
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 						UpperCAmelCase			(	a_		,   a_=0.999		,   a_="cosine"		,   )		->	Optional[int]:







				"""simple docstring"""



				if alpha_transform_type == "cosine":

								def alpha_bar_fn(a_						):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

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

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

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





class 							UpperCAmelCase      (  a__			,			a__  ):




	'''simple docstring'''





	snake_case_					    =     [e.name for e in KarrasDiffusionSchedulers]
	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: Optional[int]							,A					: Optional[Any] = 10_00							,A					: Union[str, Any] = 0.0_00_85							,A					: List[Any] = 0.0_12							,A					: int = "linear"							,A					: List[Any] = None							,A					: List[Any] = "epsilon"							,A					: List[str] = "linspace"							,A					: List[str] = 0							,):
					if trained_betas is not None:
									__A   			= torch.tensor(SCREAMING_SNAKE_CASE_							,dtype=torch.floataa )
					elif beta_schedule == "linear":
									__A   			= torch.linspace(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,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							,SCREAMING_SNAKE_CASE_							,dtype=torch.floataa ) ** 2
									)
					elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__A   			= betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
					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 )

					#  set all values
					self.set_timesteps(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: int							,A					: Optional[Any]							,A					: str=None ):
					if schedule_timesteps is None:
									__A   			= self.timesteps

					__A   			= (schedule_timesteps == timestep).nonzero()

					# The sigma index that is taken for the **very** first `step`
					# is always the second index (or the last index if there is only 1)
					# This way we can ensure we don't accidentally skip a sigma in
					# case we start in the middle of the denoising schedule (e.g. for image-to-image)
					if len(self._index_counter ) == 0:
									__A   			= 1 if len(SCREAMING_SNAKE_CASE_ ) > 1 else 0
					else:
									__A   			= timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
									__A   			= self._index_counter[timestep_int]

					return indices[pos].item()






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					# 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 				UpperCamelCase_  (       self					: List[str]							,A					: Union[str, Any]							,A					: Optional[Any]							,):
					__A   			= self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

					if self.state_in_first_order:
									__A   			= self.sigmas[step_index]
					else:
									__A   			= self.sigmas_interpol[step_index]

					__A   			= sample / ((sigma**2 + 1) ** 0.5)
					return sample






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: str							,A					: Optional[int] = None							,A					: str = None							,):
					__A   			= num_inference_steps

					__A   			= num_train_timesteps or self.config.num_train_timesteps

					# "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							,num_train_timesteps - 1							,SCREAMING_SNAKE_CASE_							,dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
					elif self.config.timestep_spacing == "leading":
									__A   			= 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							,SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
									timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
									__A   			= 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(SCREAMING_SNAKE_CASE_							,0							,-step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
									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   			= torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )

					__A   			= np.interp(SCREAMING_SNAKE_CASE_							,np.arange(0							,len(SCREAMING_SNAKE_CASE_ ) )							,SCREAMING_SNAKE_CASE_ )
					__A   			= np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
					__A   			= torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )

					# interpolate sigmas
					__A   			= sigmas.log().lerp(sigmas.roll(1 ).log()							,0.5 ).exp()

					__A   			= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
					__A   			= torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

					if str(SCREAMING_SNAKE_CASE_ ).startswith("mps" ):
									# mps does not support float64
									__A   			= torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_							,dtype=torch.floataa )
					else:
									__A   			= torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )

					# interpolate timesteps
					__A   			= self.sigma_to_t(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_							,dtype=timesteps.dtype )
					__A   			= torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])							,dim=-1 ).flatten()

					__A   			= torch.cat([timesteps[:1], interleaved_timesteps] )

					__A   			= None

					# for exp beta schedules, such as the one for `pipeline_shap_e.py`
					# we need an index counter
					__A   			= defaultdict(SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Dict ):
					# get log sigma
					__A   			= sigma.log()

					# get distribution
					__A   			= log_sigma - self.log_sigmas[:, None]

					# get sigmas range
					__A   			= dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
					__A   			= low_idx + 1

					__A   			= self.log_sigmas[low_idx]
					__A   			= self.log_sigmas[high_idx]

					# interpolate sigmas
					__A   			= (low - log_sigma) / (low - high)
					__A   			= w.clamp(0							,1 )

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






	@property
	def 				UpperCamelCase_  (       self					: str ):
					return self.sample is None






	def 				UpperCamelCase_  (       self					: Any							,A					: Tuple							,A					: Tuple							,A					: Union[str, Any]							,A					: Tuple = True							,):
					__A   			= self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

					# advance index counter by 1
					__A   			= timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
					self._index_counter[timestep_int] += 1

					if self.state_in_first_order:
									__A   			= self.sigmas[step_index]
									__A   			= self.sigmas_interpol[step_index + 1]
									__A   			= self.sigmas[step_index + 1]
					else:
									# 2nd order / KDPM2's method
									__A   			= self.sigmas[step_index - 1]
									__A   			= self.sigmas_interpol[step_index]
									__A   			= self.sigmas[step_index]

					# currently only gamma=0 is supported. This usually works best anyways.
					# We can support gamma in the future but then need to scale the timestep before
					# passing it to the model which requires a change in API
					__A   			= 0
					__A   			= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
									__A   			= sigma_hat if self.state_in_first_order else sigma_interpol
									__A   			= sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
									__A   			= sigma_hat if self.state_in_first_order else sigma_interpol
									__A   			= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
					elif self.config.prediction_type == "sample":
									raise NotImplementedError("prediction_type not implemented yet: sample" )
					else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

					if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									__A   			= (sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__A   			= sigma_interpol - sigma_hat

									# store for 2nd order step
									__A   			= sample
					else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									__A   			= (sample - pred_original_sample) / sigma_interpol

									# 3. delta timestep
									__A   			= sigma_next - sigma_hat

									__A   			= self.sample
									__A   			= None

					__A   			= sample + derivative * dt

					if not return_dict:
									return (prev_sample,)

					return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: Optional[Any]							,A					: Union[str, Any]							,A					: int							,):
					# 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(SCREAMING_SNAKE_CASE_ ):
									# 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   			= [self.index_for_timestep(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_ ) 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					: int ):
					return self.config.num_train_timesteps





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__UpperCAmelCase          =       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
    __UpperCAmelCase          =       json.load(f)



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          if torch_device == "cuda":
             model.half()
          return model






       @parameterized.expand(
           [
               ['en-ru', 26.0],
               ['ru-en', 22.0],
               ['en-de', 22.0],
               ['de-en', 29.0],
           ]       )
       @slow
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



