



'''simple docstring'''




import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowerCamelCase							:Any				    =					NewType('''DataClass''', Any)
lowerCamelCase							:List[str]				    =					NewType('''DataClassType''', Any)



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise ArgumentTypeError(
						    f'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     {str(lowerCamelCase__    ): choice for choice in choices}
			return lambda lowerCamelCase__    : str_to_choice.get(lowerCamelCase__						,      lowerCamelCase__    )



def 	a			( *,
    lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__ = dataclasses.MISSING						,      lowerCamelCase__ = dataclasses.MISSING						,      lowerCamelCase__ = None						,      **lowerCamelCase__						,      ):
			'''simple docstring'''





			if metadata is None:
						# Important, don't use as default param in function signature because dict is mutable and shared across function calls
						A_	:						int	=     {}
			if aliases is not None:
						A_	:						Optional[Any]	=     aliases
			if help is not None:
						A_	:						Dict	=     help

			return dataclasses.field(metadata=lowerCamelCase__						,      default=lowerCamelCase__						,      default_factory=lowerCamelCase__						,      **lowerCamelCase__    )







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Iterable[DataClassType]


	def __init__(self       ,      lowercase       ,      **lowercase							):
				# To make the default appear when using --help
				if "formatter_class" not in kwargs:
							A_	:						str	=     ArgumentDefaultsHelpFormatter
				super().__init__(**lowercase							)
				if dataclasses.is_dataclass(lowercase							):
							A_	:						Any	=     [dataclass_types]
				A_	:						Any	=     list(lowercase							)
				for dtype in self.dataclass_types:
							self._add_dataclass_arguments(lowercase							)


	@staticmethod
	def     _a	(lowercase       ,      lowercase							):
				A_	:						str	=     F'--{field.name}'
				A_	:						Any	=     field.metadata.copy()
				# field.metadata is not used at all by Data Classes,
				# it is provided as a third-party extension mechanism.
				if isinstance(field.type       ,      lowercase							):
							raise RuntimeError(
							    """Unresolved type detected, which should have been done with the help of """
							    """`typing.get_type_hints` method by default"""							)

				A_	:						str	=     kwargs.pop("""aliases"""       ,      []							)
				if isinstance(lowercase       ,      lowercase							):
							A_	:						Any	=     [aliases]

				A_	:						str	=     getattr(field.type       ,      """__origin__"""       ,      field.type							)
				if origin_type is Union or (hasattr(lowercase       ,      """UnionType"""							) and isinstance(lowercase       ,      types.UnionType							)):
							if str not in field.type.__args__ and (
							    len(field.type.__args__							) != 2 or type(lowercase							) not in field.type.__args__
							):
										raise ValueError(
										    """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
										    """ the argument parser only supports one type per argument."""
										    F' Problem encountered in field \'{field.name}\'.'							)
							if type(lowercase							) not in field.type.__args__:
										# filter `str` in Union
										A_	:						Tuple	=     field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
										A_	:						str	=     getattr(field.type       ,      """__origin__"""       ,      field.type							)
							elif bool not in field.type.__args__:
										# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
										A_	:						List[str]	=     (
										    field.type.__args__[0] if isinstance(lowercase       ,      field.type.__args__[1]							) else field.type.__args__[1]
										)
										A_	:						int	=     getattr(field.type       ,      """__origin__"""       ,      field.type							)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
				A_	:						Union[str, Any]	=     {}
				if origin_type is Literal or (isinstance(field.type       ,      lowercase							) and issubclass(field.type       ,      lowercase							)):
							if origin_type is Literal:
										A_	:						Union[str, Any]	=     field.type.__args__
							else:
										A_	:						Union[str, Any]	=     [x.value for x in field.type]

							A_	:						Union[str, Any]	=     make_choice_type_function(kwargs["""choices"""]							)

							if field.default is not dataclasses.MISSING:
										A_	:						str	=     field.default
							else:
										A_	:						int	=     True
				elif field.type is bool or field.type == Optional[bool]:
							# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
							# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
							A_	:						List[Any]	=     copy(lowercase							)

