




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
				import PIL.Image

				from .features import FeatureType


a													=			None
a													=			"<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
a													=			[
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]




@dataclass
class       _A				:
		__a					    =       True
		__a					    =       None
		# Automatically constructed
		__a					    =       "PIL.Image.Image"
		__a					    =       pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}     )
		__a					    =       field(default="""Image"""       ,     init=__lowercase       ,     repr=__lowercase     )






		def __call__(    self			):
								return self.pa_type






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								if config.PIL_AVAILABLE:
														import PIL.Image
								else:
														raise ImportError("""To support encoding images, please install 'Pillow'."""			)

								if isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			np.array(_SCREAMING_SNAKE_CASE			)

								if isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														return {"path": value, "bytes": None}
								elif isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														return {"path": None, "bytes": value}
								elif isinstance(_SCREAMING_SNAKE_CASE						,		np.ndarray			):
														# convert the image array to PNG/TIFF bytes
														return encode_np_array(_SCREAMING_SNAKE_CASE			)
								elif isinstance(_SCREAMING_SNAKE_CASE						,		PIL.Image.Image			):
														# convert the PIL image to bytes (default format is PNG/TIFF)
														return encode_pil_image(_SCREAMING_SNAKE_CASE			)
								elif value.get("""path"""			) is not None and os.path.isfile(value["""path"""]			):
														# we set "bytes": None to not duplicate the data if they're already available locally
														return {"bytes": None, "path": value.get("""path"""			)}
								elif value.get("""bytes"""			) is not None or value.get("""path"""			) is not None:
														# store the image bytes, and path is used to infer the image format using the file extension
														return {"bytes": value.get("""bytes"""			), "path": value.get("""path"""			)}
								else:
														raise ValueError(
														    F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								if not self.decode:
														raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""			)

								if config.PIL_AVAILABLE:
														import PIL.Image
								else:
														raise ImportError("""To support decoding images, please install 'Pillow'."""			)

								if token_per_repo_id is None:
														_UpperCAmelCase      =			{}

								_UpperCAmelCase    ,					_UpperCAmelCase      =			value["""path"""], value["""bytes"""]
								if bytes_ is None:
														if path is None:
																				raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}."			)
														else:
																				if is_local_path(_SCREAMING_SNAKE_CASE			):
																										_UpperCAmelCase      =			PIL.Image.open(_SCREAMING_SNAKE_CASE			)
																				else:
																										_UpperCAmelCase      =			path.split("""::"""			)[-1]
																										try:
																																_UpperCAmelCase      =			string_to_dict(_SCREAMING_SNAKE_CASE						,		config.HUB_DATASETS_URL			)["""repo_id"""]
																																_UpperCAmelCase      =			token_per_repo_id.get(_SCREAMING_SNAKE_CASE			)
																										except ValueError:
																																_UpperCAmelCase      =			None
																										with xopen(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		use_auth_token=_SCREAMING_SNAKE_CASE			) as f:
																																_UpperCAmelCase      =			BytesIO(f.read()			)
																										_UpperCAmelCase      =			PIL.Image.open(bytes_			)
								else:
														_UpperCAmelCase      =			PIL.Image.open(BytesIO(bytes_			)			)
								image.load()  # to avoid "Too many open files" errors
								return image






