"""simple docstring"""


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
class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      42
       SCREAMING_SNAKE_CASE_			=      42
       SCREAMING_SNAKE_CASE_			=      None




class  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      2







       @register_to_config
       def __init__( self,							lowerCAmelCase__ = 0.02,							lowerCAmelCase__ = 100,							lowerCAmelCase__ = 1.007,							lowerCAmelCase__ = 80,							lowerCAmelCase__ = 0.05,							lowerCAmelCase__ = 50,							)      ->      List[str]:
            # standard deviation of the initial noise distribution
            snake_case_					       =					sigma_max

            # setable values
            snake_case_					       =					None
            snake_case_					       =					None
            snake_case_					       =					None  # sigma(t_i)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      torch.FloatTensor:
            return sample







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      int:
            snake_case_					       =					num_inference_steps
            snake_case_					       =					np.arange(0,							self.num_inference_steps)[::-1].copy()
            snake_case_					       =					torch.from_numpy(lowerCAmelCase__).to(lowerCAmelCase__)
            snake_case_					       =					[
                (
                    self.config.sigma_max**2
                    * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                )
                for i in self.timesteps
            ]
            snake_case_					       =					torch.tensor(lowerCAmelCase__,							dtype=torch.floataa,							device=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      Tuple[torch.FloatTensor, float]:
            if self.config.s_min <= sigma <= self.config.s_max:
                 snake_case_					       =					min(self.config.s_churn / self.num_inference_steps,							2**0.5 - 1)
            else:
                 snake_case_					       =					0

            # sample eps ~ N(0, S_noise^2 * I)
            snake_case_					       =					self.config.s_noise * randn_tensor(sample.shape,							generator=lowerCAmelCase__).to(sample.device)
            snake_case_					       =					sigma + gamma * sigma
            snake_case_					       =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = True,							)      ->      Union[KarrasVeOutput, Tuple]:
            snake_case_					       =					sample_hat + sigma_hat * model_output
            snake_case_					       =					(sample_hat - pred_original_sample) / sigma_hat
            snake_case_					       =					sample_hat + (sigma_prev - sigma_hat) * derivative

            if not return_dict:
                 return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=lowerCAmelCase__,							derivative=lowerCAmelCase__,							pred_original_sample=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = True,							)      ->      Union[KarrasVeOutput, Tuple]:
            snake_case_					       =					sample_prev + sigma_prev * model_output
            snake_case_					       =					(sample_prev - pred_original_sample) / sigma_prev
            snake_case_					       =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

            if not return_dict:
                 return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=lowerCAmelCase__,							derivative=lowerCAmelCase__,							pred_original_sample=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[str]:
            raise NotImplementedError()








"""simple docstring"""





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad






class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							1_0






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							[1, 2, 3, 4]
         lowerCAmelCase__			:Tuple											=							[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
         lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							''
         lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							(
             'It was the year of Our Lord one thousand seven hundred and '
             'seventy-five\n\nSpiritual revelations were conceded to England '
             'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
         )
         lowerCAmelCase__							,				lowerCAmelCase__			:str											=							process_story(__UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							[
             'It was the year of Our Lord one thousand seven hundred and seventy-five.',
             'Spiritual revelations were conceded to England at that favoured period, as at this.',
         ]
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							['It was the best of times.']
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							torch.tensor([1, 2, 3, 4]      )
         lowerCAmelCase__			:List[str]											=							torch.tensor([1, 1, 1, 1]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 0      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]      )
         lowerCAmelCase__			:Optional[int]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 2_3      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([8, 2, 3, 4, 1, 1, 1]      )
         lowerCAmelCase__			:Optional[Any]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 1      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							1_0_1
         lowerCAmelCase__			:str											=							torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]      )
         lowerCAmelCase__			:Any											=							torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]      )

         lowerCAmelCase__			:List[Any]											=							compute_token_type_ids(__UpperCAmelCase    , __UpperCAmelCase      )
         np.testing.assert_array_equal(__UpperCAmelCase    , __UpperCAmelCase      )
