import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  _snake_case ):
 __lowerCamelCase     : Union[str, Any] 	=   (DPMSolverMultistepScheduler,)
 __lowerCamelCase     : Dict 	=   (("""num_inference_steps""", 25),)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	={
           "num_train_timesteps": 1000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "solver_order": 2,
           "prediction_type": "epsilon",
           "thresholding": False,
           "sample_max_value": 1.0,
           "algorithm_type": "dpmsolver++",
           "solver_type": "midpoint",
           "lower_order_final": False,
           "lambda_min_clipped": -float('''inf'''    ),
           "variance_type": None,
       }

       config.update(**snake_case_    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__=0		, **snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=dict(self.forward_default_kwargs    )
       UpperCAmelCase     :			List[str]     	=kwargs.pop('''num_inference_steps'''		, snake_case_    )
       UpperCAmelCase     :			List[Any]     	=self.dummy_sample
       UpperCAmelCase     :			List[Any]     	=0.1 * sample
       UpperCAmelCase     :			Union[str, Any]     	=[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase     :			Any     	=self.get_scheduler_config(**snake_case_    )
             UpperCAmelCase     :			str     	=scheduler_class(**snake_case_    )
             scheduler.set_timesteps(snake_case_    )
             # copy over dummy past residuals
             UpperCAmelCase     :			int     	=dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(snake_case_    )
                   UpperCAmelCase     :			List[Any]     	=scheduler_class.from_pretrained(snake_case_    )
                   new_scheduler.set_timesteps(snake_case_    )
                   # copy over dummy past residuals
                   UpperCAmelCase     :			Any     	=dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCAmelCase     :			str     	=sample, sample
             for t in range(snake_case_		, time_step + scheduler.config.solver_order + 1    ):
                   UpperCAmelCase     :			Optional[int]     	=scheduler.step(snake_case_		, snake_case_		, snake_case_		, **snake_case_    ).prev_sample
                   UpperCAmelCase     :			str     	=new_scheduler.step(snake_case_		, snake_case_		, snake_case_		, **snake_case_    ).prev_sample

                   assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       pass
 def 						UpperCAmelCase__							(	self		, snake_case__=0		, **snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=dict(self.forward_default_kwargs    )
       UpperCAmelCase     :			Any     	=kwargs.pop('''num_inference_steps'''		, snake_case_    )
       UpperCAmelCase     :			Tuple     	=self.dummy_sample
       UpperCAmelCase     :			Dict     	=0.1 * sample
       UpperCAmelCase     :			Optional[Any]     	=[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase     :			List[str]     	=self.get_scheduler_config()
             UpperCAmelCase     :			str     	=scheduler_class(**snake_case_    )
             scheduler.set_timesteps(snake_case_    )

             # copy over dummy past residuals (must be after setting timesteps)
             UpperCAmelCase     :			Dict     	=dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(snake_case_    )
                   UpperCAmelCase     :			Optional[int]     	=scheduler_class.from_pretrained(snake_case_    )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(snake_case_    )

                   # copy over dummy past residual (must be after setting timesteps)
                   UpperCAmelCase     :			List[str]     	=dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCAmelCase     :			List[Any]     	=scheduler.step(snake_case_		, snake_case_		, snake_case_		, **snake_case_    ).prev_sample
             UpperCAmelCase     :			Optional[int]     	=new_scheduler.step(snake_case_		, snake_case_		, snake_case_		, **snake_case_    ).prev_sample

             assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"
 def 						UpperCAmelCase__							(	self		, snake_case__=None		, **snake_case__    )						->				str:
       '''simple docstring'''





       if scheduler is None:
             UpperCAmelCase     :			Tuple     	=self.scheduler_classes[0]
             UpperCAmelCase     :			Tuple     	=self.get_scheduler_config(**snake_case_    )
             UpperCAmelCase     :			int     	=scheduler_class(**snake_case_    )

