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

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 					_UpperCAmelCase			(     SCREAMING_SNAKE_CASE__  ):



 '''simple docstring'''




 __A			       =					42
 __A			       =					None


def 				lowerCAmelCase__       (			a__    ,     a__=0.999    ,     a__="cosine"    ,     )      ->Dict:




  '''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}' )

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







class 					_UpperCAmelCase			(     SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__  ):



 '''simple docstring'''




 __A			       =					1







 @register_to_config
 def __init__(		self				:   Tuple							, lowercase_				:   str = 1000							, lowercase_				:   List[Any] = 0.00_01							, lowercase_				:   Union[str, Any] = 0.02							, lowercase_				:   Union[str, Any] = "linear"							, lowercase_				:   str = None							, lowercase_				:   Optional[int] = True							, lowercase_				:   int = True							, lowercase_				:   List[Any] = 0							, lowercase_				:   List[Any] = "epsilon"							, lowercase_				:   int = 1.0							, **lowercase_				:   Union[str, Any]							, )						->	Any:


   """simple docstring"""







   if kwargs.get("set_alpha_to_one"							, A_) is not None:
     _UpperCamelCase           =    (
         '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
     )
     deprecate("set_alpha_to_one"							, "1.0.0"							, A_							, standard_warn=A_)
     _UpperCamelCase           =    kwargs['''set_alpha_to_one''']
   if trained_betas is not None:
     _UpperCamelCase           =    torch.tensor(A_							, dtype=torch.floataa)
   elif beta_schedule == "linear":
     _UpperCamelCase           =    torch.linspace(A_							, A_							, A_							, dtype=torch.floataa)
   elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     _UpperCamelCase           =    (
         torch.linspace(beta_start**0.5							, beta_end**0.5							, A_							, dtype=torch.floataa) ** 2
     )
   elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     _UpperCamelCase           =    betas_for_alpha_bar(A_)
   else:
     raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}')

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

   # At every step in inverted ddim, we are looking into the next alphas_cumprod
   # For the final step, there is no next alphas_cumprod, and the index is out of bounds
   # `set_alpha_to_zero` decides whether we set this parameter simply to zero
   # in this case, self.step() just output the predicted noise
   # or whether we use the final alpha of the "non-previous" one.
   _UpperCamelCase           =    torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

   # standard deviation of the initial noise distribution
   _UpperCamelCase           =    1.0

   # setable values
   _UpperCamelCase           =    None
   _UpperCamelCase           =    torch.from_numpy(np.arange(0							, A_).copy().astype(np.intaa))







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   List[str]							, lowercase_				:   List[str] = None)						->	torch.FloatTensor:


   """simple docstring"""







   return sample







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   int							, lowercase_				:   Dict = None)						->	List[str]:


   """simple docstring"""







   if num_inference_steps > self.config.num_train_timesteps:
     raise ValueError(
         f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
         f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
         f' maximal {self.config.num_train_timesteps} timesteps.')

   _UpperCamelCase           =    num_inference_steps
   _UpperCamelCase           =    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
   _UpperCamelCase           =    (np.arange(0							, A_) * step_ratio).round().copy().astype(np.intaa)
   _UpperCamelCase           =    torch.from_numpy(A_).to(A_)
   self.timesteps += self.config.steps_offset







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   Dict							, lowercase_				:   List[str]							, lowercase_				:   List[Any]							, lowercase_				:   Dict = 0.0							, lowercase_				:   Tuple = False							, lowercase_				:   Optional[Any] = None							, lowercase_				:   Tuple = True							, )						->	Union[DDIMSchedulerOutput, Tuple]:


   """simple docstring"""







   _UpperCamelCase           =    timestep + self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   # change original implementation to exactly match noise levels for analogous forward process
   _UpperCamelCase           =    self.alphas_cumprod[timestep]
   _UpperCamelCase           =    (
       self.alphas_cumprod[prev_timestep]
       if prev_timestep < self.config.num_train_timesteps
       else self.final_alpha_cumprod
   )

   _UpperCamelCase           =    1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   if self.config.prediction_type == "epsilon":
     _UpperCamelCase           =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     _UpperCamelCase           =    model_output
   elif self.config.prediction_type == "sample":
     _UpperCamelCase           =    model_output
     _UpperCamelCase           =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
     _UpperCamelCase           =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     _UpperCamelCase           =    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
   else:
     raise ValueError(
         f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
         " `v_prediction`")

   # 4. Clip or threshold "predicted x_0"
   if self.config.clip_sample:
     _UpperCamelCase           =    pred_original_sample.clamp(
         -self.config.clip_sample_range							, self.config.clip_sample_range)

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   _UpperCamelCase           =    (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

   # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   _UpperCamelCase           =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if not return_dict:
     return (prev_sample, pred_original_sample)
   return DDIMSchedulerOutput(prev_sample=A_							, pred_original_sample=A_)







 def __len__(		self				:   str)						->	Optional[Any]:


   """simple docstring"""







   return self.config.num_train_timesteps






"""simple docstring"""




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A__ :       Any				= [
    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

A__ :       Tuple				= [
    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
    ' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Any   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''', '''rougeL''']    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''']    )
  assert (
      pd.DataFrame(no_aggregation['''rouge2''']    ).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra['''rouge2''']    ).fmeasure.mean()
  )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  '''rougeLsum'''
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  _lowercase:      List[str]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  assert score > score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Tuple   			=  ['''rouge1''', '''rouge2''', '''rougeL''']
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  assert score_sep == score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  [
      '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
  ]
  _lowercase:      Union[str, Any]   			=  [
      '''Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
      ''' the final seconds on board Flight 9525.''',
  ]
  assert calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    ) == calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      int   			=  [
      '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
  ]
  _lowercase:      Union[str, Any]   			=  [
      ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
  ]

  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']			,					newline_sep=_UpperCamelCase    )['''rougeLsum''']
  _lowercase:      Union[str, Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']    )['''rougeLsum''']
  assert new_score > prev_score







def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  Path('''examples/seq2seq/test_data/wmt_en_ro'''    )
  _lowercase:      int   			=  calculate_rouge_path(data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge_path(
      data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )			,					bootstrap_aggregation=_UpperCamelCase    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
