

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Any          =					(LMSDiscreteScheduler,)
      lowerCamelCase_	:      List[str]          =					10







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Any:
        snake_case_       						=      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,  [0.0002, 0.002, 0.02]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for t in [0, 500, 800]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       str:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        scheduler.set_timesteps(self.num_inference_steps   )

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps   ):
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 1006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(prediction_type="""v_prediction"""   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        scheduler.set_timesteps(self.num_inference_steps   )

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps   ):
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 0.0017   ) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        scheduler.set_timesteps(self.num_inference_steps							,  device=lowerCamelCase   )

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case_       						=      sample.to(lowerCamelCase   )

        for i, t in enumerate(scheduler.timesteps   ):
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 1006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase							,  use_karras_sigmas=lowerCamelCase   )

        scheduler.set_timesteps(self.num_inference_steps							,  device=lowerCamelCase   )

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter.to(lowerCamelCase   ) * scheduler.init_noise_sigma
        snake_case_       						=      sample.to(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 3812.9927   ) < 2e-2
        assert abs(result_mean.item() - 4.9648   ) < 1e-3


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class  __lowerCamelCase				(	unittest.TestCase	):
      lowerCamelCase_	:      Tuple          =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      lowerCamelCase_	:      int          =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        if isinstance(model.config							,  lowerCamelCase   ):
          snake_case_						,				snake_case_       						=      list(tokenizer.lang_code_to_id.keys()   )[:2]
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang=lowerCamelCase							,  tgt_lang=lowerCamelCase   )
        else:
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        return translator, ["Some string", "Some other text"]







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        snake_case_       						=      translator("""Some string"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

        snake_case_       						=      translator(["""Some string"""]   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

        snake_case_       						=      translator(["""Some string""", """other string"""]   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}, {"""translation_text""": ANY(lowerCamelCase   )}]   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                        """ Beide Beide"""
                    )
                }
            ]							,  )







      @require_tf
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                        """ Beide Beide"""
                    )
                }
            ]							,  )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                        """ urine urine urine urine urine urine urine"""
                    )
                }
            ]							,  )







      @require_tf
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                        """ urine urine urine urine urine urine urine"""
                    )
                }
            ]							,  )







class  __lowerCamelCase				(	unittest.TestCase	):







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # We don't provide a default for this pair
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation_cn_to_ar"""   )

        # but we do for this one
        snake_case_       						=      pipeline(task="""translation_en_to_de"""   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """en"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """de"""   )







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )
        snake_case_       						=      MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )

        snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        # Missing src_lang, tgt_lang
        with self.assertRaises(lowerCamelCase   ):
          translator("""This is a test"""   )

        snake_case_       						=      translator("""This is a test"""							,  src_lang="""en_XX"""							,  tgt_lang="""ar_AR"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """هذا إختبار"""}]   )

        snake_case_       						=      translator("""This is a test"""							,  src_lang="""en_XX"""							,  tgt_lang="""hi_IN"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """यह एक परीक्षण है"""}]   )

        # src_lang, tgt_lang can be defined at pipeline call time
        snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang="""en_XX"""							,  tgt_lang="""ar_AR"""   )
        snake_case_       						=      translator("""This is a test"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """هذا إختبار"""}]   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        snake_case_       						=      pipeline(task="""translation_cn_to_ar"""							,  model=lowerCamelCase   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """cn"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """ar"""   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        with pytest.warns(lowerCamelCase							,  match=r""".*translation_en_to_de.*"""   ):
          snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase   )
        self.assertEqual(translator.task							,  """translation_en_to_de"""   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """en"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """de"""   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       Any:
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation"""   )