


'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def     a   (    __a				,      __a=1     )						->			Any:



					'''simple docstring'''






					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('''.'''     )[n_shave_prefix_segments:]     )
					else:
										return ".".join(path.split('''.'''     )[:n_shave_prefix_segments]     )


def     a   (    __a				,      __a=0     )						->			List[Any]:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[]
					for old_item in old_list:
										UpperCamelCase__     :str							      =				old_item.replace('''in_layers.0'''				,      '''norm1'''     )
										UpperCamelCase__     :Optional[int]							      =				new_item.replace('''in_layers.2'''				,      '''conv1'''     )

										UpperCamelCase__     :Union[str, Any]							      =				new_item.replace('''out_layers.0'''				,      '''norm2'''     )
										UpperCamelCase__     :Tuple							      =				new_item.replace('''out_layers.3'''				,      '''conv2'''     )

										UpperCamelCase__     :Dict							      =				new_item.replace('''emb_layers.1'''				,      '''time_emb_proj'''     )
										UpperCamelCase__     :Optional[Any]							      =				new_item.replace('''skip_connection'''				,      '''conv_shortcut'''     )

										UpperCamelCase__     :Union[str, Any]							      =				shave_segments(__a				,      n_shave_prefix_segments=__a     )

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

					return mapping


def     a   (    __a				,      __a=0     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				[]
					for old_item in old_list:
										UpperCamelCase__     :Optional[int]							      =				old_item

										UpperCamelCase__     :Optional[int]							      =				new_item.replace('''norm.weight'''				,      '''group_norm.weight'''     )
										UpperCamelCase__     :Optional[Any]							      =				new_item.replace('''norm.bias'''				,      '''group_norm.bias'''     )

										UpperCamelCase__     :int							      =				new_item.replace('''proj_out.weight'''				,      '''proj_attn.weight'''     )
										UpperCamelCase__     :int							      =				new_item.replace('''proj_out.bias'''				,      '''proj_attn.bias'''     )

										UpperCamelCase__     :Any							      =				shave_segments(__a				,      n_shave_prefix_segments=__a     )

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

					return mapping


def     a   (    __a				,      __a				,      __a				,      __a=None				,      __a=None				,      __a=None     )						->			str:



					'''simple docstring'''






					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():
															UpperCamelCase__     :Dict							      =				old_checkpoint[path]
															UpperCamelCase__     :Any							      =				old_tensor.shape[0] // 3

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

															UpperCamelCase__     :List[Any]							      =				old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

															UpperCamelCase__     :Optional[int]							      =				query.reshape(__a     )
															UpperCamelCase__     :Tuple							      =				key.reshape(__a     )
															UpperCamelCase__     :List[str]							      =				value.reshape(__a     )

					for path in paths:
										UpperCamelCase__     :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

										# Global renaming happens here
										UpperCamelCase__     :Dict							      =				new_path.replace('''middle_block.0'''				,      '''mid_block.resnets.0'''     )
										UpperCamelCase__     :Optional[Any]							      =				new_path.replace('''middle_block.1'''				,      '''mid_block.attentions.0'''     )
										UpperCamelCase__     :Any							      =				new_path.replace('''middle_block.2'''				,      '''mid_block.resnets.1'''     )

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

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															UpperCamelCase__     :Optional[int]							      =				old_checkpoint[path['''old''']][:, :, 0]
										else:
															UpperCamelCase__     :Union[str, Any]							      =				old_checkpoint[path['''old''']]


def     a   (    __a				,      __a     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :Union[str, Any]							      =				{}

					UpperCamelCase__     :str							      =				checkpoint['''time_embed.0.weight''']
					UpperCamelCase__     :List[str]							      =				checkpoint['''time_embed.0.bias''']
					UpperCamelCase__     :Tuple							      =				checkpoint['''time_embed.2.weight''']
					UpperCamelCase__     :List[str]							      =				checkpoint['''time_embed.2.bias''']

					UpperCamelCase__     :Dict							      =				checkpoint['''input_blocks.0.0.weight''']
					UpperCamelCase__     :List[str]							      =				checkpoint['''input_blocks.0.0.bias''']

