
'''simple docstring'''

import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


UpperCAmelCase__				:	List[str]					=						logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
UpperCAmelCase__				:	Dict					=						direct_transformers_import(Path(__file__).parent)


UpperCAmelCase__				:	int					=						{
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :List[Any]				       =       ['feature_extractor', 'tokenizer']
	# Names need to be attr_class for attr in attributes
	snake_case__ :Optional[Any]				       =       None
	snake_case__ :Any				       =       None
	snake_case__ :Optional[Any]				       =       None





	def __init__(   self     :    List[Any] ,							*__magic_name__     :    str ,							**__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				for key in kwargs:
							if key not in self.attributes:
										raise TypeError(f"""Unexpected keyword argument {key}."""							)
				for arg, attribute_name in zip(__magic_name__ ,							self.attributes							):
							if attribute_name in kwargs:
										raise TypeError(f"""Got multiple values for argument {attribute_name}."""							)
							else:
										lowerCAmelCase__									=							arg

				if len(__magic_name__							) != len(self.attributes							):
							raise ValueError(
							    f"""This processor requires {len(self.attributes							)} arguments: {", ".join(self.attributes							)}. Got """
							    f"""{len(__magic_name__							)} arguments instead."""							)

				# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
				for attribute_name, arg in kwargs.items():
							lowerCAmelCase__									=							getattr(self ,							f"""{attribute_name}_class"""							)
							# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
							lowerCAmelCase__									=							AUTO_TO_BASE_CLASS_MAPPING.get(__magic_name__ ,							__magic_name__							)
							if isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							tuple(getattr(__magic_name__ ,							__magic_name__							) for n in class_name if n is not None							)
							else:
										lowerCAmelCase__									=							getattr(__magic_name__ ,							__magic_name__							)

							if not isinstance(__magic_name__ ,							__magic_name__							):
										raise ValueError(
										    f"""Received a {type(__magic_name__							).__name__} for argument {attribute_name}, but a {class_name} was expected."""							)

							setattr(self ,							__magic_name__ ,							__magic_name__							)





	def __repr__(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							[f"""- {name}: {repr(getattr(self ,							__magic_name__							)							)}""" for name in self.attributes]
				lowerCAmelCase__									=							"\n".join(__magic_name__							)
				return f"""{self.__class__.__name__}:\n{attributes_repr}"""





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    bool = False ,							**__magic_name__     :    Dict							):


				"""simple docstring"""

				os.makedirs(__magic_name__ ,							exist_ok=__magic_name__							)

				if push_to_hub:
							lowerCAmelCase__									=							kwargs.pop("commit_message" ,							__magic_name__							)
							lowerCAmelCase__									=							kwargs.pop("repo_id" ,							save_directory.split(os.path.sep							)[-1]							)
							lowerCAmelCase__									=							self._create_repo(__magic_name__ ,							**__magic_name__							)
							lowerCAmelCase__									=							self._get_files_timestamps(__magic_name__							)
				# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
				# loaded from the Hub.
				if self._auto_class is not None:
							lowerCAmelCase__									=							[getattr(self ,							__magic_name__							) for attribute_name in self.attributes]
							lowerCAmelCase__									=							[(a.init_kwargs if isinstance(__magic_name__ ,							__magic_name__							) else a) for a in attrs]
							custom_object_save(self ,							__magic_name__ ,							config=__magic_name__							)

				for attribute_name in self.attributes:
							lowerCAmelCase__									=							getattr(self ,							__magic_name__							)
							# Include the processor class in the attribute config so this processor can then be reloaded with the
							# `AutoProcessor` API.
							if hasattr(__magic_name__ ,							"_set_processor_class"							):
										attribute._set_processor_class(self.__class__.__name__							)
							attribute.save_pretrained(__magic_name__							)

				if self._auto_class is not None:
							# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
							for attribute_name in self.attributes:
										lowerCAmelCase__									=							getattr(self ,							__magic_name__							)
										if isinstance(__magic_name__ ,							__magic_name__							):
													del attribute.init_kwargs["auto_map"]

				if push_to_hub:
							self._upload_modified_files(
							    __magic_name__ ,							__magic_name__ ,							__magic_name__ ,							commit_message=__magic_name__ ,							token=kwargs.get("use_auth_token"							) ,							)





