




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 SchedulerMixin


__SCREAMING_SNAKE_CASE      :				Dict				      =logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class      A     (						lowerCamelCase__						):
     """simple docstring"""

     _snake_case:		torch.FloatTensor






class      A     (						lowerCamelCase__     ,  lowerCamelCase__						):
     """simple docstring"""

     _snake_case:		Optional[Any]       =1




     @register_to_config
     def __init__(      self					:				Optional[Any]		,					A__					:				Optional[Any] = 4_0		,					A__					:				str = 0.002		,					A__					:				List[str] = 80.0		,					A__					:				Optional[int] = 0.5		,					A__					:				str = 1.0		,					A__					:				Union[str, Any] = 7.0		,					A__					:				str = True		,					)			->    Optional[int]:
           lowerCamelCase__ 					=			sigma_max

           lowerCamelCase__ 					=			np.linspace(0		,					1		,					lowercase_     )
           lowerCamelCase__ 					=			self._convert_to_karras(lowercase_     )
           lowerCamelCase__ 					=			self.sigma_to_t(lowercase_     )

           # setable values
           lowerCamelCase__ 					=			None
           lowerCamelCase__ 					=			torch.from_numpy(lowercase_     )
           lowerCamelCase__ 					=			torch.from_numpy(lowercase_     )
           lowerCamelCase__ 					=			False
           lowerCamelCase__ 					=			False




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				Dict		,					A__					:				int=None     )			->    Union[str, Any]:






           if schedule_timesteps is None:
                 lowerCamelCase__ 					=			self.timesteps

           lowerCamelCase__ 					=			(schedule_timesteps == timestep).nonzero()
           return indices.item()




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				Optional[int]		,					A__					:				str     )			->    torch.FloatTensor:






           if isinstance(lowercase_		,					torch.Tensor     ):
                 lowerCamelCase__ 					=			timestep.to(self.timesteps.device     )
           lowerCamelCase__ 					=			self.index_for_timestep(lowercase_     )
           lowerCamelCase__ 					=			self.sigmas[step_idx]

           lowerCamelCase__ 					=			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

           lowerCamelCase__ 					=			True
           return sample




     def 	__snake_case				(      self					:				int		,					A__					:				str     )			->    Optional[Any]:






           if not isinstance(lowercase_		,					np.ndarray     ):
                 lowerCamelCase__ 					=			np.array(lowercase_		,					dtype=np.floataa     )

           lowerCamelCase__ 					=			1_0_0_0 * 0.25 * np.log(sigmas + 1E-44     )

           return timesteps




     def 	__snake_case				(      self					:				Tuple		,					A__					:				List[str] = None		,					A__					:				Union[str, Any] = None		,					A__					:				Dict = None		,					)			->    Tuple:






           if num_inference_steps is None and timesteps is None:
                 raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'     )

           if num_inference_steps is not None and timesteps is not None:
                 raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'     )

           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
                 for i in range(1		,					len(lowercase_     )     ):
                       if timesteps[i] >= timesteps[i - 1]:
                             raise ValueError('`timesteps` must be in descending order.'     )

                 if timesteps[0] >= self.config.num_train_timesteps:
                       raise ValueError(
                           F"""`timesteps` must start before `self.config.train_timesteps`:"""
                           F""" {self.config.num_train_timesteps}."""     )

                 lowerCamelCase__ 					=			np.array(lowercase_		,					dtype=np.intaa     )
                 lowerCamelCase__ 					=			True
           else:
                 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."""     )

                 lowerCamelCase__ 					=			num_inference_steps

                 lowerCamelCase__ 					=			self.config.num_train_timesteps // self.num_inference_steps
                 lowerCamelCase__ 					=			(np.arange(0		,					lowercase_     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
                 lowerCamelCase__ 					=			False