					UpperCamelCase__     :Any							      =				checkpoint['''out.0.weight''']
					UpperCamelCase__     :List[Any]							      =				checkpoint['''out.0.bias''']
					UpperCamelCase__     :Optional[Any]							      =				checkpoint['''out.2.weight''']
					UpperCamelCase__     :Union[str, Any]							      =				checkpoint['''out.2.bias''']

					# Retrieves the keys for the input blocks only
					UpperCamelCase__     :Tuple							      =				len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''input_blocks''' in layer}     )
					UpperCamelCase__     :str							      =				{
					    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
					    for layer_id in range(__a     )
					}

					# Retrieves the keys for the middle blocks only
					UpperCamelCase__     :int							      =				len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''middle_block''' in layer}     )
					UpperCamelCase__     :Dict							      =				{
					    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
					    for layer_id in range(__a     )
					}

					# Retrieves the keys for the output blocks only
					UpperCamelCase__     :int							      =				len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''output_blocks''' in layer}     )
					UpperCamelCase__     :Optional[int]							      =				{
					    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
					    for layer_id in range(__a     )
					}

					for i in range(1				,      __a     ):
										UpperCamelCase__     :Union[str, Any]							      =				(i - 1) // (config['''num_res_blocks'''] + 1)
										UpperCamelCase__     :Any							      =				(i - 1) % (config['''num_res_blocks'''] + 1)

										UpperCamelCase__     :Tuple							      =				[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
										UpperCamelCase__     :Dict							      =				[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

										if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
															UpperCamelCase__     :Union[str, Any]							      =				checkpoint[
															    f'''input_blocks.{i}.0.op.weight'''
															]
															UpperCamelCase__     :Union[str, Any]							      =				checkpoint[
															    f'''input_blocks.{i}.0.op.bias'''
															]
															continue

										UpperCamelCase__     :Optional[Any]							      =				renew_resnet_paths(__a     )
										UpperCamelCase__     :Tuple							      =				{'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
										UpperCamelCase__     :List[Any]							      =				{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
										assign_to_checkpoint(
										    __a				,      __a				,      __a				,      additional_replacements=[meta_path, resnet_op]				,      config=__a     )

										if len(__a     ):
															UpperCamelCase__     :Optional[Any]							      =				renew_attention_paths(__a     )
															UpperCamelCase__     :Optional[Any]							      =				{
															    '''old''': f'''input_blocks.{i}.1''',
															    '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
															}
															UpperCamelCase__     :Optional[Any]							      =				{
															    f'''input_blocks.{i}.1.qkv.bias''': {
															        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
															        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
															        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
															    },
															    f'''input_blocks.{i}.1.qkv.weight''': {
															        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
															        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
															        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
															    },
															}
															assign_to_checkpoint(
															    __a				,      __a				,      __a				,      additional_replacements=[meta_path]				,      attention_paths_to_split=__a				,      config=__a				,      )

					UpperCamelCase__     :Union[str, Any]							      =				middle_blocks[0]
					UpperCamelCase__     :List[Any]							      =				middle_blocks[1]
					UpperCamelCase__     :int							      =				middle_blocks[2]

					UpperCamelCase__     :Optional[int]							      =				renew_resnet_paths(__a     )
					assign_to_checkpoint(__a				,      __a				,      __a				,      config=__a     )

					UpperCamelCase__     :List[Any]							      =				renew_resnet_paths(__a     )
					assign_to_checkpoint(__a				,      __a				,      __a				,      config=__a     )

