

'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    1







						@register_to_config
						def __init__(							self					:      str			,			snake_case_					:      int = 2_0_0_0			,			snake_case_					:      float = 0.1_5			,			snake_case_					:      float = 0.0_1			,			snake_case_					:      float = 1_3_4_8.0			,			snake_case_					:      float = 1e-5			,			snake_case_					:      int = 1			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  sigma_max

								# setable values
								snake_case__						:       Union[str, Any]       						=  None

								self.set_sigmas(snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      str			,			snake_case_					:      int			,			snake_case_					:      float = None			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

								snake_case__						:       List[str]       						=  torch.linspace(1			,			snake_case_			,			snake_case_			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      float = None			,			snake_case_					:      float = None			,			snake_case_					:      float = None  ):



								'''simple docstring'''



								snake_case__						:       int       						=  sigma_min if sigma_min is not None else self.config.sigma_min
								snake_case__						:       int       						=  sigma_max if sigma_max is not None else self.config.sigma_max
								snake_case__						:       Optional[int]       						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
										self.set_timesteps(snake_case_			,			snake_case_  )

								snake_case__						:       Dict       						=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								snake_case__						:       Optional[int]       						=  torch.exp(torch.linspace(math.log(snake_case_  )			,			math.log(snake_case_  )			,			snake_case_  )  )
								snake_case__						:       List[Any]       						=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[str]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								return torch.where(
								    timesteps == 0			,			torch.zeros_like(t.to(timesteps.device  )  )			,			self.discrete_sigmas[timesteps - 1].to(timesteps.device  )			,			)







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      int			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[torch.Generator] = None			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								if self.timesteps is None:
										raise ValueError(
										    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''  )

								snake_case__						:       Optional[int]       						=  timestep * torch.ones(
								    sample.shape[0]			,			device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
								snake_case__						:       int       						=  (timestep * (len(self.timesteps  ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								snake_case__						:       Union[str, Any]       						=  timesteps.to(self.discrete_sigmas.device  )

								snake_case__						:       Optional[Any]       						=  self.discrete_sigmas[timesteps].to(sample.device  )
								snake_case__						:       int       						=  self.get_adjacent_sigma(snake_case_			,			snake_case_  ).to(sample.device  )
								snake_case__						:       Optional[Any]       						=  torch.zeros_like(snake_case_  )
								snake_case__						:       Any       						=  (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								snake_case__						:       Optional[Any]       						=  diffusion.flatten()
								while len(diffusion.shape  ) < len(sample.shape  ):
										snake_case__						:       Optional[Any]       						=  diffusion.unsqueeze(-1  )
								snake_case__						:       Tuple       						=  drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								snake_case__						:       Union[str, Any]       						=  randn_tensor(
								    sample.shape			,			layout=sample.layout			,			generator=snake_case_			,			device=sample.device			,			dtype=sample.dtype  )
								snake_case__						:       Union[str, Any]       						=  sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								snake_case__						:       Tuple       						=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
										return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=snake_case_			,			prev_sample_mean=snake_case_  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[torch.Generator] = None			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								if self.timesteps is None:
										raise ValueError(
										    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''  )

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								snake_case__						:       Optional[Any]       						=  randn_tensor(sample.shape			,			layout=sample.layout			,			generator=snake_case_  ).to(sample.device  )

								# compute step size from the model_output, the noise, and the snr
								snake_case__						:       List[str]       						=  torch.norm(model_output.reshape(model_output.shape[0]			,			-1  )			,			dim=-1  ).mean()
								snake_case__						:       int       						=  torch.norm(noise.reshape(noise.shape[0]			,			-1  )			,			dim=-1  ).mean()
								snake_case__						:       List[Any]       						=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								snake_case__						:       Union[str, Any]       						=  step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								snake_case__						:       int       						=  step_size.flatten()
								while len(step_size.shape  ) < len(sample.shape  ):
										snake_case__						:       int       						=  step_size.unsqueeze(-1  )
								snake_case__						:       Dict       						=  sample + step_size * model_output
								snake_case__						:       Any       						=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
										return (prev_sample,)

								return SchedulerOutput(prev_sample=snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			):



								'''simple docstring'''



								snake_case__						:       int       						=  timesteps.to(original_samples.device  )
								snake_case__						:       Union[str, Any]       						=  self.discrete_sigmas.to(original_samples.device  )[timesteps]
								snake_case__						:       Union[str, Any]       						=  (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(snake_case_  ) * sigmas[:, None, None, None]
								)
								snake_case__						:       List[Any]       						=  noise + original_samples
								return noisy_samples







						def __len__(							self					:      Any  ):



								'''simple docstring'''



								return self.config.num_train_timesteps






'''simple docstring'''


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Dict			,			snake_case_					:      str			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      List[Any]=True			,			snake_case_					:      Any=None			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  parent
								snake_case__						:       List[Any]       						=  config_class
								snake_case__						:       Optional[int]       						=  has_text_modality
								snake_case__						:       str       						=  kwargs
								snake_case__						:       Dict       						=  common_properties







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       List[Any]       						=  (
								    ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
										common_properties.extend(['''vocab_size''']  )

								# Test that config has the common properties as getters
								for prop in common_properties:
										self.parent.assertTrue(hasattr(snake_case_			,			snake_case_  )			,			msg=F"""`{prop}` does not exist"""  )

								# Test that config has the common properties as setter
								for idx, name in enumerate(snake_case_  ):
										try:
												setattr(snake_case_			,			snake_case_			,			snake_case_  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(snake_case_  ):
										try:
												snake_case__						:       Dict       						=  self.config_class(**{name: idx}  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       str       						=  json.loads(config.to_json_string()  )
								for key, value in self.inputs_dict.items():
										self.parent.assertEqual(obj[key]			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[Any]       						=  os.path.join(snake_case_			,			'''config.json'''  )
										config_first.to_json_file(snake_case_  )
										snake_case__						:       Dict       						=  self.config_class.from_json_file(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       Optional[Any]       						=  self.config_class.from_pretrained(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								snake_case__						:       str       						=  '''test'''
								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[str]       						=  os.path.join(snake_case_			,			snake_case_  )
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       List[str]       						=  self.config_class.from_pretrained(snake_case_			,			subfolder=snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.config_class(**self.inputs_dict			,			num_labels=5  )
								self.parent.assertEqual(len(config.idalabel  )			,			5  )
								self.parent.assertEqual(len(config.labelaid  )			,			5  )

								snake_case__						:       List[Any]       						=  3
								self.parent.assertEqual(len(config.idalabel  )			,			3  )
								self.parent.assertEqual(len(config.labelaid  )			,			3  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								if self.config_class.is_composition:
										return
								snake_case__						:       Any       						=  self.config_class()
								self.parent.assertIsNotNone(snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  copy.deepcopy(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  self.config_class(**snake_case_  )
								snake_case__						:       List[str]       						=  []
								for key, value in config_common_kwargs.items():
										if key == "torch_dtype":
												if not is_torch_available():
														continue
												else:
														import torch

														if config.torch_dtype != torch.floataa:
																wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)  )
										elif getattr(snake_case_			,			snake_case_  ) != value:
												wrong_values.append((key, getattr(snake_case_			,			snake_case_  ), value)  )

								if len(snake_case_  ) > 0:
										snake_case__						:       Dict       						=  '''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]  )
										raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()