       UpperCAmelCase     :			Tuple     	=10
       UpperCAmelCase     :			Union[str, Any]     	=self.dummy_model()
       UpperCAmelCase     :			Dict     	=self.dummy_sample_deter
       scheduler.set_timesteps(snake_case_    )

       for i, t in enumerate(scheduler.timesteps    ):
             UpperCAmelCase     :			int     	=model(snake_case_		, snake_case_    )
             UpperCAmelCase     :			str     	=scheduler.step(snake_case_		, snake_case_		, snake_case_    ).prev_sample

       return sample
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=dict(self.forward_default_kwargs    )

       UpperCAmelCase     :			int     	=kwargs.pop('''num_inference_steps'''		, snake_case_    )

       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase     :			Optional[int]     	=self.get_scheduler_config()
             UpperCAmelCase     :			Tuple     	=scheduler_class(**snake_case_    )

             UpperCAmelCase     :			List[str]     	=self.dummy_sample
             UpperCAmelCase     :			Union[str, Any]     	=0.1 * sample

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

             # copy over dummy past residuals (must be done after set_timesteps)
             UpperCAmelCase     :			List[Any]     	=[residual + 0.2, residual + 0.15, residual + 0.10]
             UpperCAmelCase     :			Any     	=dummy_past_residuals[: scheduler.config.solver_order]

             UpperCAmelCase     :			Optional[Any]     	=scheduler.timesteps[5]
             UpperCAmelCase     :			str     	=scheduler.timesteps[6]

             UpperCAmelCase     :			str     	=scheduler.step(snake_case_		, snake_case_		, snake_case_		, **snake_case_    ).prev_sample
             UpperCAmelCase     :			Any     	=scheduler.step(snake_case_		, snake_case_		, snake_case_		, **snake_case_    ).prev_sample

             self.assertEqual(output_a.shape		, sample.shape    )
             self.assertEqual(output_a.shape		, output_a.shape    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       for timesteps in [25, 50, 100, 999, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case_    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       self.check_over_configs(thresholding=snake_case_    )
       for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                   for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   thresholding=snake_case_		, prediction_type=snake_case_		, sample_max_value=snake_case_		, algorithm_type='''dpmsolver++'''		, solver_order=snake_case_		, solver_type=snake_case_		, )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case_    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                               if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                     if order == 3:
                                           continue
                               else:
                                     self.check_over_configs(
                                         solver_order=snake_case_		, solver_type=snake_case_		, prediction_type=snake_case_		, algorithm_type=snake_case_		, )
                               UpperCAmelCase     :			Optional[Any]     	=self.full_loop(
                                   solver_order=snake_case_		, solver_type=snake_case_		, prediction_type=snake_case_		, algorithm_type=snake_case_		, )
                               assert not torch.isnan(snake_case_    ).any(), "Samples have nan numbers"
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       self.check_over_configs(lower_order_final=snake_case_    )
       self.check_over_configs(lower_order_final=snake_case_    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       self.check_over_configs(lambda_min_clipped=-float('''inf'''    )    )
       self.check_over_configs(lambda_min_clipped=-5.1    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       self.check_over_configs(variance_type=snake_case_    )
       self.check_over_configs(variance_type='''learned_range'''    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
             self.check_over_forward(num_inference_steps=snake_case_		, time_step=0    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=self.full_loop()
       UpperCAmelCase     :			List[str]     	=torch.mean(torch.abs(snake_case_    )    )

       assert abs(result_mean.item() - 0.3301    ) < 1e-3
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.full_loop(thresholding=snake_case_		, dynamic_thresholding_ratio=0.87		, sample_max_value=0.5    )
       UpperCAmelCase     :			Optional[int]     	=torch.mean(torch.abs(snake_case_    )    )

       assert abs(result_mean.item() - 1.1364    ) < 1e-3
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.full_loop(prediction_type='''v_prediction'''    )
       UpperCAmelCase     :			Union[str, Any]     	=torch.mean(torch.abs(snake_case_    )    )

