


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  (DPMSolverSinglestepScheduler,)
							__UpperCAmelCase   :							str					  =  (("num_inference_steps", 25),)

							def    __snake_case (							self	:  Dict   ,     **lowerCamelCase	:  Any    )     ->   str:
														__snake_case		:      Union[str, Any]										=       {
														    "num_train_timesteps": 1000,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														    "solver_order": 2,
														    "prediction_type": "epsilon",
														    "thresholding": False,
														    "sample_max_value": 1.0,
														    "algorithm_type": "dpmsolver++",
														    "solver_type": "midpoint",
														    "lambda_min_clipped": -float("inf"    ),
														    "variance_type": None,
														}

														config.update(**lowerCamelCase    )
														return config

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Union[str, Any]=0   ,     **lowerCamelCase	:  Optional[int]    )     ->   Optional[Any]:
														__snake_case		:      List[Any]										=       dict(self.forward_default_kwargs    )
														__snake_case		:      Any										=       kwargs.pop("num_inference_steps"   ,     lowerCamelCase    )
														__snake_case		:      List[str]										=       self.dummy_sample
														__snake_case		:      Union[str, Any]										=       0.1 * sample
														__snake_case		:      Dict										=       [residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					__snake_case		:      Union[str, Any]										=       self.get_scheduler_config(**lowerCamelCase    )
																					__snake_case		:      Tuple										=       scheduler_class(**lowerCamelCase    )
																					scheduler.set_timesteps(lowerCamelCase    )
																					# copy over dummy past residuals
																					__snake_case		:      Tuple										=       dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase    )
																												__snake_case		:      Optional[Any]										=       scheduler_class.from_pretrained(lowerCamelCase    )
																												new_scheduler.set_timesteps(lowerCamelCase    )
																												# copy over dummy past residuals
																												__snake_case		:      Any										=       dummy_past_residuals[: new_scheduler.config.solver_order]

																					__snake_case		:      Any										=       sample, sample
																					for t in range(lowerCamelCase   ,     time_step + scheduler.config.solver_order + 1    ):
																												__snake_case		:      List[str]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    ).prev_sample
																												__snake_case		:      Optional[Any]										=       new_scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    ).prev_sample

																												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

							def    __snake_case (							self	:  List[str]    )     ->   Dict:
														pass

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[Any]=0   ,     **lowerCamelCase	:  Optional[int]    )     ->   int:
														__snake_case		:      Dict										=       dict(self.forward_default_kwargs    )
														__snake_case		:      Optional[Any]										=       kwargs.pop("num_inference_steps"   ,     lowerCamelCase    )
														__snake_case		:      List[Any]										=       self.dummy_sample
														__snake_case		:      List[Any]										=       0.1 * sample
														__snake_case		:      Tuple										=       [residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					__snake_case		:      Any										=       self.get_scheduler_config()
																					__snake_case		:      Union[str, Any]										=       scheduler_class(**lowerCamelCase    )
																					scheduler.set_timesteps(lowerCamelCase    )

																					# copy over dummy past residuals (must be after setting timesteps)
																					__snake_case		:      List[Any]										=       dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase    )
																												__snake_case		:      List[str]										=       scheduler_class.from_pretrained(lowerCamelCase    )
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(lowerCamelCase    )

																												# copy over dummy past residual (must be after setting timesteps)
																												__snake_case		:      Any										=       dummy_past_residuals[: new_scheduler.config.solver_order]

																					__snake_case		:      Optional[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    ).prev_sample
																					__snake_case		:      int										=       new_scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    ).prev_sample

																					assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Dict=None   ,     **lowerCamelCase	:  str    )     ->   Dict:
														if scheduler is None:
																					__snake_case		:      List[Any]										=       self.scheduler_classes[0]
																					__snake_case		:      List[str]										=       self.get_scheduler_config(**lowerCamelCase    )
																					__snake_case		:      List[Any]										=       scheduler_class(**lowerCamelCase    )

														__snake_case		:      Optional[int]										=       self.scheduler_classes[0]
														__snake_case		:      Optional[int]										=       self.get_scheduler_config(**lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       scheduler_class(**lowerCamelCase    )

