
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCamelCase_						:     int	       =							[
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

UpperCamelCase_						:     str	       =							[
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

UpperCamelCase_						:     Dict	       =							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														UpperCamelCase_						:     List[Any]	       =							f'''down_blocks.{i}.resnets.{j}.'''
														UpperCamelCase_						:     str	       =							f'''input_blocks.{3*i + j + 1}.0.'''
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					UpperCamelCase_						:     Dict	       =							f'''down_blocks.{i}.attentions.{j}.'''
																					UpperCamelCase_						:     List[str]	       =							f'''input_blocks.{3*i + j + 1}.1.'''
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														UpperCamelCase_						:     List[str]	       =							f'''up_blocks.{i}.resnets.{j}.'''
														UpperCamelCase_						:     Dict	       =							f'''output_blocks.{3*i + j}.0.'''
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					UpperCamelCase_						:     int	       =							f'''up_blocks.{i}.attentions.{j}.'''
																					UpperCamelCase_						:     List[Any]	       =							f'''output_blocks.{3*i + j}.1.'''
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														UpperCamelCase_						:     List[str]	       =							f'''down_blocks.{i}.downsamplers.0.conv.'''
														UpperCamelCase_						:     List[Any]	       =							f'''input_blocks.{3*(i+1)}.0.op.'''
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														UpperCamelCase_						:     List[Any]	       =							f'''up_blocks.{i}.upsamplers.0.'''
														UpperCamelCase_						:     Dict	       =							f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCamelCase_						:     Any	       =							"""mid_block.attentions.0."""
UpperCamelCase_						:     List[Any]	       =							"""middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							UpperCamelCase_						:     Dict	       =							f'''mid_block.resnets.{j}.'''
							UpperCamelCase_						:     Tuple	       =							f'''middle_block.{2*j}.'''
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 		UpperCamelCase     (       _UpperCAmelCase  :   Tuple  )   ->			str:






						'''simple docstring'''

						_lowercase       :	Dict           =   {k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												_lowercase       :	int           =   sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								_lowercase       :	Optional[int]           =   v.replace(_UpperCAmelCase ,			_UpperCAmelCase  )
																		_lowercase       :	int           =   v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		_lowercase       :	Optional[int]           =   v.replace(_UpperCAmelCase ,			_UpperCAmelCase  )
												_lowercase       :	Optional[int]           =   v
						_lowercase       :	Union[str, Any]           =   {v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCamelCase_						:     Tuple	       =							[
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														UpperCamelCase_						:     str	       =							f'''encoder.down_blocks.{i}.resnets.{j}.'''
														UpperCamelCase_						:     str	       =							f'''encoder.down.{i}.block.{j}.'''
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														UpperCamelCase_						:     Optional[Any]	       =							f'''down_blocks.{i}.downsamplers.0.'''
														UpperCamelCase_						:     int	       =							f'''down.{i}.downsample.'''
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														UpperCamelCase_						:     List[str]	       =							f'''up_blocks.{i}.upsamplers.0.'''
														UpperCamelCase_						:     Any	       =							f'''up.{3-i}.upsample.'''
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														UpperCamelCase_						:     int	       =							f'''decoder.up_blocks.{i}.resnets.{j}.'''
														UpperCamelCase_						:     int	       =							f'''decoder.up.{3-i}.block.{j}.'''
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							UpperCamelCase_						:     str	       =							f'''mid_block.resnets.{i}.'''
							UpperCamelCase_						:     Optional[Any]	       =							f'''mid.block_{i+1}.'''
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCamelCase_						:     Dict	       =							[
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]



def 		UpperCamelCase     (       _UpperCAmelCase  :   Tuple  )   ->			Dict:






						'''simple docstring'''

						return w.reshape(*w.shape ,			1 ,			1  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any]  )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	int           =   {k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		_lowercase       :	Optional[int]           =   v.replace(_UpperCAmelCase ,			_UpperCAmelCase  )
												_lowercase       :	int           =   v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								_lowercase       :	List[str]           =   v.replace(_UpperCAmelCase ,			_UpperCAmelCase  )
																		_lowercase       :	str           =   v
						_lowercase       :	Any           =   {v: vae_state_dict[k] for k, v in mapping.items()}
						_lowercase       :	str           =   ["q", "k", "v", "proj_out"]
						for k, v in new_state_dict.items():
												for weight_name in weights_to_convert:
																		if f"""mid.attn_1.{weight_name}.weight""" in k:
																								print(f"""Reshaping {k} for SD format"""  )
																								_lowercase       :	List[Any]           =   reshape_weight_for_sd(_UpperCAmelCase  )
						return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCamelCase_						:     Optional[int]	       =							[
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
UpperCamelCase_						:     Optional[Any]	       =							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase_						:     List[Any]	       =							re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCamelCase_						:     Tuple	       =							{"""q""": 0, """k""": 1, """v""": 2}



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	List[str]           =   {}
						_lowercase       :	Tuple           =   {}
						_lowercase       :	Optional[int]           =   {}
						for k, v in text_enc_dict.items():
												if (
												    k.endswith(".self_attn.q_proj.weight"  )
												    or k.endswith(".self_attn.k_proj.weight"  )
												    or k.endswith(".self_attn.v_proj.weight"  )
												):
																		_lowercase       :	str           =   k[: -len(".q_proj.weight"  )]
																		_lowercase       :	List[str]           =   k[-len("q_proj.weight"  )]
																		if k_pre not in capture_qkv_weight:
																								_lowercase       :	List[Any]           =   [None, None, None]
																		_lowercase       :	int           =   v
																		continue

												if (
												    k.endswith(".self_attn.q_proj.bias"  )
												    or k.endswith(".self_attn.k_proj.bias"  )
												    or k.endswith(".self_attn.v_proj.bias"  )
												):
																		_lowercase       :	Optional[Any]           =   k[: -len(".q_proj.bias"  )]
																		_lowercase       :	List[str]           =   k[-len("q_proj.bias"  )]
																		if k_pre not in capture_qkv_bias:
																								_lowercase       :	Optional[int]           =   [None, None, None]
																		_lowercase       :	List[Any]           =   v
																		continue

												_lowercase       :	Dict           =   textenc_pattern.sub(lambda _UpperCAmelCase  : protected[re.escape(m.group(0  )  )] ,			_UpperCAmelCase  )
												_lowercase       :	Optional[Any]           =   v

						for k_pre, tensors in capture_qkv_weight.items():
												if None in tensors:
																		raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"  )
												_lowercase       :	Tuple           =   textenc_pattern.sub(lambda _UpperCAmelCase  : protected[re.escape(m.group(0  )  )] ,			_UpperCAmelCase  )
												_lowercase       :	int           =   torch.cat(_UpperCAmelCase  )

						for k_pre, tensors in capture_qkv_bias.items():
												if None in tensors:
																		raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"  )
												_lowercase       :	Union[str, Any]           =   textenc_pattern.sub(lambda _UpperCAmelCase  : protected[re.escape(m.group(0  )  )] ,			_UpperCAmelCase  )
												_lowercase       :	Tuple           =   torch.cat(_UpperCAmelCase  )

						return new_state_dict



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[Any]  )   ->			int:






						'''simple docstring'''

						return text_enc_dict


if __name__ == "__main__":
							UpperCamelCase_						:     str	       =							argparse.ArgumentParser()

							parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
							parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
							parser.add_argument(
							    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
							)

							UpperCamelCase_						:     Optional[Any]	       =							parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							UpperCamelCase_						:     int	       =							osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
							UpperCamelCase_						:     Any	       =							osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
							UpperCamelCase_						:     List[Any]	       =							osp.join(args.model_path, """text_encoder""", """model.safetensors""")

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														UpperCamelCase_						:     str	       =							load_file(unet_path, device="""cpu""")
							else:
														UpperCamelCase_						:     Optional[int]	       =							osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
														UpperCamelCase_						:     List[Any]	       =							torch.load(unet_path, map_location="""cpu""")

							if osp.exists(vae_path):
														UpperCamelCase_						:     int	       =							load_file(vae_path, device="""cpu""")
							else:
														UpperCamelCase_						:     Dict	       =							osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
														UpperCamelCase_						:     List[str]	       =							torch.load(vae_path, map_location="""cpu""")

							if osp.exists(text_enc_path):
														UpperCamelCase_						:     Optional[int]	       =							load_file(text_enc_path, device="""cpu""")
							else:
														UpperCamelCase_						:     Optional[Any]	       =							osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
														UpperCamelCase_						:     Union[str, Any]	       =							torch.load(text_enc_path, map_location="""cpu""")