           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           lowerCamelCase__ 					=			self.config.num_train_timesteps
           lowerCamelCase__ 					=			timesteps[::-1].copy()
           lowerCamelCase__ 					=			ramp / (num_train_timesteps - 1)
           lowerCamelCase__ 					=			self._convert_to_karras(lowercase_     )
           lowerCamelCase__ 					=			self.sigma_to_t(lowercase_     )

           lowerCamelCase__ 					=			np.concatenate([sigmas, [self.sigma_min]]     ).astype(np.floataa     )
           lowerCamelCase__ 					=			torch.from_numpy(lowercase_     ).to(device=lowercase_     )

           if str(lowercase_     ).startswith('mps'     ):
                 # mps does not support float64
                 lowerCamelCase__ 					=			torch.from_numpy(lowercase_     ).to(lowercase_		,					dtype=torch.floataa     )
           else:
                 lowerCamelCase__ 					=			torch.from_numpy(lowercase_     ).to(device=lowercase_     )




     def 	__snake_case				(      self					:				str		,					A__					:				Any     )			->    str:
           lowerCamelCase__ 					=			self.config.sigma_min
           lowerCamelCase__ 					=			self.config.sigma_max

           lowerCamelCase__ 					=			self.config.rho
           lowerCamelCase__ 					=			sigma_min ** (1 / rho)
           lowerCamelCase__ 					=			sigma_max ** (1 / rho)
           lowerCamelCase__ 					=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]     )			->    Dict:
           lowerCamelCase__ 					=			self.config.sigma_data

           lowerCamelCase__ 					=			sigma_data**2 / (sigma**2 + sigma_data**2)
           lowerCamelCase__ 					=			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				Dict     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.config.sigma_min
           lowerCamelCase__ 					=			self.config.sigma_data

           lowerCamelCase__ 					=			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           lowerCamelCase__ 					=			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]		,					A__					:				str		,					A__					:				Any		,					A__					:				int = None		,					A__					:				Any = True		,					)			->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:






           if (
               isinstance(lowercase_		,					lowercase_     )
               or isinstance(lowercase_		,					torch.IntTensor     )
               or isinstance(lowercase_		,					torch.LongTensor     )
           ):
                 raise ValueError(
                     (
                         'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                         F""" `{self.__class__}.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(lowercase_		,					torch.Tensor     ):
                 lowerCamelCase__ 					=			timestep.to(self.timesteps.device     )

           lowerCamelCase__ 					=			self.config.sigma_min
           lowerCamelCase__ 					=			self.config.sigma_max

           lowerCamelCase__ 					=			self.index_for_timestep(lowercase_     )

           # sigma_next corresponds to next_t in original implementation
           lowerCamelCase__ 					=			self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
                 lowerCamelCase__ 					=			self.sigmas[step_index + 1]
           else:
                 # Set sigma_next to sigma_min
                 lowerCamelCase__ 					=			self.sigmas[-1]

           # Get scalings for boundary conditions
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.get_scalings_for_boundary_condition(lowercase_     )

           # 1. Denoise model output using boundary conditions
           lowerCamelCase__ 					=			c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
                 lowerCamelCase__ 					=			denoised.clamp(-1		,					1     )

           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps     ) > 1:
                 lowerCamelCase__ 					=			randn_tensor(
                     model_output.shape		,					dtype=model_output.dtype		,					device=model_output.device		,					generator=lowercase_     )
           else:
                 lowerCamelCase__ 					=			torch.zeros_like(lowercase_     )
           lowerCamelCase__ 					=			noise * self.config.s_noise

           lowerCamelCase__ 					=			sigma_next.clamp(min=lowercase_		,					max=lowercase_     )

           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           lowerCamelCase__ 					=			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

           if not return_dict:
                 return (prev_sample,)

           return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				str		,					A__					:				str		,					A__					:				Union[str, Any]		,					)			->    torch.FloatTensor:
           lowerCamelCase__ 					=			self.sigmas.to(device=original_samples.device		,					dtype=original_samples.dtype     )
           if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_     ):
                 # mps does not support float64
                 lowerCamelCase__ 					=			self.timesteps.to(original_samples.device		,					dtype=torch.floataa     )
                 lowerCamelCase__ 					=			timesteps.to(original_samples.device		,					dtype=torch.floataa     )
           else:
                 lowerCamelCase__ 					=			self.timesteps.to(original_samples.device     )
                 lowerCamelCase__ 					=			timesteps.to(original_samples.device     )

