




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__     )





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(DPMSolverMultistepScheduler,)
     _snake_case:		Union[str, Any]       =(("num_inference_steps", 25),)




     def 	__snake_case				(      self					:				str		,					**A__					:				Union[str, Any]     )			->    str:
           lowerCamelCase__ 					=			{
               'num_train_timesteps': 1_0_0_0,
               '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(**A__     )
           return config




     def 	__snake_case				(      self					:				Dict		,					A__					:				Dict=0		,					**A__					:				Dict     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )
                 # copy over dummy past residuals
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

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

                 lowerCamelCase__					,						lowerCamelCase__ 					=			sample, sample
                 for t in range(A__		,					time_step + scheduler.config.solver_order + 1     ):
                       lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                       lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				List[str]     )			->    Union[str, Any]:
           pass




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]=0		,					**A__					:				Optional[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(A__     )

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

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Any=None		,					**A__					:				Any     )			->    List[str]:
           if scheduler is None:
                 lowerCamelCase__ 					=			self.scheduler_classes[0]
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           return sample




     def 	__snake_case				(      self					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )

           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 lowerCamelCase__ 					=			self.dummy_sample
                 lowerCamelCase__ 					=			0.1 * sample

                 if num_inference_steps is not None and hasattr(A__		,					'set_timesteps'     ):
                       scheduler.set_timesteps(A__     )
                 elif num_inference_steps is not None and not hasattr(A__		,					'set_timesteps'     ):
                       lowerCamelCase__ 					=			num_inference_steps

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

                 lowerCamelCase__ 					=			scheduler.timesteps[5]
                 lowerCamelCase__ 					=			scheduler.timesteps[6]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Dict     )			->    Tuple:
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__     )




     def 	__snake_case				(      self					:				int     )			->    str:
           self.check_over_configs(thresholding=A__     )
           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=A__		,					prediction_type=A__		,					sample_max_value=A__		,					algorithm_type='dpmsolver++'		,					solver_order=A__		,					solver_type=A__		,					)




     def 	__snake_case				(      self					:				Any     )			->    Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    str:
           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=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   lowerCamelCase__ 					=			self.full_loop(
                                       solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   assert not torch.isnan(A__     ).any(), "Samples have nan numbers"




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
           self.check_over_configs(lower_order_final=A__     )
           self.check_over_configs(lower_order_final=A__     )




     def 	__snake_case				(      self					:				List[Any]     )			->    Dict:
           self.check_over_configs(lambda_min_clipped=-float('inf'     )     )
           self.check_over_configs(lambda_min_clipped=-5.1     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    int:
           self.check_over_configs(variance_type=A__     )
           self.check_over_configs(variance_type='learned_range'     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[str]:
           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__		,					time_step=0     )




     def 	__snake_case				(      self					:				List[Any]     )			->    str:
           lowerCamelCase__ 					=			self.full_loop()
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

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




     def 	__snake_case				(      self					:				Tuple     )			->    int:
           lowerCamelCase__ 					=			self.full_loop(thresholding=A__		,					dynamic_thresholding_ratio=0.87		,					sample_max_value=0.5     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 1.1364     ) < 1E-3




     def 	__snake_case				(      self					:				Optional[int]     )			->    List[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2251     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'		,					use_karras_sigmas=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2096     ) < 1E-3




     def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

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

           lowerCamelCase__ 					=			DPMSolverSinglestepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			UniPCMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DEISMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler.from_config(scheduler.config     )

           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

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




     def 	__snake_case				(      self					:				int     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config(thresholding=A__		,					dynamic_thresholding_ratio=0     )
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.half()
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           assert sample.dtype == torch.floataa




     def 	__snake_case				(      self					:				List[str]		,					**A__					:				int     )			->    Optional[int]:
           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

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