

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Optional[Any]  							=       (PNDMScheduler,)
					UpperCAmelCase__:   str  							=       (('''num_inference_steps''', 50),)






					def   __A			( self    ,       **A__			):
						A__							:    str        =		{
						    """num_train_timesteps""": 1000,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						}

						config.update(**A__			)
						return config






					def   __A			( self    ,       A__=0    ,       **A__			):
						A__							:    Union[str, Any]        =		dict(self.forward_default_kwargs			)
						A__							:    List[str]        =		kwargs.pop("""num_inference_steps"""    ,       A__			)
						A__							:    str        =		self.dummy_sample
						A__							:    List[Any]        =		0.1 * sample
						A__							:    Dict        =		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

						for scheduler_class in self.scheduler_classes:
							A__							:    int        =		self.get_scheduler_config(**A__			)
							A__							:    str        =		scheduler_class(**A__			)
							scheduler.set_timesteps(A__			)
							# copy over dummy past residuals
							A__							:    List[Any]        =		dummy_past_residuals[:]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(A__			)
								A__							:    Optional[int]        =		scheduler_class.from_pretrained(A__			)
								new_scheduler.set_timesteps(A__			)
								# copy over dummy past residuals
								A__							:    Union[str, Any]        =		dummy_past_residuals[:]

							A__							:    Optional[int]        =		scheduler.step_prk(A__    ,       A__    ,       A__    ,       **A__			).prev_sample
							A__							:    Any        =		new_scheduler.step_prk(A__    ,       A__    ,       A__    ,       **A__			).prev_sample

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

							A__							:    List[str]        =		scheduler.step_plms(A__    ,       A__    ,       A__    ,       **A__			).prev_sample
							A__							:    Dict        =		new_scheduler.step_plms(A__    ,       A__    ,       A__    ,       **A__			).prev_sample

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






					def   __A			( self			):
						pass






					def   __A			( self    ,       A__=0    ,       **A__			):
						A__							:    int        =		dict(self.forward_default_kwargs			)
						A__							:    List[str]        =		kwargs.pop("""num_inference_steps"""    ,       A__			)
						A__							:    Optional[Any]        =		self.dummy_sample
						A__							:    List[Any]        =		0.1 * sample
						A__							:    Any        =		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

						for scheduler_class in self.scheduler_classes:
							A__							:    Dict        =		self.get_scheduler_config()
							A__							:    List[str]        =		scheduler_class(**A__			)
							scheduler.set_timesteps(A__			)

							# copy over dummy past residuals (must be after setting timesteps)
							A__							:    List[Any]        =		dummy_past_residuals[:]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(A__			)
								A__							:    str        =		scheduler_class.from_pretrained(A__			)
								# copy over dummy past residuals
								new_scheduler.set_timesteps(A__			)

								# copy over dummy past residual (must be after setting timesteps)
								A__							:    Optional[int]        =		dummy_past_residuals[:]

							A__							:    Union[str, Any]        =		scheduler.step_prk(A__    ,       A__    ,       A__    ,       **A__			).prev_sample
							A__							:    str        =		new_scheduler.step_prk(A__    ,       A__    ,       A__    ,       **A__			).prev_sample

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

							A__							:    Optional[Any]        =		scheduler.step_plms(A__    ,       A__    ,       A__    ,       **A__			).prev_sample
							A__							:    Dict        =		new_scheduler.step_plms(A__    ,       A__    ,       A__    ,       **A__			).prev_sample

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






					def   __A			( self    ,       **A__			):
						A__							:    List[str]        =		self.scheduler_classes[0]
						A__							:    str        =		self.get_scheduler_config(**A__			)
						A__							:    Optional[int]        =		scheduler_class(**A__			)

						A__							:    Union[str, Any]        =		10
						A__							:    Union[str, Any]        =		self.dummy_model()
						A__							:    List[str]        =		self.dummy_sample_deter
						scheduler.set_timesteps(A__			)

						for i, t in enumerate(scheduler.prk_timesteps			):
							A__							:    Tuple        =		model(A__    ,       A__			)
							A__							:    Optional[int]        =		scheduler.step_prk(A__    ,       A__    ,       A__			).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps			):
							A__							:    int        =		model(A__    ,       A__			)
							A__							:    Optional[Any]        =		scheduler.step_plms(A__    ,       A__    ,       A__			).prev_sample

						return sample






					def   __A			( self			):
						A__							:    int        =		dict(self.forward_default_kwargs			)

						A__							:    Any        =		kwargs.pop("""num_inference_steps"""    ,       A__			)

						for scheduler_class in self.scheduler_classes:
							A__							:    Tuple        =		self.get_scheduler_config()
							A__							:    Union[str, Any]        =		scheduler_class(**A__			)

							A__							:    Optional[int]        =		self.dummy_sample
							A__							:    Tuple        =		0.1 * sample

							if num_inference_steps is not None and hasattr(A__    ,       """set_timesteps"""			):
								scheduler.set_timesteps(A__			)
							elif num_inference_steps is not None and not hasattr(A__    ,       """set_timesteps"""			):
								A__							:    Optional[Any]        =		num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							A__							:    Optional[Any]        =		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
							A__							:    Union[str, Any]        =		dummy_past_residuals[:]

							A__							:    Optional[Any]        =		scheduler.step_prk(A__    ,       0    ,       A__    ,       **A__			).prev_sample
							A__							:    str        =		scheduler.step_prk(A__    ,       1    ,       A__    ,       **A__			).prev_sample

							self.assertEqual(output_a.shape    ,       sample.shape			)
							self.assertEqual(output_a.shape    ,       output_a.shape			)