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

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

           lowerCamelCase__ 					=			original_samples + noise * sigma
           return noisy_samples




     def __len__(      self					:				Tuple     )			->    int:
           return self.config.num_train_timesteps





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


__SCREAMING_SNAKE_CASE      :				Union[str, Any]				      =-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class      A     (						unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Tuple       =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _snake_case:		int       =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




     def 	__snake_case				(      self					:				Any		,					A__					:				str		,					A__					:				int		,					A__					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			SummarizationPipeline(model=A__		,					tokenizer=A__     )
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]




     def 	__snake_case				(      self					:				int		,					A__					:				int		,					A__					:				Tuple     )			->    Tuple:
           lowerCamelCase__ 					=			summarizer.model

           lowerCamelCase__ 					=			summarizer('(CNN)The Palestinian Authority officially became'     )
           self.assertEqual(A__		,					[{'summary_text': ANY(A__     )}]     )

           lowerCamelCase__ 					=			summarizer(
               '(CNN)The Palestinian Authority officially became '		,					num_beams=2		,					min_length=2		,					max_length=5		,					)
           self.assertEqual(A__		,					[{'summary_text': ANY(A__     )}]     )

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           lowerCamelCase__ 					=			[
               'SwitchTransformersConfig',
               'T5Config',
               'LongT5Config',
               'LEDConfig',
               'PegasusXConfig',
               'FSMTConfig',
               'M2M100Config',
               'ProphetNetConfig',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
           ]
           if model.config.__class__.__name__ not in model_can_handle_longer_seq:
                 # Too long and exception is expected.
                 # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
                 # the embedding layer.
                 if not (
                     isinstance(A__		,					A__     )
                     and get_gpu_count() > 0
                     and len(summarizer.model.trainable_weights     ) > 0
                 ):
                       with self.assertRaises(A__     ):
                             lowerCamelCase__ 					=			summarizer('This ' * 1_0_0_0     )
           lowerCamelCase__ 					=			summarizer('This ' * 1_0_0_0		,					truncation=TruncationStrategy.ONLY_FIRST     )




     @require_torch
     def 	__snake_case				(      self					:				str     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			pipeline(task='summarization'		,					model='sshleifer/tiny-mbart'		,					framework='pt'     )
           lowerCamelCase__ 					=			summarizer('This is a small test'     )
           self.assertEqual(
               A__		,					[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]		,					)




     @require_tf
     def 	__snake_case				(      self					:				List[str]     )			->    List[str]:
           lowerCamelCase__ 					=			pipeline(task='summarization'		,					model='sshleifer/tiny-mbart'		,					framework='tf'     )
           lowerCamelCase__ 					=			summarizer('This is a small test'     )
           self.assertEqual(
               A__		,					[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]		,					)




     @require_torch
     @slow
     def 	__snake_case				(      self					:				List[str]     )			->    Tuple:
           lowerCamelCase__ 					=			pipeline(task='summarization'		,					device=A__     )
           lowerCamelCase__ 					=			(
               ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'
               ' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'
               ' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'
               ' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'
               ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
               ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
               ' situation in Palestinian territories, paving the way for possible war crimes investigations against'
               ' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'
               ' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'
               ' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'
               ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
               ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
               ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
               ' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'
               ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
               ' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'
               ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
               ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
               ' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'
               ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
               ' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'
               ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
               ' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'
               ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
               ' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'
               ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
               ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
               ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
               ' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'
               ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
               ' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'
               ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
               ' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'
               ' will include alleged war crimes committed since June. The International Criminal Court was set up in'
               ' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'
               ' and Faith Karimi contributed to this report.'
           )
           lowerCamelCase__ 					=			(
               ' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'
               ' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'
               ' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'
               ' says governments seeking to penalize Palestine should end pressure .'
           )
           lowerCamelCase__ 					=			summarizer(A__     )
           self.assertEqual(result[0]['summary_text']		,					A__     )