							# Hack because type=bool in argparse does not behave as we want.
							A_	:						Optional[Any]	=     string_to_bool
							if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
										# Default value is False if we have no default when of type bool.
										A_	:						Optional[int]	=     False if field.default is dataclasses.MISSING else field.default
										# This is the value that will get picked if we don't include --field_name in any way
										A_	:						str	=     default
										# This tells argparse we accept 0 or 1 value after --field_name
										A_	:						List[Any]	=     """?"""
										# This is the value that will get picked if we do --field_name (without value)
										A_	:						Tuple	=     True
				elif isclass(lowercase							) and issubclass(lowercase       ,      lowercase							):
							A_	:						int	=     field.type.__args__[0]
							A_	:						List[str]	=     """+"""
							if field.default_factory is not dataclasses.MISSING:
										A_	:						List[Any]	=     field.default_factory()
							elif field.default is dataclasses.MISSING:
										A_	:						Optional[Any]	=     True
				else:
							A_	:						Optional[int]	=     field.type
							if field.default is not dataclasses.MISSING:
										A_	:						List[Any]	=     field.default
							elif field.default_factory is not dataclasses.MISSING:
										A_	:						str	=     field.default_factory()
							else:
										A_	:						Optional[int]	=     True
				parser.add_argument(lowercase       ,      *lowercase       ,      **lowercase							)

				# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
				# Order is important for arguments with the same destination!
				# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
				# here and we do not need those changes/additional keys.
				if field.default is True and (field.type is bool or field.type == Optional[bool]):
							A_	:						Union[str, Any]	=     False
							parser.add_argument(F'--no_{field.name}'       ,      action="""store_false"""       ,      dest=field.name       ,      **lowercase							)


	def     _a	(self       ,      lowercase							):
				if hasattr(lowercase       ,      """_argument_group_name"""							):
							A_	:						str	=     self.add_argument_group(dtype._argument_group_name							)
				else:
							A_	:						Optional[Any]	=     self

				try:
							A_	:						Dict[str, type]	=     get_type_hints(lowercase							)
				except NameError:
							raise RuntimeError(
							    F'Type resolution failed for {dtype}. Try declaring the class in global scope or '
							    """removing line of `from __future__ import annotations` which opts in Postponed """
							    """Evaluation of Annotations (PEP 563)"""							)
				except TypeError as ex:
							# Remove this block when we drop Python 3.9 support
							if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowercase							):
										A_	:						Union[str, Any]	=     """.""".join(map(lowercase       ,      sys.version_info[:3]							)							)
										raise RuntimeError(
										    F'Type resolution failed for {dtype} on Python {python_version}. Try removing '
										    """line of `from __future__ import annotations` which opts in union types as """
										    """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
										    """support Python versions that lower than 3.10, you need to use """
										    """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
										    """`X | None`."""							) from ex
							raise

				for field in dataclasses.fields(lowercase							):
							if not field.init:
										continue
							A_	:						List[str]	=     type_hints[field.name]
							self._parse_dataclass_field(lowercase       ,      lowercase							)


	def     _a	(self       ,      lowercase=None       ,      lowercase=False       ,      lowercase=True       ,      lowercase=None       ,      lowercase=None       ,      ):

				if args_file_flag or args_filename or (look_for_args_file and len(sys.argv							)):
							A_	:						List[str]	=     []

							if args_filename:
										args_files.append(Path(lowercase							)							)
							elif look_for_args_file and len(sys.argv							):
										args_files.append(Path(sys.argv[0]							).with_suffix(""".args"""							)							)

							# args files specified via command line flag should overwrite default args files so we add them last
							if args_file_flag:
										# Create special parser just to extract the args_file_flag values
										A_	:						Union[str, Any]	=     ArgumentParser()
										args_file_parser.add_argument(lowercase       ,      type=lowercase       ,      action="""append"""							)

										# Use only remaining args for further parsing (remove the args_file_flag)
										A_,    A_	:						Optional[int]	=     args_file_parser.parse_known_args(args=lowercase							)
										A_	:						str	=     vars(lowercase							).get(args_file_flag.lstrip("""-"""							)       ,      lowercase							)

										if cmd_args_file_paths:
													args_files.extend([Path(lowercase							) for p in cmd_args_file_paths]							)