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    List[Any] ,							__magic_name__     :    Union[str, os.PathLike] ,							__magic_name__     :    Optional[Union[str, os.PathLike]] = None ,							__magic_name__     :    bool = False ,							__magic_name__     :    bool = False ,							__magic_name__     :    Optional[Union[str, bool]] = None ,							__magic_name__     :    str = "main" ,							**__magic_name__     :    Optional[Any] ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							cache_dir
				lowerCAmelCase__									=							force_download
				lowerCAmelCase__									=							local_files_only
				lowerCAmelCase__									=							revision

				lowerCAmelCase__									=							kwargs.pop("use_auth_token" ,							__magic_name__							)
				if use_auth_token is not None:
							warnings.warn(
							    "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers." ,							__magic_name__							)
							if token is not None:
										raise ValueError(
										    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."							)
							lowerCAmelCase__									=							use_auth_token

				if token is not None:
							# change to `token` in a follow-up PR
							lowerCAmelCase__									=							token

				lowerCAmelCase__									=							cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__							)
				return cls(*__magic_name__							)





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    List[str] ,							__magic_name__     :    Optional[int]="AutoProcessor"							):


				"""simple docstring"""

				if not isinstance(__magic_name__ ,							__magic_name__							):
							lowerCAmelCase__									=							auto_class.__name__

				import transformers.models.auto as auto_module

				if not hasattr(__magic_name__ ,							__magic_name__							):
							raise ValueError(f"""{auto_class} is not a valid auto class."""							)

				lowerCAmelCase__									=							auto_class





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    Union[str, Any] ,							__magic_name__     :    Tuple ,							**__magic_name__     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for attribute_name in cls.attributes:
							lowerCAmelCase__									=							getattr(cls ,							f"""{attribute_name}_class"""							)
							if isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							tuple(getattr(__magic_name__ ,							__magic_name__							) if n is not None else None for n in class_name							)
										lowerCAmelCase__									=							kwargs.get("use_fast" ,							__magic_name__							)
										if use_fast and classes[1] is not None:
													lowerCAmelCase__									=							classes[1]
										else:
													lowerCAmelCase__									=							classes[0]
							else:
										lowerCAmelCase__									=							getattr(__magic_name__ ,							__magic_name__							)

							args.append(attribute_class.from_pretrained(__magic_name__ ,							**__magic_name__							)							)
				return args







	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							getattr(self ,							self.attributes[0]							)
				return getattr(__magic_name__ ,							"model_input_names" ,							__magic_name__							)

UpperCAmelCase__				:	List[Any]					=						copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
		UpperCAmelCase__				:	Optional[Any]					=						ProcessorMixin.push_to_hub.__doc__.format(
		    object="processor", object_class="AutoProcessor", object_files="processor files"
		)







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :torch.FloatTensor






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





	@register_to_config
	def __init__(   self     :    Tuple ,							__magic_name__     :    Optional[Union[int, Tuple[int, int]]] = None ,							__magic_name__     :    int = 3 ,							__magic_name__     :    int = 3 ,							__magic_name__     :    bool = False ,							__magic_name__     :    str = "positional" ,							__magic_name__     :    int = 0 ,							__magic_name__     :    bool = True ,							__magic_name__     :    Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,							__magic_name__     :    Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,							__magic_name__     :    Tuple[int] = (224, 448, 672, 896) ,							__magic_name__     :    int = 2 ,							__magic_name__     :    float = 1 ,							__magic_name__     :    int = 1 ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "silu" ,							__magic_name__     :    Optional[int] = 8 ,							__magic_name__     :    int = 32 ,							__magic_name__     :    float = 1E-5 ,							__magic_name__     :    str = "default" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[str] = None ,							__magic_name__     :    Optional[int] = None ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							sample_size
				lowerCAmelCase__									=							block_out_channels[0] * 4

				# Check inputs
				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""							)

				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""							)

				# input
				lowerCAmelCase__									=							nn.Convad(__magic_name__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)							)

				# time
				if time_embedding_type == "fourier":
							lowerCAmelCase__									=							GaussianFourierProjection(embedding_size=block_out_channels[0] ,							scale=16							)
							lowerCAmelCase__									=							2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							lowerCAmelCase__									=							Timesteps(block_out_channels[0] ,							__magic_name__ ,							__magic_name__							)
							lowerCAmelCase__									=							block_out_channels[0]

				lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)

				# class embedding
				if class_embed_type is None and num_class_embeds is not None:
							lowerCAmelCase__									=							nn.Embedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "timestep":
							lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "identity":
							lowerCAmelCase__									=							nn.Identity(__magic_name__ ,							__magic_name__							)
				else:
							lowerCAmelCase__									=							None

