


'''simple docstring'''







import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
    a									=					(ScoreSdeVeScheduler,)
    a									=					()


    @property
    def      A					(						self    :							Any):
           _A  :			Any											=      4
           _A  :			Any											=      3
           _A  :			Optional[int]											=      8
           _A  :			Optional[Any]											=      8

           _A  :			Optional[Any]											=      torch.rand((batch_size, num_channels, height, width))

           return sample


    @property
    def      A					(						self    :							Tuple):
           _A  :			int											=      4
           _A  :			Dict											=      3
           _A  :			str											=      8
           _A  :			Any											=      8

           _A  :			Tuple											=      batch_size * num_channels * height * width
           _A  :			Union[str, Any]											=      torch.arange(SCREAMING_SNAKE_CASE)
           _A  :			Dict											=      sample.reshape(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
           _A  :			Optional[Any]											=      sample / num_elems
           _A  :			Optional[int]											=      sample.permute(3   ,							0   ,							1   ,							2)

           return sample


    def      A					(						self    :							Tuple):







           def model(SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							*SCREAMING_SNAKE_CASE    :							Any):
                  return sample * t / (t + 1)

           return model


    def      A					(						self    :							List[str]   ,							**SCREAMING_SNAKE_CASE    :							Tuple):
           _A  :			str											=      {
               "num_train_timesteps": 2000,
               "snr": 0.15,
               "sigma_min": 0.01,
               "sigma_max": 1348,
               "sampling_eps": 1e-5,
           }

           config.update(**SCREAMING_SNAKE_CASE)
           return config


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=0   ,							**SCREAMING_SNAKE_CASE    :							Tuple):
           _A  :			int											=      dict(self.forward_default_kwargs)

           for scheduler_class in self.scheduler_classes:
                  _A  :			Any											=      self.dummy_sample
                  _A  :			Union[str, Any]											=      0.1 * sample

                  _A  :			Union[str, Any]											=      self.get_scheduler_config(**SCREAMING_SNAKE_CASE)
                  _A  :			str											=      scheduler_class(**SCREAMING_SNAKE_CASE)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(SCREAMING_SNAKE_CASE)
                         _A  :			int											=      scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE)

                  _A  :			List[Any]											=      scheduler.step_pred(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A  :			Dict											=      new_scheduler.step_pred(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample

                  assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

                  _A  :			List[Any]											=      scheduler.step_correct(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A  :			Optional[Any]											=      new_scheduler.step_correct(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample

                  assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							int=0   ,							**SCREAMING_SNAKE_CASE    :							Tuple):
           _A  :			List[Any]											=      dict(self.forward_default_kwargs)
           kwargs.update(SCREAMING_SNAKE_CASE)

           for scheduler_class in self.scheduler_classes:
                  _A  :			Tuple											=      self.dummy_sample
                  _A  :			Optional[int]											=      0.1 * sample

                  _A  :			Union[str, Any]											=      self.get_scheduler_config()
                  _A  :			Optional[int]											=      scheduler_class(**SCREAMING_SNAKE_CASE)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(SCREAMING_SNAKE_CASE)
                         _A  :			Optional[int]											=      scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE)

                  _A  :			Any											=      scheduler.step_pred(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A  :			Optional[Any]											=      new_scheduler.step_pred(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample

                  assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

                  _A  :			Any											=      scheduler.step_correct(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A  :			Optional[int]											=      new_scheduler.step_correct(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample

                  assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"


    def      A					(						self    :							int):







           for timesteps in [10, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Dict):







           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]   ,							[1, 100, 1000]):
                  self.check_over_configs(sigma_min=SCREAMING_SNAKE_CASE   ,							sigma_max=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple):







           for t in [0.1, 0.5, 0.75]:
                  self.check_over_forward(time_step=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Union[str, Any]):
           _A  :			List[str]											=      dict(self.forward_default_kwargs)

           _A  :			Optional[Any]											=      self.scheduler_classes[0]
           _A  :			Any											=      self.get_scheduler_config()
           _A  :			Any											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           _A  :			List[str]											=      3

           _A  :			List[str]											=      self.dummy_model()
           _A  :			Tuple											=      self.dummy_sample_deter

           scheduler.set_sigmas(SCREAMING_SNAKE_CASE)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
           _A  :			Union[str, Any]											=      torch.manual_seed(0)

           for i, t in enumerate(scheduler.timesteps):
                  _A  :			Optional[int]											=      scheduler.sigmas[i]

                  for _ in range(scheduler.config.correct_steps):
                         with torch.no_grad():
                                _A  :			Dict											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                         _A  :			Optional[Any]											=      scheduler.step_correct(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE).prev_sample

                  with torch.no_grad():
                         _A  :			Tuple											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			int											=      scheduler.step_pred(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)
                  _A  :			List[str]											=      output.prev_sample, output.prev_sample_mean

           _A  :			Any											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			List[str]											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert np.isclose(result_sum.item()   ,							14372758528.0)
           assert np.isclose(result_mean.item()   ,							18714530.0)


    def      A					(						self    :							List[Any]):
           _A  :			Union[str, Any]											=      dict(self.forward_default_kwargs)

           _A  :			List[Any]											=      kwargs.pop('num_inference_steps'   ,							SCREAMING_SNAKE_CASE)

           for scheduler_class in self.scheduler_classes:
                  _A  :			int											=      self.get_scheduler_config()
                  _A  :			Optional[int]											=      scheduler_class(**SCREAMING_SNAKE_CASE)

                  _A  :			Any											=      self.dummy_sample
                  _A  :			Any											=      0.1 * sample

                  if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE   ,							'set_timesteps'):
                         scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
                  elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE   ,							'set_timesteps'):
                         _A  :			Tuple											=      num_inference_steps

                  _A  :			List[Any]											=      scheduler.step_pred(SCREAMING_SNAKE_CASE   ,							0   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A  :			Dict											=      scheduler.step_pred(SCREAMING_SNAKE_CASE   ,							1   ,							SCREAMING_SNAKE_CASE   ,							generator=torch.manual_seed(0)   ,							**SCREAMING_SNAKE_CASE).prev_sample

                  self.assertEqual(output_a.shape   ,							sample.shape)
                  self.assertEqual(output_a.shape   ,							output_a.shape)










'''simple docstring'''







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


A   :  Optional[Any]       		=				-1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    a									=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    a									=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			str											=      SummarizationPipeline(model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE)
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           _A  :			str											=      summarizer.model

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

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

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           _A  :			Union[str, Any]											=      [
               '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(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                      and get_gpu_count() > 0
                      and len(summarizer.model.trainable_weights) > 0
                  ):
                         with self.assertRaises(SCREAMING_SNAKE_CASE):
                                _A  :			Any											=      summarizer('This ' * 1000)
           _A  :			int											=      summarizer('This ' * 1000   ,							truncation=TruncationStrategy.ONLY_FIRST)


    @require_torch
    def      A					(						self    :							int):
           _A  :			Tuple											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='pt')
           _A  :			List[str]											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_tf
    def      A					(						self    :							int):
           _A  :			Optional[Any]											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='tf')
           _A  :			Any											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_torch
    @slow
    def      A					(						self    :							List[Any]):
           _A  :			int											=      pipeline(task='summarization'   ,							device=SCREAMING_SNAKE_CASE)
           _A  :			int											=      (
               ' (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.'
           )
           _A  :			Optional[Any]											=      (
               ' 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 .'
           )
           _A  :			Union[str, Any]											=      summarizer(SCREAMING_SNAKE_CASE)
           self.assertEqual(result[0]['summary_text']   ,							SCREAMING_SNAKE_CASE)