					UpperCamelCase__     :Dict							      =				renew_attention_paths(__a     )
					UpperCamelCase__     :Tuple							      =				{
					    '''middle_block.1.qkv.bias''': {
					        '''key''': '''mid_block.attentions.0.key.bias''',
					        '''query''': '''mid_block.attentions.0.query.bias''',
					        '''value''': '''mid_block.attentions.0.value.bias''',
					    },
					    '''middle_block.1.qkv.weight''': {
					        '''key''': '''mid_block.attentions.0.key.weight''',
					        '''query''': '''mid_block.attentions.0.query.weight''',
					        '''value''': '''mid_block.attentions.0.value.weight''',
					    },
					}
					assign_to_checkpoint(
					    __a				,      __a				,      __a				,      attention_paths_to_split=__a				,      config=__a     )

					for i in range(__a     ):
										UpperCamelCase__     :Tuple							      =				i // (config['''num_res_blocks'''] + 1)
										UpperCamelCase__     :Dict							      =				i % (config['''num_res_blocks'''] + 1)
										UpperCamelCase__     :Optional[Any]							      =				[shave_segments(__a				,      2     ) for name in output_blocks[i]]
										UpperCamelCase__     :Optional[int]							      =				{}

										for layer in output_block_layers:
															UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				layer.split('''.'''     )[0], shave_segments(__a				,      1     )
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(__a     )
															else:
																				UpperCamelCase__     :Tuple							      =				[layer_name]

										if len(__a     ) > 1:
															UpperCamelCase__     :str							      =				[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
															UpperCamelCase__     :Optional[int]							      =				[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

															UpperCamelCase__     :Optional[int]							      =				renew_resnet_paths(__a     )
															UpperCamelCase__     :Any							      =				renew_resnet_paths(__a     )

															UpperCamelCase__     :List[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     )

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				UpperCamelCase__     :Tuple							      =				list(output_block_list.values()     ).index(['''conv.weight''', '''conv.bias''']     )
																				UpperCamelCase__     :int							      =				checkpoint[
																				    f'''output_blocks.{i}.{index}.conv.weight'''
																				]
																				UpperCamelCase__     :str							      =				checkpoint[
																				    f'''output_blocks.{i}.{index}.conv.bias'''
																				]

																				# Clear attentions as they have been attributed above.
																				if len(__a     ) == 2:
																									UpperCamelCase__     :Dict							      =				[]

															if len(__a     ):
																				UpperCamelCase__     :Optional[Any]							      =				renew_attention_paths(__a     )
																				UpperCamelCase__     :Union[str, Any]							      =				{
																				    '''old''': f'''output_blocks.{i}.1''',
																				    '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																				}
																				UpperCamelCase__     :Any							      =				{
																				    f'''output_blocks.{i}.1.qkv.bias''': {
																				        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																				        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																				        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																				    },
																				    f'''output_blocks.{i}.1.qkv.weight''': {
																				        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																				        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																				        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																				    },
																				}
																				assign_to_checkpoint(
																				    __a				,      __a				,      __a				,      additional_replacements=[meta_path]				,      attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions     ) else None				,      config=__a				,      )
										else:
															UpperCamelCase__     :Tuple							      =				renew_resnet_paths(__a				,      n_shave_prefix_segments=1     )
															for path in resnet_0_paths:
																				UpperCamelCase__     :Any							      =				'''.'''.join(['''output_blocks''', str(__a     ), path['''old''']]     )
																				UpperCamelCase__     :Union[str, Any]							      =				'''.'''.join(['''up_blocks''', str(__a     ), '''resnets''', str(__a     ), path['''new''']]     )

																				UpperCamelCase__     :Tuple							      =				checkpoint[old_path]

					return new_checkpoint


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

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		__snake_case							   =  parser.parse_args()

		__snake_case							   =  torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				__snake_case							   =  json.loads(f.read())

		__snake_case							   =  convert_ldm_checkpoint(checkpoint, config)

		if "ldm" in config:
				del config["ldm"]

		__snake_case							   =  UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				__snake_case							   =  DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
				__snake_case							   =  VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__snake_case							   =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)



'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a				,      __a				,      __a				,      __a     )						->			Tuple[int, int]:



					'''simple docstring'''






					def constraint_to_multiple_of(__a				,      __a				,      __a=0				,      __a=None     ):
										UpperCamelCase__     :Union[str, Any]							      =				round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															UpperCamelCase__     :List[str]							      =				math.floor(val / multiple     ) * multiple

										if x < min_val:
															UpperCamelCase__     :Union[str, Any]							      =				math.ceil(val / multiple     ) * multiple

										return x

					UpperCamelCase__     :str							      =				(output_size, output_size) if isinstance(__a				,      __a     ) else output_size