       assert abs(result_mean.item() - 0.2251    ) < 1e-3
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.full_loop(prediction_type='''v_prediction'''		, use_karras_sigmas=snake_case_    )
       UpperCAmelCase     :			List[Any]     	=torch.mean(torch.abs(snake_case_    )    )

       assert abs(result_mean.item() - 0.2096    ) < 1e-3
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
       UpperCAmelCase     :			Dict     	=self.full_loop(scheduler=snake_case_    )
       UpperCAmelCase     :			List[str]     	=torch.mean(torch.abs(snake_case_    )    )

       assert abs(result_mean.item() - 0.3301    ) < 1e-3

       UpperCAmelCase     :			Optional[Any]     	=DPMSolverSinglestepScheduler.from_config(scheduler.config    )
       UpperCAmelCase     :			Dict     	=UniPCMultistepScheduler.from_config(scheduler.config    )
       UpperCAmelCase     :			List[Any]     	=DEISMultistepScheduler.from_config(scheduler.config    )
       UpperCAmelCase     :			Tuple     	=DPMSolverMultistepScheduler.from_config(scheduler.config    )

       UpperCAmelCase     :			Dict     	=self.full_loop(scheduler=snake_case_    )
       UpperCAmelCase     :			Optional[Any]     	=torch.mean(torch.abs(snake_case_    )    )

       assert abs(result_mean.item() - 0.3301    ) < 1e-3
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=self.scheduler_classes[0]
       UpperCAmelCase     :			Tuple     	=self.get_scheduler_config(thresholding=snake_case_		, dynamic_thresholding_ratio=0    )
       UpperCAmelCase     :			Any     	=scheduler_class(**snake_case_    )

       UpperCAmelCase     :			int     	=10
       UpperCAmelCase     :			int     	=self.dummy_model()
       UpperCAmelCase     :			str     	=self.dummy_sample_deter.half()
       scheduler.set_timesteps(snake_case_    )

       for i, t in enumerate(scheduler.timesteps    ):
             UpperCAmelCase     :			Tuple     	=model(snake_case_		, snake_case_    )
             UpperCAmelCase     :			List[str]     	=scheduler.step(snake_case_		, snake_case_		, snake_case_    ).prev_sample

       assert sample.dtype == torch.floataa






 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase     :			Union[str, Any]     	=self.get_scheduler_config(**snake_case_    )
             UpperCAmelCase     :			Any     	=scheduler_class(**snake_case_    )

             scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
             assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps


"""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   :      List[Any]       =				-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class   _UpperCAmelCase					(       unittest.TestCase):
    __lowercase				:			List[str]	=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __lowercase				:			str	=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = SummarizationPipeline(model=snake_case_    ,     tokenizer=snake_case_  )
      return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = summarizer.model

      _snake_case  :    Optional[int]            = summarizer("(CNN)The Palestinian Authority officially became"  )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      _snake_case  :    List[str]            = summarizer(
          "(CNN)The Palestinian Authority officially became "    ,     num_beams=2    ,     min_length=2    ,     max_length=5    ,     )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
      _snake_case  :    Tuple            = [
          "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(snake_case_    ,     snake_case_  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
          with self.assertRaises(snake_case_  ):
            _snake_case  :    Tuple            = summarizer("This " * 10_00  )
      _snake_case  :    Any            = summarizer("This " * 10_00    ,     truncation=TruncationStrategy.ONLY_FIRST  )
    @require_torch
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="pt"  )
      _snake_case  :    List[Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )
    @require_tf
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="tf"  )
      _snake_case  :    Union[str, Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )


    @require_torch
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = pipeline(task="summarization"    ,     device=snake_case_  )
      _snake_case  :    Any            = (
          " (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."
      )
      _snake_case  :    int            = (
          " 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 ."
      )
      _snake_case  :    Union[str, Any]            = summarizer(snake_case_  )
      self.assertEqual(result[0]["summary_text"]    ,     snake_case_  )