							A_	:						Tuple	=     []
							for args_file in args_files:
										if args_file.exists():
													file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
							A_	:						int	=     file_args + args if args is not None else file_args + sys.argv[1:]
				A_,    A_	:						Any	=     self.parse_known_args(args=lowercase							)
				A_	:						Any	=     []
				for dtype in self.dataclass_types:
							A_	:						Optional[Any]	=     {f.name for f in dataclasses.fields(lowercase							) if f.init}
							A_	:						int	=     {k: v for k, v in vars(lowercase							).items() if k in keys}
							for k in keys:
										delattr(lowercase       ,      lowercase							)
							A_	:						Tuple	=     dtype(**lowercase							)
							outputs.append(lowercase							)
				if len(namespace.__dict__							) > 0:
							# additional namespace.
							outputs.append(lowercase							)
				if return_remaining_strings:
							return (*outputs, remaining_args)
				else:
							if remaining_args:
										raise ValueError(F'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'							)

							return (*outputs,)


	def     _a	(self       ,      lowercase       ,      lowercase = False							):
				A_	:						List[str]	=     set(args.keys()							)
				A_	:						List[Any]	=     []
				for dtype in self.dataclass_types:
							A_	:						Optional[Any]	=     {f.name for f in dataclasses.fields(lowercase							) if f.init}
							A_	:						str	=     {k: v for k, v in args.items() if k in keys}
							unused_keys.difference_update(inputs.keys()							)
							A_	:						str	=     dtype(**lowercase							)
							outputs.append(lowercase							)
				if not allow_extra_keys and unused_keys:
							raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(lowercase							)}'							)
				return tuple(lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase = False							):
				with open(Path(lowercase							)       ,      encoding="""utf-8"""							) as open_json_file:
							A_	:						int	=     json.loads(open_json_file.read()							)
				A_	:						Tuple	=     self.parse_dict(lowercase       ,      allow_extra_keys=lowercase							)
				return tuple(lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase = False							):
				A_	:						List[str]	=     self.parse_dict(yaml.safe_load(Path(lowercase							).read_text()							)       ,      allow_extra_keys=lowercase							)
				return tuple(lowercase							)




'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowerCamelCase							:Any				    =					logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				super().__init__()
				self.register_modules(
				    vae=lowercase       ,      text_encoder=lowercase       ,      tokenizer=lowercase       ,      unet=lowercase       ,      scheduler=lowercase       ,      safety_checker=lowercase       ,      feature_extractor=lowercase       ,      )


	def     _a	(self       ,      lowercase = "auto"							):
				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							A_	:						int	=     self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowercase							)


	def     _a	(self							):
				self.enable_attention_slicing(lowercase							)


	@torch.no_grad()
	def __call__(self       ,      lowercase       ,      lowercase = 512       ,      lowercase = 512       ,      lowercase = 50       ,      lowercase = 7.5       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = 0.0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = "pil"       ,      lowercase = True       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = None       ,      **lowercase       ,      ):

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Union[str, Any]	=     1
				elif isinstance(lowercase       ,      lowercase							):
							A_	:						Any	=     len(lowercase							)
				else:
							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase							)}'							)

				if height % 8 != 0 or width % 8 != 0:
							raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(lowercase       ,      lowercase							) or callback_steps <= 0)
				):
							raise ValueError(
							    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    F' {type(lowercase							)}.'							)

				# get prompt text embeddings
				A_	:						Optional[Any]	=     self.tokenizer(
				    lowercase       ,      padding="""max_length"""       ,      max_length=self.tokenizer.model_max_length       ,      return_tensors="""pt"""       ,      )
				A_	:						Dict	=     text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A_	:						Union[str, Any]	=     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
							A_	:						Dict	=     text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
							A_	:						Any	=     self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				A_,    A_,    A_	:						Tuple	=     text_embeddings.shape
				A_	:						Optional[Any]	=     text_embeddings.repeat(1       ,      lowercase       ,      1							)
				A_	:						Any	=     text_embeddings.view(bs_embed * num_images_per_prompt       ,      lowercase       ,      -1							)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				A_	:						List[str]	=     guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							A_	:						List[str]
							if negative_prompt is None:
										A_	:						Optional[int]	=     [""""""]
							elif type(lowercase							) is not type(lowercase							):
										raise TypeError(
										    F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase							)} !='
										    F' {type(lowercase							)}.'							)
							elif isinstance(lowercase       ,      lowercase							):
										A_	:						Dict	=     [negative_prompt]
							elif batch_size != len(lowercase							):
										raise ValueError(
										    F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase							)}, but `prompt`:'
										    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
										    """ the batch size of `prompt`."""							)
							else:
										A_	:						Dict	=     negative_prompt