		def 				UpperCAmelCase (    self			):
								from .features import Value

								return (
								    self
								    if self.decode
								    else {
								        "bytes": Value("""binary"""			),
								        "path": Value("""string"""			),
								    }
								)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								if pa.types.is_string(storage.type			):
														_UpperCAmelCase      =			pa.array([None] * len(_SCREAMING_SNAKE_CASE			)						,		type=pa.binary()			)
														_UpperCAmelCase      =			pa.StructArray.from_arrays([bytes_array, storage]						,		["""bytes""", """path"""]						,		mask=storage.is_null()			)
								elif pa.types.is_binary(storage.type			):
														_UpperCAmelCase      =			pa.array([None] * len(_SCREAMING_SNAKE_CASE			)						,		type=pa.string()			)
														_UpperCAmelCase      =			pa.StructArray.from_arrays([storage, path_array]						,		["""bytes""", """path"""]						,		mask=storage.is_null()			)
								elif pa.types.is_struct(storage.type			):
														if storage.type.get_field_index("""bytes"""			) >= 0:
																				_UpperCAmelCase      =			storage.field("""bytes"""			)
														else:
																				_UpperCAmelCase      =			pa.array([None] * len(_SCREAMING_SNAKE_CASE			)						,		type=pa.binary()			)
														if storage.type.get_field_index("""path"""			) >= 0:
																				_UpperCAmelCase      =			storage.field("""path"""			)
														else:
																				_UpperCAmelCase      =			pa.array([None] * len(_SCREAMING_SNAKE_CASE			)						,		type=pa.string()			)
														_UpperCAmelCase      =			pa.StructArray.from_arrays([bytes_array, path_array]						,		["""bytes""", """path"""]						,		mask=storage.is_null()			)
								elif pa.types.is_list(storage.type			):
														_UpperCAmelCase      =			pa.array(
														    [encode_np_array(np.array(_SCREAMING_SNAKE_CASE			)			)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]						,		type=pa.binary()						,		)
														_UpperCAmelCase      =			pa.array([None] * len(_SCREAMING_SNAKE_CASE			)						,		type=pa.string()			)
														_UpperCAmelCase      =			pa.StructArray.from_arrays(
														    [bytes_array, path_array]						,		["""bytes""", """path"""]						,		mask=bytes_array.is_null()			)
								return array_cast(_SCREAMING_SNAKE_CASE						,		self.pa_type			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):

								@no_op_if_value_is_null
								def path_to_bytes(_SCREAMING_SNAKE_CASE			):
														with xopen(_SCREAMING_SNAKE_CASE						,		"""rb"""			) as f:
																				_UpperCAmelCase      =			f.read()
														return bytes_

								_UpperCAmelCase      =			pa.array(
								    [
								        (path_to_bytes(x["""path"""]			) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
								        for x in storage.to_pylist()
								    ]						,		type=pa.binary()						,		)
								_UpperCAmelCase      =			pa.array(
								    [os.path.basename(_SCREAMING_SNAKE_CASE			) if path is not None else None for path in storage.field("""path"""			).to_pylist()]						,		type=pa.string()						,		)
								_UpperCAmelCase      =			pa.StructArray.from_arrays([bytes_array, path_array]						,		["""bytes""", """path"""]						,		mask=bytes_array.is_null()			)
								return array_cast(_SCREAMING_SNAKE_CASE						,		self.pa_type			)




def        _SCREAMING_SNAKE_CASE      (		)						->				List[str]:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install 'Pillow'."""       )

						global _IMAGE_COMPRESSION_FORMATS
						if _IMAGE_COMPRESSION_FORMATS is None:
												PIL.Image.init()
												_UpperCAmelCase      =			list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
						return _IMAGE_COMPRESSION_FORMATS




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				bytes:
						_UpperCAmelCase      =			BytesIO()
						if image.format in list_image_compression_formats():
												_UpperCAmelCase      =			image.format
						else:
												_UpperCAmelCase      =			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
						image.save(snake_case			,     format=snake_case       )
						return buffer.getvalue()




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				dict:
						if hasattr(snake_case			,     """filename"""       ) and image.filename != "":
												return {"path": image.filename, "bytes": None}
						else:
												return {"path": None, "bytes": image_to_bytes(snake_case       )}




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				dict:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install 'Pillow'."""       )

						_UpperCAmelCase      =			array.dtype
						_UpperCAmelCase      =			dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
						_UpperCAmelCase      =			dtype.kind
						_UpperCAmelCase      =			dtype.itemsize