														__snake_case		:      Optional[int]										=       10
														__snake_case		:      List[Any]										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase    )

														for i, t in enumerate(scheduler.timesteps    ):
																					__snake_case		:      Union[str, Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      List[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														return sample

							def    __snake_case (							self	:  str    )     ->   int:
														__snake_case		:      Union[str, Any]										=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
														__snake_case		:      Union[str, Any]										=       50
														__snake_case		:      Optional[Any]										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase    )

														# make sure that the first t is uneven
														for i, t in enumerate(scheduler.timesteps[3:]    ):
																					__snake_case		:      List[Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      str										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.25_74    ) < 1E-3

							def    __snake_case (							self	:  str    )     ->   List[str]:
														for timesteps in [25, 50, 100, 999, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase    )

							def    __snake_case (							self	:  Optional[int]    )     ->   int:
														# make sure that iterating over schedulers with same config names gives same results
														# for defaults
														__snake_case		:      List[Any]										=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
														__snake_case		:      List[Any]										=       self.full_loop(scheduler=lowerCamelCase    )
														__snake_case		:      Any										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.27_91    ) < 1E-3

														__snake_case		:      Union[str, Any]										=       DEISMultistepScheduler.from_config(scheduler.config    )
														__snake_case		:      Optional[Any]										=       DPMSolverMultistepScheduler.from_config(scheduler.config    )
														__snake_case		:      Tuple										=       UniPCMultistepScheduler.from_config(scheduler.config    )
														__snake_case		:      int										=       DPMSolverSinglestepScheduler.from_config(scheduler.config    )

														__snake_case		:      str										=       self.full_loop(scheduler=lowerCamelCase    )
														__snake_case		:      Tuple										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.27_91    ) < 1E-3

							def    __snake_case (							self	:  List[str]    )     ->   Any:
														self.check_over_configs(thresholding=lowerCamelCase    )
														for order in [1, 2, 3]:
																					for solver_type in ["midpoint", "heun"]:
																												for threshold in [0.5, 1.0, 2.0]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    thresholding=lowerCamelCase   ,     prediction_type=lowerCamelCase   ,     sample_max_value=lowerCamelCase   ,     algorithm_type="dpmsolver++"   ,     solver_order=lowerCamelCase   ,     solver_type=lowerCamelCase   ,     )

							def    __snake_case (							self	:  int    )     ->   Tuple:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   Dict:
														for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																					for solver_type in ["midpoint", "heun"]:
																												for order in [1, 2, 3]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    solver_order=lowerCamelCase   ,     solver_type=lowerCamelCase   ,     prediction_type=lowerCamelCase   ,     algorithm_type=lowerCamelCase   ,     )
																																										__snake_case		:      Any										=       self.full_loop(
																																										    solver_order=lowerCamelCase   ,     solver_type=lowerCamelCase   ,     prediction_type=lowerCamelCase   ,     algorithm_type=lowerCamelCase   ,     )
																																										assert not torch.isnan(lowerCamelCase    ).any(), "Samples have nan numbers"

							def    __snake_case (							self	:  Tuple    )     ->   str:
														self.check_over_configs(lower_order_final=lowerCamelCase    )
														self.check_over_configs(lower_order_final=lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														self.check_over_configs(lambda_min_clipped=-float("inf"    )    )
														self.check_over_configs(lambda_min_clipped=-5.1    )

							def    __snake_case (							self	:  int    )     ->   Dict:
														self.check_over_configs(variance_type=lowerCamelCase    )
														self.check_over_configs(variance_type="learned_range"    )

							def    __snake_case (							self	:  Optional[int]    )     ->   Union[str, Any]:
														for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																					self.check_over_forward(num_inference_steps=lowerCamelCase   ,     time_step=0    )

							def    __snake_case (							self	:  List[str]    )     ->   List[str]:
														__snake_case		:      Union[str, Any]										=       self.full_loop()
														__snake_case		:      Optional[Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.27_91    ) < 1E-3

