




"""simple docstring"""

import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
		from .testing_utils import (
		    floats_tensor,
		    load_hf_numpy,
		    load_image,
		    load_numpy,
		    load_pt,
		    nightly,
		    parse_flag_from_env,
		    print_tensor_test,
		    require_torch_a,
		    require_torch_gpu,
		    skip_mps,
		    slow,
		    torch_all_close,
		    torch_device,
		)
		from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCamelCase__ :    Union[str, Any] 					=							get_logger(__name__)




def       snake_case__	(       lowerCAmelCase   :		Union[str, Any]		):
					if version.parse(lowerCAmelCase		) < version.parse(lowerCAmelCase		):
										if "dev" in min_version:
															__snake_case          =	(
															    """This example requires a source install from HuggingFace diffusers (see """
															    """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
															)
										else:
															__snake_case          =	F'''This example requires a minimum version of {min_version},'''
										error_message += F''' but the version found is {__version__}.\n'''
										raise ImportError(lowerCAmelCase		)








"""simple docstring"""

from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize
class     A__      (      lowercase_     ):
	A_							:				Tuple						     =      1
	A_							:				Union[str, Any]						     =      2
	A_							:				Union[str, Any]						     =      3




	def __repr__(   self  :	Dict	)					-> Union[str, Any]:
						return f'''{self.__class__.__name__}.{self.name}'''




def       snake_case__	(       lowerCAmelCase   :		tuple[int, int]				,					lowerCAmelCase   :		int				,					lowerCAmelCase   :		int		):
					__snake_case      ,      __snake_case          =	point
					return degrees(atana(y - miny				,					x - minx		)		)




def       snake_case__	(       lowerCAmelCase   :		tuple[int, int]				,					lowerCAmelCase   :		tuple[int, int]				,					lowerCAmelCase   :		tuple[int, int]		):
					__snake_case      ,      __snake_case          =	starting
					__snake_case      ,      __snake_case          =	via
					__snake_case      ,      __snake_case          =	target
					__snake_case          =	degrees(atana(ya - ya				,					xa - xa		)		)
					via_angle %= 360
					__snake_case          =	degrees(atana(ya - ya				,					xa - xa		)		)
					target_angle %= 360
					# t-
					#  \ \
					#   \ v
					#    \|
					#     s
					# via_angle is always lower than target_angle, if direction is left.
					# If they are same, it means they are on a same line of convex hull.
					if target_angle > via_angle:
										return Direction.left
					elif target_angle == via_angle:
										return Direction.straight
					else:
										return Direction.right




def       snake_case__	(       lowerCAmelCase   :		list[tuple[int, int]]		):

					if len(lowerCAmelCase		) <= 2:
										# There is no convex hull
										raise ValueError("""graham_scan: argument must contain more than 3 points."""		)
					if len(lowerCAmelCase		) == 3:
										return points
					# find the lowest and the most left point
					__snake_case          =	0
					__snake_case      ,      __snake_case          =	maxsize, maxsize
					for i, point in enumerate(lowerCAmelCase		):
										__snake_case          =	point[0]
										__snake_case          =	point[1]
										if y < miny:
															__snake_case          =	y
															__snake_case          =	x
															__snake_case          =	i
										if y == miny and x < minx:
															__snake_case          =	x
															__snake_case          =	i

    # remove the lowest and the most left point from points for preparing for sort
					points.pop(lowerCAmelCase		)

					__snake_case          =	sorted(lowerCAmelCase				,					key=lambda lowerCAmelCase		: angle_comparer(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)		)
					# This insert actually costs complexity,
					# and you should instead add (minx, miny) into stack later.
					# I'm using insert just for easy understanding.
					sorted_points.insert(0				,					(minx, miny)		)

					__snake_case          =	deque()
					stack.append(sorted_points[0]		)
					stack.append(sorted_points[1]		)
					stack.append(sorted_points[2]		)
					# In any ways, the first 3 points line are towards left.
					# Because we sort them the angle from minx, miny.
					__snake_case          =	Direction.left

					for i in range(3				,					len(lowerCAmelCase		)		):
										while True:
															__snake_case          =	stack[-2]
															__snake_case          =	stack[-1]
															__snake_case          =	sorted_points[i]
															__snake_case          =	check_direction(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)

															if next_direction == Direction.left:
																				__snake_case          =	Direction.left
																				break
															if next_direction == Direction.straight:
																				if current_direction == Direction.left:
																									# We keep current_direction as left.
																									# Because if the straight line keeps as straight,
																									# we want to know if this straight line is towards left.
																									break
																				elif current_direction == Direction.right:
																									# If the straight line is towards right,
																									# every previous points on those straigh line is not convex hull.
																									stack.pop()
															if next_direction == Direction.right:
																				stack.pop()
										stack.append(sorted_points[i]		)
					return list(lowerCAmelCase		)