						_UpperCAmelCase      =			None

						# Multi-channel array case (only np.dtype("|u1") is allowed)
						if array.shape[2:]:
												_UpperCAmelCase      =			np.dtype("""|u1"""       )
												if dtype_kind not in ["u", "i"]:
																		raise TypeError(
																		    f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."       )
												if dtype is not dest_dtype:
																		warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"       )
    # Exact match
						elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
												_UpperCAmelCase      =			dtype
						else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
												while dtype_itemsize >= 1:
																		_UpperCAmelCase      =			dtype_byteorder + dtype_kind + str(snake_case       )
																		_UpperCAmelCase      =			np.dtype(snake_case       )
																		if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
																								warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"       )
																								break
																		else:
																								dtype_itemsize //= 2

						if dest_dtype is None:
												raise TypeError(
												    f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"       )
						_UpperCAmelCase      =			PIL.Image.fromarray(array.astype(snake_case       )       )
						return {"path": None, "bytes": image_to_bytes(snake_case       )}




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[dict]:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install 'Pillow'."""       )

						if objs:
												_UpperCAmelCase    ,					_UpperCAmelCase      =			first_non_null_value(snake_case       )
												if isinstance(snake_case			,     snake_case       ):
																		return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
												if isinstance(snake_case			,     np.ndarray       ):
																		_UpperCAmelCase      =			no_op_if_value_is_null(snake_case       )
																		return [obj_to_image_dict_func(snake_case       ) for obj in objs]
												elif isinstance(snake_case			,     PIL.Image.Image       ):
																		_UpperCAmelCase      =			no_op_if_value_is_null(snake_case       )
																		return [obj_to_image_dict_func(snake_case       ) for obj in objs]
												else:
																		return objs
						else:
												return objs





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class       _A				(		__lowercase     ):
		__a					    =       42




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE=True						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			torch.nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[0]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			nn.ModuleList([]			)

								# down
								_UpperCAmelCase      =			block_out_channels[0]
								for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			block_out_channels[i]
														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_down_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		add_downsample=not is_final_block						,		resnet_eps=1e-6						,		downsample_padding=0						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)
														self.down_blocks.append(_SCREAMING_SNAKE_CASE			)

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default"""						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# out
								_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[-1]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()

								_UpperCAmelCase      =			2 * out_channels if double_z else out_channels
								_UpperCAmelCase      =			nn.Convad(block_out_channels[-1]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			x
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														# down
														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE			)

								else:
														# down
														for down_block in self.down_blocks:
																				_UpperCAmelCase      =			down_block(_SCREAMING_SNAKE_CASE			)

														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE			)

								# post-process
								_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE="group"						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[-1]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			nn.ModuleList([]			)

								_UpperCAmelCase      =			in_channels if norm_type == """spatial""" else None

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# up
								_UpperCAmelCase      =			list(reversed(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			reversed_block_out_channels[0]
								for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			reversed_block_out_channels[i]

														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_up_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block + 1						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		prev_output_channel=_SCREAMING_SNAKE_CASE						,		add_upsample=not is_final_block						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		resnet_time_scale_shift=_SCREAMING_SNAKE_CASE						,		)
														self.up_blocks.append(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			output_channel

								# out
								if norm_type == "spatial":
														_UpperCAmelCase      =			SpatialNorm(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[0]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()
								_UpperCAmelCase      =			nn.Convad(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			z
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			next(iter(self.up_blocks.parameters()			)			).dtype
								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								else:
														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

														# up
														for up_block in self.up_blocks:
																				_UpperCAmelCase      =			up_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

        # post-process
								if latent_embeds is None:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="random"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True			):
								super().__init__()
								_UpperCAmelCase      =			n_e
								_UpperCAmelCase      =			vq_embed_dim
								_UpperCAmelCase      =			beta
								_UpperCAmelCase      =			legacy

								_UpperCAmelCase      =			nn.Embedding(self.n_e						,		self.vq_embed_dim			)
								self.embedding.weight.data.uniform_(-1.0 / self.n_e						,		1.0 / self.n_e			)

								_UpperCAmelCase      =			remap
								if self.remap is not None:
														self.register_buffer("""used"""						,		torch.tensor(np.load(self.remap			)			)			)
														_UpperCAmelCase      =			self.used.shape[0]
														_UpperCAmelCase      =			unknown_index  # "random" or "extra" or integer
														if self.unknown_index == "extra":
																				_UpperCAmelCase      =			self.re_embed
																				_UpperCAmelCase      =			self.re_embed + 1
														print(
														    F"Remapping {self.n_e} indices to {self.re_embed} indices. "
														    F"Using {self.unknown_index} for unknown indices."			)
								else:
														_UpperCAmelCase      =			n_e