							# Convert the UNet model
							UpperCamelCase_						:     Dict	       =							convert_unet_state_dict(unet_state_dict)
							UpperCamelCase_						:     Tuple	       =							{"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							UpperCamelCase_						:     Optional[Any]	       =							convert_vae_state_dict(vae_state_dict)
							UpperCamelCase_						:     int	       =							{"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							UpperCamelCase_						:     Optional[Any]	       =							"""text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														UpperCamelCase_						:     str	       =							{"""transformer.""" + k: v for k, v in text_enc_dict.items()}
														UpperCamelCase_						:     List[str]	       =							convert_text_enc_state_dict_vaa(text_enc_dict)
														UpperCamelCase_						:     str	       =							{"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
							else:
														UpperCamelCase_						:     Optional[Any]	       =							convert_text_enc_state_dict(text_enc_dict)
														UpperCamelCase_						:     Optional[int]	       =							{"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							UpperCamelCase_						:     List[Any]	       =							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														UpperCamelCase_						:     Optional[int]	       =							{k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														UpperCamelCase_						:     Optional[Any]	       =							{"""state_dict""": state_dict}
														torch.save(state_dict, args.checkpoint_path)






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCamelCase_						:     Tuple	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: List[str]      ,	snake_case				: bool = True      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BILINEAR      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	**snake_case				: List[Any]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	List[str]           =   size if size is not None else {"shortest_edge": 256}
							_lowercase       :	Union[str, Any]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	List[str]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	param_name="crop_size"    )
							_lowercase       :	List[Any]           =   do_resize
							_lowercase       :	Optional[Any]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Tuple           =   do_center_crop
							_lowercase       :	Any           =   crop_size
							_lowercase       :	str           =   do_rescale
							_lowercase       :	int           =   rescale_factor
							_lowercase       :	List[Any]           =   do_normalize
							_lowercase       :	List[str]           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_lowercase       :	Optional[Any]           =   image_std if image_std is not None else IMAGENET_STANDARD_STD






	def       _a(self				: Tuple      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: int      ,	) ->			np.ndarray:
							_lowercase       :	Union[str, Any]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							if "shortest_edge" not in size:
													raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
							_lowercase       :	int           =   get_resize_output_image_size(snake_case      ,	size=size["shortest_edge"]      ,	default_to_square=snake_case    )
							return resize(snake_case      ,	size=snake_case      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Union[str, Any]      ,	) ->			np.ndarray:
							_lowercase       :	Union[str, Any]           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Union[str, Any]      ,	snake_case				: np.ndarray      ,	snake_case				: float      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[Any]    ) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: int      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Tuple      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Optional[int]      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: bool = None      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	snake_case				: Union[str, ChannelDimension] = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			Union[str, Any]:
							_lowercase       :	Any           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	List[str]           =   size if size is not None else self.size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Tuple           =   resample if resample is not None else self.resample
							_lowercase       :	List[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	int           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	param_name="crop_size"    )
							_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       :	Union[str, Any]           =   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       :	Optional[Any]           =   image_std if image_std is not None else self.image_std

							_lowercase       :	int           =   make_list_of_images(snake_case    )

							if not valid_images(snake_case    ):
													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:
													raise ValueError("Size 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."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Tuple           =   [to_numpy_array(snake_case    ) for image in images]

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

							if do_center_crop:
													_lowercase       :	List[str]           =   [self.center_crop(image=snake_case      ,	size=snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	Any           =   [self.rescale(image=snake_case      ,	scale=snake_case    ) for image in images]

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

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Optional[Any]           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )






	def       _a(self				: Dict      ,	snake_case				: List[str]      ,	snake_case				: List[Tuple] = None    ) ->			Optional[Any]:
							_lowercase       :	Dict           =   outputs.logits

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

													if is_torch_tensor(snake_case    ):
																			_lowercase       :	Dict           =   target_sizes.numpy()

													_lowercase       :	Tuple           =   []

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

							return semantic_segmentation