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

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

							lowerCAmelCase__									=							get_down_block(
							    __magic_name__ ,							num_layers=__magic_name__ ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							temb_channels=__magic_name__ ,							add_downsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							downsample_padding=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							downsample_type=__magic_name__ ,							)
							self.down_blocks.append(__magic_name__							)

				# mid
				lowerCAmelCase__									=							UNetMidBlockaD(
				    in_channels=block_out_channels[-1] ,							temb_channels=__magic_name__ ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							output_scale_factor=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1] ,							resnet_groups=__magic_name__ ,							add_attention=__magic_name__ ,							)

				# up
				lowerCAmelCase__									=							list(reversed(__magic_name__							)							)
				lowerCAmelCase__									=							reversed_block_out_channels[0]
				for i, up_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							reversed_block_out_channels[i]
							lowerCAmelCase__									=							reversed_block_out_channels[min(i + 1 ,							len(__magic_name__							) - 1							)]

							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_up_block(
							    __magic_name__ ,							num_layers=layers_per_block + 1 ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							prev_output_channel=__magic_name__ ,							temb_channels=__magic_name__ ,							add_upsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							resnet_time_scale_shift=__magic_name__ ,							upsample_type=__magic_name__ ,							)
							self.up_blocks.append(__magic_name__							)
							lowerCAmelCase__									=							output_channel

				# out
				lowerCAmelCase__									=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							32							)
				lowerCAmelCase__									=							nn.GroupNorm(num_channels=block_out_channels[0] ,							num_groups=__magic_name__ ,							eps=__magic_name__							)
				lowerCAmelCase__									=							nn.SiLU()
				lowerCAmelCase__									=							nn.Convad(block_out_channels[0] ,							__magic_name__ ,							kernel_size=3 ,							padding=1							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    Union[torch.Tensor, float, int] ,							__magic_name__     :    Optional[torch.Tensor] = None ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				if self.config.center_input_sample:
							lowerCAmelCase__									=							2 * sample - 1.0

				# 1. time
				lowerCAmelCase__									=							timestep
				if not torch.is_tensor(__magic_name__							):
							lowerCAmelCase__									=							torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device							)
				elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
							lowerCAmelCase__									=							timesteps[None].to(sample.device							)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				lowerCAmelCase__									=							timesteps * torch.ones(sample.shape[0] ,							dtype=timesteps.dtype ,							device=timesteps.device							)

				lowerCAmelCase__									=							self.time_proj(__magic_name__							)

				# timesteps does not contain any weights and will always return f32 tensors
				# but time_embedding might actually be running in fp16. so we need to cast here.
				# there might be better ways to encapsulate this.
				lowerCAmelCase__									=							t_emb.to(dtype=self.dtype							)
				lowerCAmelCase__									=							self.time_embedding(__magic_name__							)

				if self.class_embedding is not None:
							if class_labels is None:
										raise ValueError("class_labels should be provided when doing class conditioning"							)

							if self.config.class_embed_type == "timestep":
										lowerCAmelCase__									=							self.time_proj(__magic_name__							)

							lowerCAmelCase__									=							self.class_embedding(__magic_name__							).to(dtype=self.dtype							)
							lowerCAmelCase__									=							emb + class_emb

				# 2. pre-process
				lowerCAmelCase__									=							sample
				lowerCAmelCase__									=							self.conv_in(__magic_name__							)

				# 3. down
				lowerCAmelCase__									=							(sample,)
				for downsample_block in self.down_blocks:
							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(
										    hidden_states=__magic_name__ ,							temb=__magic_name__ ,							skip_sample=__magic_name__							)
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(hidden_states=__magic_name__ ,							temb=__magic_name__							)

							down_block_res_samples += res_samples

				# 4. mid
				lowerCAmelCase__									=							self.mid_block(__magic_name__ ,							__magic_name__							)

				# 5. up
				lowerCAmelCase__									=							None
				for upsample_block in self.up_blocks:
							lowerCAmelCase__									=							down_block_res_samples[-len(upsample_block.resnets							) :]
							lowerCAmelCase__									=							down_block_res_samples[: -len(upsample_block.resnets							)]

							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							else:
										lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

        # 6. post-process
				lowerCAmelCase__									=							self.conv_norm_out(__magic_name__							)
				lowerCAmelCase__									=							self.conv_act(__magic_name__							)
				lowerCAmelCase__									=							self.conv_out(__magic_name__							)

				if skip_sample is not None:
							sample += skip_sample

				if self.config.time_embedding_type == "fourier":
							lowerCAmelCase__									=							timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
							lowerCAmelCase__									=							sample / timesteps

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__magic_name__							)





