






"""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_							      = NewType('''DataClass''', Any)
lowerCamelCase_							      = NewType('''DataClassType''', Any)

def 							snake_case (  A__		):
			if isinstance(A__      ,A__		):
						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 							snake_case (  A__		):
			UpperCAmelCase_	:					Optional[Any]     	=   {str(A__		): choice for choice in choices}
			return lambda A__		: str_to_choice.get(A__      ,A__		)

def 							snake_case (  *,
    A__ = None      ,A__ = None      ,A__ = dataclasses.MISSING      ,A__ = dataclasses.MISSING      ,A__ = None      ,**A__      ,):
			if metadata is None:
						# Important, don't use as default param in function signature because dict is mutable and shared across function calls
						UpperCAmelCase_	:					int     	=   {}
			if aliases is not None:
						UpperCAmelCase_	:					List[str]     	=   aliases
			if help is not None:
						UpperCAmelCase_	:					Optional[int]     	=   help

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







class      UpperCamelCase_			(__A	):
	__magic_name__       =							42





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Union[DataClassType, Iterable[DataClassType]]      ,      **lowerCAmelCase_   :					str		)		->     Optional[Any]:
				# To make the default appear when using --help
				if "formatter_class" not in kwargs:
							UpperCAmelCase_	:					Tuple     	=   ArgumentDefaultsHelpFormatter
				super().__init__(**lowerCAmelCase_		)
				if dataclasses.is_dataclass(lowerCAmelCase_		):
							UpperCAmelCase_	:					Optional[Any]     	=   [dataclass_types]
				UpperCAmelCase_	:					int     	=   list(lowerCAmelCase_		)
				for dtype in self.dataclass_types:
							self._add_dataclass_arguments(lowerCAmelCase_		)





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					ArgumentParser      ,      lowerCAmelCase_   :					dataclasses.Field		)		->     Tuple:
				UpperCAmelCase_	:					Any     	=   f"""--{field.name}"""
				UpperCAmelCase_	:					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      ,      lowerCAmelCase_		):
							raise RuntimeError(
							    "Unresolved type detected, which should have been done with the help of "
							    "`typing.get_type_hints` method by default"		)

				UpperCAmelCase_	:					Optional[Any]     	=   kwargs.pop("aliases"      ,      []		)
				if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					Any     	=   [aliases]

				UpperCAmelCase_	:					Tuple     	=   getattr(field.type      ,      "__origin__"      ,      field.type		)
				if origin_type is Union or (hasattr(lowerCAmelCase_      ,      "UnionType"		) and isinstance(lowerCAmelCase_      ,      types.UnionType		)):
							if str not in field.type.__args__ and (
							    len(field.type.__args__		) != 2 or type(lowerCAmelCase_		) 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(lowerCAmelCase_		) not in field.type.__args__:
										# filter `str` in Union
										UpperCAmelCase_	:					Optional[Any]     	=   field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
										UpperCAmelCase_	:					int     	=   getattr(field.type      ,      "__origin__"      ,      field.type		)
							elif bool not in field.type.__args__:
										# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
										UpperCAmelCase_	:					Any     	=   (
										    field.type.__args__[0] if isinstance(lowerCAmelCase_      ,      field.type.__args__[1]		) else field.type.__args__[1]
										)
										UpperCAmelCase_	:					Optional[Any]     	=   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)
				UpperCAmelCase_	:					List[Any]     	=   {}
				if origin_type is Literal or (isinstance(field.type      ,      lowerCAmelCase_		) and issubclass(field.type      ,      lowerCAmelCase_		)):
							if origin_type is Literal:
										UpperCAmelCase_	:					str     	=   field.type.__args__
							else:
										UpperCAmelCase_	:					List[Any]     	=   [x.value for x in field.type]

							UpperCAmelCase_	:					Optional[int]     	=   make_choice_type_function(kwargs["choices"]		)

							if field.default is not dataclasses.MISSING:
										UpperCAmelCase_	:					Dict     	=   field.default
							else:
										UpperCAmelCase_	:					str     	=   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
							UpperCAmelCase_	:					Any     	=   copy(lowerCAmelCase_		)

							# Hack because type=bool in argparse does not behave as we want.
							UpperCAmelCase_	:					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.
										UpperCAmelCase_	:					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
										UpperCAmelCase_	:					int     	=   default
										# This tells argparse we accept 0 or 1 value after --field_name
										UpperCAmelCase_	:					Union[str, Any]     	=   "?"
										# This is the value that will get picked if we do --field_name (without value)
										UpperCAmelCase_	:					List[Any]     	=   True
				elif isclass(lowerCAmelCase_		) and issubclass(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					List[str]     	=   field.type.__args__[0]
							UpperCAmelCase_	:					int     	=   "+"
							if field.default_factory is not dataclasses.MISSING:
										UpperCAmelCase_	:					Union[str, Any]     	=   field.default_factory()
							elif field.default is dataclasses.MISSING:
										UpperCAmelCase_	:					List[Any]     	=   True
				else:
							UpperCAmelCase_	:					Tuple     	=   field.type
							if field.default is not dataclasses.MISSING:
										UpperCAmelCase_	:					Any     	=   field.default
							elif field.default_factory is not dataclasses.MISSING:
										UpperCAmelCase_	:					str     	=   field.default_factory()
							else:
										UpperCAmelCase_	:					Union[str, Any]     	=   True
				parser.add_argument(lowerCAmelCase_      ,      *lowerCAmelCase_      ,      **lowerCAmelCase_		)