					UpperCamelCase__      ,       UpperCamelCase__     :int							      =				get_image_size(__a     )
					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				output_size

					# determine new height and width
					UpperCamelCase__     :List[str]							      =				output_height / input_height
					UpperCamelCase__     :str							      =				output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width     ) < abs(1 - scale_height     ):
															# fit width
															UpperCamelCase__     :str							      =				scale_width
										else:
															# fit height
															UpperCamelCase__     :int							      =				scale_height

					UpperCamelCase__     :Optional[int]							      =				constraint_to_multiple_of(scale_height * input_height				,      multiple=__a     )
					UpperCamelCase__     :List[Any]							      =				constraint_to_multiple_of(scale_width * input_width				,      multiple=__a     )

					return (new_height, new_width)







class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['pixel_values']




		def __init__(	self					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = PILImageResampling.BILINEAR					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = True					,     UpperCamelCase_ = 1 / 255					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				size if size is not None else {'''height''': 384, '''width''': 384}
							UpperCamelCase__     :Dict							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				do_resize
							UpperCamelCase__     :Union[str, Any]							      =				size
							UpperCamelCase__     :List[Any]							      =				keep_aspect_ratio
							UpperCamelCase__     :Optional[int]							      =				ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample
							UpperCamelCase__     :str							      =				do_rescale
							UpperCamelCase__     :Union[str, Any]							      =				rescale_factor
							UpperCamelCase__     :List[str]							      =				do_normalize
							UpperCamelCase__     :List[str]							      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__     :Dict							      =				image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				get_size_dict(UpperCamelCase_							)
							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()}'''							)
							UpperCamelCase__     :int							      =				get_resize_output_image_size(
							    UpperCamelCase_					,     output_size=(size['''height'''], size['''width'''])					,     keep_aspect_ratio=UpperCamelCase_					,     multiple=UpperCamelCase_					,     )
							return resize(UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							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					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__     :Any							      =				size if size is not None else self.size
							UpperCamelCase__     :List[str]							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							UpperCamelCase__     :str							      =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample if resample is not None else self.resample
							UpperCamelCase__     :str							      =				do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__     :int							      =				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__     :Optional[Any]							      =				do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__     :Optional[int]							      =				image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__     :List[str]							      =				image_std if image_std is not None else self.image_std

							UpperCamelCase__     :List[str]							      =				make_list_of_images(UpperCamelCase_							)

							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.'''							)

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

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

							# All transformations expect numpy arrays.
							UpperCamelCase__     :str							      =				[to_numpy_array(UpperCamelCase_							) for image in images]

							if do_resize:
												UpperCamelCase__     :str							      =				[self.resize(image=UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_							) for image in images]

							if do_rescale:
												UpperCamelCase__     :int							      =				[self.rescale(image=UpperCamelCase_					,     scale=UpperCamelCase_							) for image in images]

							if do_normalize:
												UpperCamelCase__     :str							      =				[self.normalize(image=UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_							) for image in images]

							UpperCamelCase__     :Optional[int]							      =				[to_channel_dimension_format(UpperCamelCase_					,     UpperCamelCase_							) for image in images]

							UpperCamelCase__     :List[str]							      =				{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_					,     tensor_type=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
												if len(UpperCamelCase_							) != len(UpperCamelCase_							):
																	raise ValueError(
																	    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''							)

												if is_torch_tensor(UpperCamelCase_							):
																	UpperCamelCase__     :Union[str, Any]							      =				target_sizes.numpy()

												UpperCamelCase__     :int							      =				[]

												for idx in range(len(UpperCamelCase_							)							):
																	UpperCamelCase__     :str							      =				torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0							)					,     size=target_sizes[idx]					,     mode='''bilinear'''					,     align_corners=UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				resized_logits[0].argmax(dim=0							)
																	semantic_segmentation.append(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Any							      =				logits.argmax(dim=1							)
												UpperCamelCase__     :Optional[Any]							      =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

							return semantic_segmentation