								_UpperCAmelCase      =			sane_index_shape






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			(inds[:, :, None] == used[None, None, ...]).long()
								_UpperCAmelCase      =			match.argmax(-1			)
								_UpperCAmelCase      =			match.sum(2			) < 1
								if self.unknown_index == "random":
														_UpperCAmelCase      =			torch.randint(0						,		self.re_embed						,		size=new[unknown].shape			).to(device=new.device			)
								else:
														_UpperCAmelCase      =			self.unknown_index
								return new.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								if self.re_embed > self.used.shape[0]:  # extra token
														_UpperCAmelCase      =			0  # simply set to zero
								_UpperCAmelCase      =			torch.gather(used[None, :][inds.shape[0] * [0], :]						,		1						,		_SCREAMING_SNAKE_CASE			)
								return back.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								# reshape z -> (batch, height, width, channel) and flatten
								_UpperCAmelCase      =			z.permute(0						,		2						,		3						,		1			).contiguous()
								_UpperCAmelCase      =			z.view(-1						,		self.vq_embed_dim			)

								# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
								_UpperCAmelCase      =			torch.argmin(torch.cdist(_SCREAMING_SNAKE_CASE						,		self.embedding.weight			)						,		dim=1			)

								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			).view(z.shape			)
								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			None

								# compute loss for embedding
								if not self.legacy:
														_UpperCAmelCase      =			self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
								else:
														_UpperCAmelCase      =			torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

								# preserve gradients
								_UpperCAmelCase      =			z + (z_q - z).detach()

								# reshape back to match original input shape
								_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								if self.remap is not None:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z.shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.remap_to_used(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			min_encoding_indices.reshape(-1						,		1			)  # flatten

								if self.sane_index_shape:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z_q.shape[0]						,		z_q.shape[2]						,		z_q.shape[3]			)

								return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# shape specifying (batch, height, width, channel)
								if self.remap is not None:
														_UpperCAmelCase      =			indices.reshape(shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.unmap_to_all(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			indices.reshape(-1			)  # flatten again

								# get quantized latent vectors
								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			)

								if shape is not None:
														_UpperCAmelCase      =			z_q.view(_SCREAMING_SNAKE_CASE			)
														# reshape back to match original input shape
														_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								return z_q




class       _A				(		__lowercase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								_UpperCAmelCase      =			parameters
								_UpperCAmelCase    ,					_UpperCAmelCase      =			torch.chunk(_SCREAMING_SNAKE_CASE						,		2						,		dim=1			)
								_UpperCAmelCase      =			torch.clamp(self.logvar						,		-30.0						,		20.0			)
								_UpperCAmelCase      =			deterministic
								_UpperCAmelCase      =			torch.exp(0.5 * self.logvar			)
								_UpperCAmelCase      =			torch.exp(self.logvar			)
								if self.deterministic:
														_UpperCAmelCase      =			_UpperCAmelCase      =			torch.zeros_like(
														    self.mean						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = None			):
								# make sure sample is on the same device as the parameters and has same dtype
								_UpperCAmelCase      =			randn_tensor(
								    self.mean.shape						,		generator=_SCREAMING_SNAKE_CASE						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)
								_UpperCAmelCase      =			self.mean + self.std * sample
								return x






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								else:
														if other is None:
																				return 0.5 * torch.sum(torch.pow(self.mean						,		2			) + self.var - 1.0 - self.logvar						,		dim=[1, 2, 3]			)
														else:
																				return 0.5 * torch.sum(
																				    torch.pow(self.mean - other.mean						,		2			) / other.var
																				    + self.var / other.var
																				    - 1.0
																				    - self.logvar
																				    + other.logvar						,		dim=[1, 2, 3]						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=[1, 2, 3]			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								_UpperCAmelCase      =			np.log(2.0 * np.pi			)
								return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,		2			) / self.var						,		dim=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								return self.mean