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

import numpy as np
import torch

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





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 							UpperCAmelCase      (  a__  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     None





def 						UpperCAmelCase			(	a_		,   a_=0.999		,   a_="cosine"		,   )		->	List[Any]:







				"""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'''





	@register_to_config
	def __init__(       self					: str							,A					: Union[str, Any] = 10_00							,A					: str = "fixed_small_log"							,A					: List[Any] = True							,A					: Union[str, Any] = 1.0							,A					: List[Any] = "epsilon"							,A					: Dict = "squaredcos_cap_v2"							,):
					if beta_schedule != "squaredcos_cap_v2":
									raise ValueError("UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'" )

					__A   			= betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )

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

					# standard deviation of the initial noise distribution
					__A   			= 1.0

					# setable values
					__A   			= None
					__A   			= torch.from_numpy(np.arange(0							,SCREAMING_SNAKE_CASE_ )[::-1].copy() )

					__A   			= variance_type






	def 				UpperCamelCase_  (       self					: Tuple							,A					: List[str]							,A					: Union[str, Any] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Tuple							,A					: int							,A					: Optional[int] = None ):
					__A   			= num_inference_steps
					__A   			= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
					__A   			= (np.arange(0							,SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
					__A   			= torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[Any]=None							,A					: Any=None							,A					: Optional[Any]=None ):
					if prev_timestep is None:
									__A   			= t - 1

					__A   			= self.alphas_cumprod[t]
					__A   			= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					__A   			= 1 - alpha_prod_t
					__A   			= 1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
									__A   			= self.betas[t]
					else:
									__A   			= 1 - alpha_prod_t / alpha_prod_t_prev

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					__A   			= beta_prod_t_prev / beta_prod_t * beta

					if variance_type is None:
									__A   			= self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small_log":
									__A   			= torch.log(torch.clamp(SCREAMING_SNAKE_CASE_							,min=1E-20 ) )
									__A   			= torch.exp(0.5 * variance )
					elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									__A   			= variance.log()
									__A   			= beta.log()

									__A   			= (predicted_variance + 1) / 2
									__A   			= frac * max_log + (1 - frac) * min_log

					return variance






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

					if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
									__A   			= torch.split(SCREAMING_SNAKE_CASE_							,sample.shape[1]							,dim=1 )
					else:
									__A   			= None

					# 1. compute alphas, betas
					if prev_timestep is None:
									__A   			= t - 1

					__A   			= self.alphas_cumprod[t]
					__A   			= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					__A   			= 1 - alpha_prod_t
					__A   			= 1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
									__A   			= self.betas[t]
									__A   			= self.alphas[t]
					else:
									__A   			= 1 - alpha_prod_t / alpha_prod_t_prev
									__A   			= 1 - beta

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
									__A   			= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
									__A   			= model_output
					else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
									    " for the UnCLIPScheduler." )

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
									__A   			= torch.clamp(
									    SCREAMING_SNAKE_CASE_							,-self.config.clip_sample_range							,self.config.clip_sample_range )

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					__A   			= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
					__A   			= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					__A   			= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					__A   			= 0
					if t > 0:
									__A   			= randn_tensor(
									    model_output.shape							,dtype=model_output.dtype							,generator=SCREAMING_SNAKE_CASE_							,device=model_output.device )

									__A   			= self._get_variance(
									    SCREAMING_SNAKE_CASE_							,predicted_variance=SCREAMING_SNAKE_CASE_							,prev_timestep=SCREAMING_SNAKE_CASE_							,)

									if self.variance_type == "fixed_small_log":
													__A   			= variance
									elif self.variance_type == "learned_range":
													__A   			= (0.5 * variance).exp()
									else:
													raise ValueError(
													    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
													    " for the UnCLIPScheduler." )

									__A   			= variance * variance_noise

					__A   			= pred_prev_sample + variance

					if not return_dict:
									return (pred_prev_sample,)

					return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_							,pred_original_sample=SCREAMING_SNAKE_CASE_ )







	def 				UpperCamelCase_  (       self					: Tuple							,A					: Optional[int]							,A					: Tuple							,A					: Dict							,):
					# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
					__A   			= self.alphas_cumprod.to(device=original_samples.device							,dtype=original_samples.dtype )
					__A   			= timesteps.to(original_samples.device )

					__A   			= alphas_cumprod[timesteps] ** 0.5
					__A   			= sqrt_alpha_prod.flatten()
					while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
									__A   			= sqrt_alpha_prod.unsqueeze(-1 )

					__A   			= (1 - alphas_cumprod[timesteps]) ** 0.5
					__A   			= sqrt_one_minus_alpha_prod.flatten()
					while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
									__A   			= sqrt_one_minus_alpha_prod.unsqueeze(-1 )

					__A   			= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples





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_       )



