





'''simple docstring'''

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  a_					(    unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCamelCase			   =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


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


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

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

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


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


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


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





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




class  a_					(    unittest.TestCase	):

    '''simple docstring'''


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

        # but we do for this one
        _SCREAMING_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       snake_case_(		self	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""	)
        _SCREAMING_SNAKE_CASE     =      MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""	)

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

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

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

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


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


    @require_torch
    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      """patrickvonplaten/t5-tiny-random"""
        with pytest.warns(A			,		match=R""".*translation_en_to_de.*"""	):
            _SCREAMING_SNAKE_CASE     =      pipeline(task="""translation"""			,		model=A	)
        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       snake_case_(		self	)		->       Dict:
        with self.assertRaises(A	):
            pipeline(task="""translation"""	)










'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        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=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


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


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

        scheduler.set_timesteps(self.num_inference_steps	)

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

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

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


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

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

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

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


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

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

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

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





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

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

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

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