							A__							:    Tuple        =		scheduler.step_plms(A__    ,       0    ,       A__    ,       **A__			).prev_sample
							A__							:    Optional[Any]        =		scheduler.step_plms(A__    ,       1    ,       A__    ,       **A__			).prev_sample

							self.assertEqual(output_a.shape    ,       sample.shape			)
							self.assertEqual(output_a.shape    ,       output_a.shape			)






					def   __A			( self			):
						for timesteps in [100, 1000]:
							self.check_over_configs(num_train_timesteps=A__			)






					def   __A			( self			):
						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=A__			)

						A__							:    Any        =		self.scheduler_classes[0]
						A__							:    Tuple        =		self.get_scheduler_config(steps_offset=1			)
						A__							:    int        =		scheduler_class(**A__			)
						scheduler.set_timesteps(10			)
						assert torch.equal(
						    scheduler.timesteps    ,       torch.LongTensor(
						        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)    ,       )






					def   __A			( self			):
						for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,       [0.0_0_2, 0.0_2]			):
							self.check_over_configs(beta_start=A__    ,       beta_end=A__			)






					def   __A			( self			):
						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=A__			)






					def   __A			( self			):
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=A__			)






					def   __A			( self			):
						for t in [1, 5, 10]:
							self.check_over_forward(time_step=A__			)






					def   __A			( self			):
						for t, num_inference_steps in zip([1, 5, 10]    ,       [10, 50, 100]			):
							self.check_over_forward(num_inference_steps=A__			)






					def   __A			( self			):
						# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
						A__							:    Dict        =		27

						for scheduler_class in self.scheduler_classes:
							A__							:    Optional[Any]        =		self.dummy_sample
							A__							:    Tuple        =		0.1 * sample

							A__							:    Dict        =		self.get_scheduler_config()
							A__							:    List[Any]        =		scheduler_class(**A__			)

							scheduler.set_timesteps(A__			)

							# before power of 3 fix, would error on first step, so we only need to do two
							for i, t in enumerate(scheduler.prk_timesteps[:2]			):
								A__							:    int        =		scheduler.step_prk(A__    ,       A__    ,       A__			).prev_sample






					def   __A			( self			):
						with self.assertRaises(A__			):
							A__							:    Union[str, Any]        =		self.scheduler_classes[0]
							A__							:    Union[str, Any]        =		self.get_scheduler_config()
							A__							:    Union[str, Any]        =		scheduler_class(**A__			)

							scheduler.step_plms(self.dummy_sample    ,       1    ,       self.dummy_sample			).prev_sample






					def   __A			( self			):
						A__							:    str        =		self.full_loop()
						A__							:    Optional[Any]        =		torch.sum(torch.abs(A__			)			)
						A__							:    List[Any]        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
						assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3






					def   __A			( self			):
						A__							:    Tuple        =		self.full_loop(prediction_type="""v_prediction"""			)
						A__							:    Optional[Any]        =		torch.sum(torch.abs(A__			)			)
						A__							:    Tuple        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
						assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3






					def   __A			( self			):
						# We specify different beta, so that the first alpha is 0.99
						A__							:    Optional[Any]        =		self.full_loop(set_alpha_to_one=A__    ,       beta_start=0.0_1			)
						A__							:    Dict        =		torch.sum(torch.abs(A__			)			)
						A__							:    Union[str, Any]        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
						assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3






					def   __A			( self			):
						# We specify different beta, so that the first alpha is 0.99
						A__							:    int        =		self.full_loop(set_alpha_to_one=A__    ,       beta_start=0.0_1			)
						A__							:    List[Any]        =		torch.sum(torch.abs(A__			)			)
						A__							:    Tuple        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
						assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_       : int         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   List[Any]  							=       '''mgp-str'''






					def __init__( self    ,       A__=[32, 128]    ,       A__=4    ,       A__=3    ,       A__=27    ,       A__=38    ,       A__=5_0257    ,       A__=3_0522    ,       A__=768    ,       A__=12    ,       A__=12    ,       A__=4.0    ,       A__=True    ,       A__=False    ,       A__=1e-5    ,       A__=0.0    ,       A__=0.0    ,       A__=0.0    ,       A__=False    ,       A__=0.0_2    ,       **A__    ,       ):
						super().__init__(**A__			)

						A__							:    Dict        =		image_size
						A__							:    int        =		patch_size
						A__							:    Dict        =		num_channels
						A__							:    List[Any]        =		max_token_length
						A__							:    str        =		num_character_labels
						A__							:    Tuple        =		num_bpe_labels
						A__							:    Optional[Any]        =		num_wordpiece_labels
						A__							:    Optional[int]        =		hidden_size
						A__							:    Tuple        =		num_hidden_layers
						A__							:    Any        =		num_attention_heads
						A__							:    List[Any]        =		mlp_ratio
						A__							:    Tuple        =		distilled
						A__							:    Union[str, Any]        =		layer_norm_eps
						A__							:    Tuple        =		drop_rate
						A__							:    List[str]        =		qkv_bias
						A__							:    Optional[Any]        =		attn_drop_rate
						A__							:    Union[str, Any]        =		drop_path_rate
						A__							:    Optional[Any]        =		output_aa_attentions
						A__							:    Optional[int]        =		initializer_range