				# 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]):
							UpperCAmelCase_	:					Union[str, Any]     	=   False
							parser.add_argument(f"""--no_{field.name}"""      ,      action="store_false"      ,      dest=field.name      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					DataClassType		)		->     int:
				if hasattr(lowerCAmelCase_      ,      "_argument_group_name"		):
							UpperCAmelCase_	:					Union[str, Any]     	=   self.add_argument_group(dtype._argument_group_name		)
				else:
							UpperCAmelCase_	:					Tuple     	=   self

				try:
							UpperCAmelCase_	:					Dict[str, type]     	=   get_type_hints(lowerCAmelCase_		)
				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(lowerCAmelCase_		):
										UpperCAmelCase_	:					Union[str, Any]     	=   ".".join(map(lowerCAmelCase_      ,      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(lowerCAmelCase_		):
							if not field.init:
										continue
							UpperCAmelCase_	:					Optional[Any]     	=   type_hints[field.name]
							self._parse_dataclass_field(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str=None      ,      lowerCAmelCase_   :					Tuple=False      ,      lowerCAmelCase_   :					int=True      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					List[Any]=None      ,      )		->     Tuple[DataClass, ...]:

				if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
							UpperCAmelCase_	:					Dict     	=   []

							if args_filename:
										args_files.append(Path(lowerCAmelCase_		)		)
							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
										UpperCAmelCase_	:					int     	=   ArgumentParser()
										args_file_parser.add_argument(lowerCAmelCase_      ,      type=lowerCAmelCase_      ,      action="append"		)

										# Use only remaining args for further parsing (remove the args_file_flag)
										UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   args_file_parser.parse_known_args(args=lowerCAmelCase_		)
										UpperCAmelCase_	:					Tuple     	=   vars(lowerCAmelCase_		).get(args_file_flag.lstrip("-"		)      ,      lowerCAmelCase_		)

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

							UpperCAmelCase_	:					Dict     	=   []
							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
							UpperCAmelCase_	:					Union[str, Any]     	=   file_args + args if args is not None else file_args + sys.argv[1:]
				UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   self.parse_known_args(args=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   []
				for dtype in self.dataclass_types:
							UpperCAmelCase_	:					Union[str, Any]     	=   {f.name for f in dataclasses.fields(lowerCAmelCase_		) if f.init}
							UpperCAmelCase_	:					Optional[int]     	=   {k: v for k, v in vars(lowerCAmelCase_		).items() if k in keys}
							for k in keys:
										delattr(lowerCAmelCase_      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					Tuple     	=   dtype(**lowerCAmelCase_		)
							outputs.append(lowerCAmelCase_		)
				if len(namespace.__dict__		) > 0:
							# additional namespace.
							outputs.append(lowerCAmelCase_		)
				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 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					Dict[str, Any]      ,      lowerCAmelCase_   :					bool = False		)		->     Tuple[DataClass, ...]:
				UpperCAmelCase_	:					Dict     	=   set(args.keys()		)
				UpperCAmelCase_	:					Dict     	=   []
				for dtype in self.dataclass_types:
							UpperCAmelCase_	:					Dict     	=   {f.name for f in dataclasses.fields(lowerCAmelCase_		) if f.init}
							UpperCAmelCase_	:					Dict     	=   {k: v for k, v in args.items() if k in keys}
							unused_keys.difference_update(inputs.keys()		)
							UpperCAmelCase_	:					List[str]     	=   dtype(**lowerCAmelCase_		)
							outputs.append(lowerCAmelCase_		)
				if not allow_extra_keys and unused_keys:
							raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(lowerCAmelCase_		)}"""		)
				return tuple(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					bool = False		)		->     Tuple[DataClass, ...]:
				with open(Path(lowerCAmelCase_		)      ,      encoding="utf-8"		) as open_json_file:
							UpperCAmelCase_	:					List[Any]     	=   json.loads(open_json_file.read()		)
				UpperCAmelCase_	:					Any     	=   self.parse_dict(lowerCAmelCase_      ,      allow_extra_keys=lowerCAmelCase_		)
				return tuple(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					bool = False		)		->     Tuple[DataClass, ...]:
				UpperCAmelCase_	:					Optional[int]     	=   self.parse_dict(yaml.safe_load(Path(lowerCAmelCase_		).read_text()		)      ,      allow_extra_keys=lowerCAmelCase_		)
				return tuple(lowerCAmelCase_		)










import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      UNetaDModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