							A_	:						int	=     text_input_ids.shape[-1]
							A_	:						List[Any]	=     self.tokenizer(
							    lowercase       ,      padding="""max_length"""       ,      max_length=lowercase       ,      truncation=lowercase       ,      return_tensors="""pt"""       ,      )
							A_	:						str	=     self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A_	:						Optional[Any]	=     uncond_embeddings.shape[1]
							A_	:						str	=     uncond_embeddings.repeat(lowercase       ,      lowercase       ,      1							)
							A_	:						List[str]	=     uncond_embeddings.view(batch_size * num_images_per_prompt       ,      lowercase       ,      -1							)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							A_	:						Dict	=     torch.cat([uncond_embeddings, text_embeddings]							)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				A_	:						int	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				A_	:						Dict	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				A_	:						Dict	=     text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not exist on mps
										A_	:						Tuple	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(self.device							)
										A_	:						int	=     torch.randn(lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(
										    self.device							)
							else:
										A_	:						int	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
										A_	:						str	=     torch.randn(lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
				else:
							if latents_reference.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
							A_	:						str	=     latents_reference.to(self.device							)
							A_	:						Tuple	=     latents.to(self.device							)

				# This is the key part of the pipeline where we
				# try to ensure that the generated images w/ the same seed
				# but different sizes actually result in similar images
				A_	:						Optional[int]	=     (latents_shape[3] - latents_shape_reference[3]) // 2
				A_	:						Optional[int]	=     (latents_shape[2] - latents_shape_reference[2]) // 2
				A_	:						Optional[int]	=     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				A_	:						int	=     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				A_	:						Optional[Any]	=     0 if dx < 0 else dx
				A_	:						Optional[Any]	=     0 if dy < 0 else dy
				A_	:						Optional[int]	=     max(-dx       ,      0							)
				A_	:						List[str]	=     max(-dy       ,      0							)
				# import pdb
				# pdb.set_trace()
				A_	:						str	=     latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(lowercase							)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				A_	:						Any	=     self.scheduler.timesteps.to(self.device							)

				# scale the initial noise by the standard deviation required by the scheduler
				A_	:						Tuple	=     latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				A_	:						Dict	=     """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
				A_	:						Any	=     {}
				if accepts_eta:
							A_	:						Optional[int]	=     eta

				for i, t in enumerate(self.progress_bar(lowercase							)							):
							# expand the latents if we are doing classifier free guidance
							A_	:						Tuple	=     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
							A_	:						Tuple	=     self.scheduler.scale_model_input(lowercase       ,      lowercase							)

							# predict the noise residual
							A_	:						List[str]	=     self.unet(lowercase       ,      lowercase       ,      encoder_hidden_states=lowercase							).sample

							# perform guidance
							if do_classifier_free_guidance:
										A_,    A_	:						str	=     noise_pred.chunk(2							)
										A_	:						Optional[Any]	=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							A_	:						List[str]	=     self.scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
										callback(lowercase       ,      lowercase       ,      lowercase							)

				A_	:						List[str]	=     1 / 0.1_82_15 * latents
				A_	:						List[str]	=     self.vae.decode(lowercase							).sample

				A_	:						Optional[int]	=     (image / 2 + 0.5).clamp(0       ,      1							)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				A_	:						Optional[int]	=     image.cpu().permute(0       ,      2       ,      3       ,      1							).float().numpy()

				if self.safety_checker is not None:
							A_	:						Union[str, Any]	=     self.feature_extractor(self.numpy_to_pil(lowercase							)       ,      return_tensors="""pt"""							).to(
							    self.device							)
							A_,    A_	:						Optional[int]	=     self.safety_checker(
							    images=lowercase       ,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
				else:
							A_	:						Tuple	=     None

				if output_type == "pil":
							A_	:						Tuple	=     self.numpy_to_pil(lowercase							)

				if not return_dict:
							return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=lowercase       ,      nsfw_content_detected=lowercase							)