet_state_dict[old_path]

         lowercase_    :List[Any]					    =				[l for l in output_block_layers if "temopral_conv" in l]
         for path in temopral_conv_paths:
            lowercase_    :Optional[Any]					    =				path.split("temopral_conv."				)[-1]
            lowercase_    :Tuple					    =				".".join(["output_blocks", str(a_				), str(a_				), "temopral_conv", pruned_path]				)
            lowercase_    :Optional[int]					    =				".".join(["up_blocks", str(a_				), "temp_convs", str(a_				), pruned_path]				)
            lowercase_    :str					    =				unet_state_dict[old_path]

   return new_checkpoint


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

       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
       )
       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
       lowerCAmelCase      :  int      							=parser.parse_args()

       lowerCAmelCase      :  List[Any]      							=torch.load(args.checkpoint_path, map_location='''cpu''')
       lowerCAmelCase      :  Union[str, Any]      							=UNetaDConditionModel()

       lowerCAmelCase      :  Any      							=convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       lowerCAmelCase      :  str      							=set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       lowerCAmelCase      :  Any      							=set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --









'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase      :  Any      							=logging.get_logger(__name__)
class    a_     (       _lowerCAmelCase							):
     __A  				=    ["pixel_values"]





     def __init__(				self	:      Tuple			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      PILImageResampling = PILImageResampling.BICUBIC			,   lowercase	:      bool = True			,   lowercase	:      Union[int, float] = 1 / 255			,   lowercase	:      bool = True			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   **lowercase	:      int			,   ):





        """simple docstring"""




        super().__init__(**lowercase		)
        lowercase_    :Dict					    =				crop_size if crop_size is not None else {"height": 256, "width": 256}
        lowercase_    :str					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Optional[Any]					    =				size if size is not None else {"height": 224, "width": 224}
        lowercase_    :Union[str, Any]					    =				get_size_dict(lowercase		)

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





     def 							lowercase__   (				self	:      Any			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[Any]			,   ):





        """simple docstring"""




        lowercase_    :Dict					    =				self.size if size is None else size
        lowercase_    :Dict					    =				get_size_dict(lowercase		)
        lowercase_    :List[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)

        lowercase_    ,							lowercase_    :List[str]					    =				get_image_size(lowercase		)
        lowercase_    :List[str]					    =				min(lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				(size["height"] / crop_size["height"]) * min_dim
        lowercase_    :int					    =				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowercase			,   size=(cropped_height, cropped_width)			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      PILImageResampling = PIL.Image.BICUBIC			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[str]			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				get_size_dict(lowercase		)
        if "height" not in size or "width" not in size:
           raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
        return resize(
            lowercase			,   size=(size["height"], size["width"])			,   resample=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[int, float]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      Union[str, Any]			,   ):





        """simple docstring"""




        return rescale(lowercase			,   scale=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      str			,   ):





        """simple docstring"""




        return normalize(lowercase			,   mean=lowercase			,   std=lowercase			,   data_format=lowercase			,   **lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      ImageInput			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      PILImageResampling = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[float] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[str, TensorType]] = None			,   lowercase	:      ChannelDimension = ChannelDimension.FIRST			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_    :Union[str, Any]					    =				crop_size if crop_size is not None else self.crop_size
        lowercase_    :Optional[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Union[str, Any]					    =				do_resize if do_resize is not None else self.do_resize
        lowercase_    :Any					    =				size if size is not None else self.size
        lowercase_    :List[Any]					    =				get_size_dict(lowercase		)
        lowercase_    :Optional[Any]					    =				resample if resample is not None else self.resample
        lowercase_    :Dict					    =				do_rescale if do_rescale is not None else self.do_rescale
        lowercase_    :List[Any]					    =				rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_    :str					    =				do_normalize if do_normalize is not None else self.do_normalize
        lowercase_    :Dict					    =				image_mean if image_mean is not None else self.image_mean
        lowercase_    :List[Any]					    =				image_std if image_std is not None else self.image_std

        lowercase_    :Any					    =				make_list_of_images(lowercase		)

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

        if do_center_crop and crop_size is None:
           raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."		)

        if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."		)

        if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        lowercase_    :Optional[int]					    =				[to_numpy_array(lowercase		) for image in images]

        if do_center_crop:
           lowercase_    :str					    =				[self.center_crop(lowercase			,   lowercase			,   size=lowercase		) for image in images]

        if do_resize:
           lowercase_    :List[str]					    =				[self.resize(image=lowercase			,   size=lowercase			,   resample=lowercase		) for image in images]

        if do_rescale:
           lowercase_    :List[Any]					    =				[self.rescale(image=lowercase			,   scale=lowercase		) for image in images]

        if do_normalize:
           lowercase_    :Optional[Any]					    =				[self.normalize(image=lowercase			,   mean=lowercase			,   std=lowercase		) for image in images]

        lowercase_    :Optional[Any]					    =				[to_channel_dimension_format(lowercase			,   lowercase		) for image in images]

        lowercase_    :Union[str, Any]					    =				{"pixel_values": images}
        return BatchFeature(data=lowercase			,   tensor_type=lowercase		)


