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

import numpy as np
import torch

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


lowerCamelCase_				:		Optional[Any]       						=       logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 						a__							(   __snake_case		):
			A__				:   torch.FloatTensor
			A__				:   Optional[torch.FloatTensor]								=			None






def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=0.9_99      ,				__lowerCamelCase="cosine"      ,				):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__lowerCamelCase						):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__lowerCamelCase						):
															return math.exp(t * -12.0						)

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

					__a							=   []
					for i in range(__lowerCamelCase						):
										__a							=   i / num_diffusion_timesteps
										__a							=   (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(__lowerCamelCase						) / alpha_bar_fn(__lowerCamelCase						)      ,				__lowerCamelCase						)						)
					return torch.tensor(__lowerCamelCase      ,				dtype=torch.floataa						)



class 						a__							(   __snake_case      ,						__snake_case		):
			A__				:   List[Any]								=			[e.name for e in KarrasDiffusionSchedulers]
			A__				:   List[Any]								=			1



			@register_to_config
			def __init__(						self      ,						UpperCAmelCase = 1_0_0_0      ,						UpperCAmelCase = 0.0_001      ,						UpperCAmelCase = 0.02      ,						UpperCAmelCase = "linear"      ,						UpperCAmelCase = None      ,						UpperCAmelCase = "epsilon"      ,						UpperCAmelCase = "linear"      ,						UpperCAmelCase = False      ,						UpperCAmelCase = "linspace"      ,						UpperCAmelCase = 0      ,						) ->    int:
								if trained_betas is not None:
													__a							=   torch.tensor(UpperCAmelCase      ,						dtype=torch.floataa    )
								elif beta_schedule == "linear":
													__a							=   torch.linspace(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__a							=   (
													    torch.linspace(beta_start**0.5      ,						beta_end**0.5      ,						UpperCAmelCase      ,						dtype=torch.floataa    ) ** 2
													)
								elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__a							=   betas_for_alpha_bar(UpperCAmelCase    )
								else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

								__a							=   1.0 - self.betas
								__a							=   torch.cumprod(self.alphas      ,						dim=0    )

								__a							=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
								__a							=   np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
								__a							=   torch.from_numpy(UpperCAmelCase    )

								# setable values
								__a							=   None
								__a							=   np.linspace(0      ,						num_train_timesteps - 1      ,						UpperCAmelCase      ,						dtype=UpperCAmelCase    )[::-1].copy()
								__a							=   torch.from_numpy(UpperCAmelCase    )
								__a							=   False
								__a							=   use_karras_sigmas



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    torch.FloatTensor:
								if isinstance(UpperCAmelCase      ,						torch.Tensor    ):
													__a							=   timestep.to(self.timesteps.device    )
								__a							=   (self.timesteps == timestep).nonzero().item()
								__a							=   self.sigmas[step_index]

								__a							=   sample / ((sigma**2 + 1) ** 0.5)

								__a							=   True
								return sample



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None    ) ->    Tuple:
								__a							=   num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
													__a							=   np.linspace(0      ,						self.config.num_train_timesteps - 1      ,						UpperCAmelCase      ,						dtype=UpperCAmelCase    )[
													    ::-1
													].copy()
								elif self.config.timestep_spacing == "leading":
													__a							=   self.config.num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__a							=   (np.arange(0      ,						UpperCAmelCase    ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase    )
													timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
													__a							=   self.config.num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__a							=   (np.arange(self.config.num_train_timesteps      ,						0      ,						-step_ratio    )).round().copy().astype(UpperCAmelCase    )
													timesteps -= 1
								else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

								__a							=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
								__a							=   np.log(UpperCAmelCase    )

								if self.config.interpolation_type == "linear":
													__a							=   np.interp(UpperCAmelCase      ,						np.arange(0      ,						len(UpperCAmelCase    )    )      ,						UpperCAmelCase    )
								elif self.config.interpolation_type == "log_linear":
													__a							=   torch.linspace(np.log(sigmas[-1]    )      ,						np.log(sigmas[0]    )      ,						num_inference_steps + 1    ).exp()
								else:
													raise ValueError(
													    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
													    ' \'linear\' or \'log_linear\''    )

								if self.use_karras_sigmas:
													__a							=   self._convert_to_karras(in_sigmas=UpperCAmelCase      ,						num_inference_steps=self.num_inference_steps    )
													__a							=   np.array([self._sigma_to_t(UpperCAmelCase      ,						UpperCAmelCase    ) for sigma in sigmas]    )