							def    __snake_case (							self	:  int    )     ->   List[str]:
														__snake_case		:      Any										=       self.full_loop(use_karras_sigmas=lowerCamelCase    )
														__snake_case		:      List[Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.22_48    ) < 1E-3

							def    __snake_case (							self	:  str    )     ->   Any:
														__snake_case		:      int										=       self.full_loop(prediction_type="v_prediction"    )
														__snake_case		:      Dict										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.14_53    ) < 1E-3

							def    __snake_case (							self	:  Any    )     ->   Dict:
														__snake_case		:      str										=       self.full_loop(prediction_type="v_prediction"   ,     use_karras_sigmas=lowerCamelCase    )
														__snake_case		:      str										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.06_49    ) < 1E-3






							def    __snake_case (							self	:  int    )     ->   Optional[Any]:
														__snake_case		:      int										=       self.scheduler_classes[0]
														__snake_case		:      Any										=       self.get_scheduler_config(thresholding=lowerCamelCase   ,     dynamic_thresholding_ratio=0    )
														__snake_case		:      List[Any]										=       scheduler_class(**lowerCamelCase    )

														__snake_case		:      str										=       10
														__snake_case		:      Optional[Any]										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter.half()
														scheduler.set_timesteps(lowerCamelCase    )

														for i, t in enumerate(scheduler.timesteps    ):
																					__snake_case		:      List[Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      Optional[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														assert sample.dtype == torch.floataa





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case					:		int      =    logging.get_logger(__name__)

_snake_case					:		int      =    {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "beit"

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Any=8192   ,     lowerCamelCase	:  Dict=768   ,     lowerCamelCase	:  int=12   ,     lowerCamelCase	:  Optional[Any]=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  Tuple="gelu"   ,     lowerCamelCase	:  Union[str, Any]=0.0   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Dict=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Optional[Any]=224   ,     lowerCamelCase	:  Optional[int]=16   ,     lowerCamelCase	:  Any=3   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  int=False   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Tuple=0.1   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  int=[3, 5, 7, 11]   ,     lowerCamelCase	:  str=[1, 2, 3, 6]   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  List[Any]=0.4   ,     lowerCamelCase	:  int=256   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  List[str]=255   ,     **lowerCamelCase	:  Dict   ,     )     ->   int:
														super().__init__(**lowerCamelCase    )

														__snake_case		:      Any										=       vocab_size
														__snake_case		:      List[str]										=       hidden_size
														__snake_case		:      List[Any]										=       num_hidden_layers
														__snake_case		:      Tuple										=       num_attention_heads
														__snake_case		:      Dict										=       intermediate_size
														__snake_case		:      Union[str, Any]										=       hidden_act
														__snake_case		:      Optional[Any]										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      Union[str, Any]										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps

														__snake_case		:      Optional[Any]										=       image_size
														__snake_case		:      List[str]										=       patch_size
														__snake_case		:      Optional[Any]										=       num_channels
														__snake_case		:      Any										=       use_mask_token
														__snake_case		:      List[str]										=       use_absolute_position_embeddings
														__snake_case		:      List[Any]										=       use_relative_position_bias
														__snake_case		:      str										=       use_shared_relative_position_bias
														__snake_case		:      str										=       layer_scale_init_value
														__snake_case		:      Any										=       drop_path_rate
														__snake_case		:      int										=       use_mean_pooling
														# decode head attributes (semantic segmentation)
														__snake_case		:      Optional[Any]										=       out_indices
														__snake_case		:      List[str]										=       pool_scales
														# auxiliary head attributes (semantic segmentation)
														__snake_case		:      int										=       use_auxiliary_head
														__snake_case		:      int										=       auxiliary_loss_weight
														__snake_case		:      Optional[int]										=       auxiliary_channels
														__snake_case		:      int										=       auxiliary_num_convs
														__snake_case		:      str										=       auxiliary_concat_input
														__snake_case		:      List[str]										=       semantic_loss_ignore_index

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  version.parse("1.11"				)

							@property
							def    __snake_case (							self	:  Dict    )     ->   Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ]    )






							@property
							def    __snake_case (							self	:  str    )     ->   float:
														return 1E-4