								__a							=   np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
								__a							=   torch.from_numpy(UpperCAmelCase    ).to(device=UpperCAmelCase    )
								if str(UpperCAmelCase    ).startswith('mps'    ):
													# mps does not support float64
													__a							=   torch.from_numpy(UpperCAmelCase    ).to(UpperCAmelCase      ,						dtype=torch.floataa    )
								else:
													__a							=   torch.from_numpy(UpperCAmelCase    ).to(device=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[str]:
								# get log sigma
								__a							=   np.log(UpperCAmelCase    )

								# get distribution
								__a							=   log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								__a							=   np.cumsum((dists >= 0)      ,						axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
								__a							=   low_idx + 1

								__a							=   log_sigmas[low_idx]
								__a							=   log_sigmas[high_idx]

								# interpolate sigmas
								__a							=   (low - log_sigma) / (low - high)
								__a							=   np.clip(UpperCAmelCase      ,						0      ,						1    )

								# transform interpolation to time range
								__a							=   (1 - w) * low_idx + w * high_idx
								__a							=   t.reshape(sigma.shape    )
								return t



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    torch.FloatTensor:
								__a							=   in_sigmas[-1].item()
								__a							=   in_sigmas[0].item()

								__a							=   7.0  # 7.0 is the value used in the paper
								__a							=   np.linspace(0      ,						1      ,						UpperCAmelCase    )
								__a							=   sigma_min ** (1 / rho)
								__a							=   sigma_max ** (1 / rho)
								__a							=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = 0.0      ,						UpperCAmelCase = 0.0      ,						UpperCAmelCase = float('inf'    )      ,						UpperCAmelCase = 1.0      ,						UpperCAmelCase = None      ,						UpperCAmelCase = True      ,						) ->    Union[EulerDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(UpperCAmelCase      ,						UpperCAmelCase    )
								    or isinstance(UpperCAmelCase      ,						torch.IntTensor    )
								    or isinstance(UpperCAmelCase      ,						torch.LongTensor    )
								):
													raise ValueError(
													    (
													        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
													        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
													        ' one of the `scheduler.timesteps` as a timestep.'
													    )      ,						)

								if not self.is_scale_input_called:
													logger.warning(
													    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
													    'See `StableDiffusionPipeline` for a usage example.'    )

								if isinstance(UpperCAmelCase      ,						torch.Tensor    ):
													__a							=   timestep.to(self.timesteps.device    )

								__a							=   (self.timesteps == timestep).nonzero().item()
								__a							=   self.sigmas[step_index]

								__a							=   min(s_churn / (len(self.sigmas    ) - 1)      ,						2**0.5 - 1    ) if s_tmin <= sigma <= s_tmax else 0.0

								__a							=   randn_tensor(
								    model_output.shape      ,						dtype=model_output.dtype      ,						device=model_output.device      ,						generator=UpperCAmelCase    )

								__a							=   noise * s_noise
								__a							=   sigma * (gamma + 1)

								if gamma > 0:
													__a							=   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													__a							=   model_output
								elif self.config.prediction_type == "epsilon":
													__a							=   sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__a							=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

								# 2. Convert to an ODE derivative
								__a							=   (sample - pred_original_sample) / sigma_hat

								__a							=   self.sigmas[step_index + 1] - sigma_hat

								__a							=   sample + derivative * dt

								if not return_dict:
													return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase      ,						pred_original_sample=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						) ->    torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								__a							=   self.sigmas.to(device=original_samples.device      ,						dtype=original_samples.dtype    )
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase    ):
													# mps does not support float64
													__a							=   self.timesteps.to(original_samples.device      ,						dtype=torch.floataa    )
													__a							=   timesteps.to(original_samples.device      ,						dtype=torch.floataa    )
								else:
													__a							=   self.timesteps.to(original_samples.device    )
													__a							=   timesteps.to(original_samples.device    )

								__a							=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								__a							=   sigmas[step_indices].flatten()
								while len(sigma.shape    ) < len(original_samples.shape    ):
													__a							=   sigma.unsqueeze(-1    )

								__a							=   original_samples + noise * sigma
								return noisy_samples







			def __len__(						self    ) ->    Optional[Any]:
								return self.config.num_train_timesteps





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




