




"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Union[str, Any]        =       (KDPMaAncestralDiscreteScheduler,)
			a__	: Optional[int]        =       10


			def 							lowerCamelCase_			(       self			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			{
					    "num_train_timesteps": 1_100,
					    "beta_start": 0.00_01,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					}

					config.update(**SCREAMING_SNAKE_CASE							)
					return config


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,	[0.00_02, 0.0_02, 0.02]							):
							self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE			,	beta_end=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if torch_device == "mps":
							return
					snake_case						:  Dict        =			self.scheduler_classes[0]
					snake_case						:  Optional[int]        =			self.get_scheduler_config()
					snake_case						:  Optional[Any]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					scheduler.set_timesteps(self.num_inference_steps							)

					snake_case						:  str        =			torch.manual_seed(0							)

					snake_case						:  Any        =			self.dummy_model()
					snake_case						:  str        =			self.dummy_sample_deter * scheduler.init_noise_sigma
					snake_case						:  str        =			sample.to(SCREAMING_SNAKE_CASE							)

					for i, t in enumerate(scheduler.timesteps							):
							snake_case						:  List[Any]        =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[Any]        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  List[str]        =			scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)
							snake_case						:  Any        =			output.prev_sample

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

					assert abs(result_sum.item() - 1_38_49.38_77							) < 1E-2
					assert abs(result_mean.item() - 18.03_31							) < 5E-3


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if torch_device == "mps":
							return
					snake_case						:  Dict        =			self.scheduler_classes[0]
					snake_case						:  List[str]        =			self.get_scheduler_config(prediction_type="v_prediction"							)
					snake_case						:  Optional[int]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					scheduler.set_timesteps(self.num_inference_steps							)

					snake_case						:  Tuple        =			self.dummy_model()
					snake_case						:  Optional[int]        =			self.dummy_sample_deter * scheduler.init_noise_sigma
					snake_case						:  Optional[Any]        =			sample.to(SCREAMING_SNAKE_CASE							)

					snake_case						:  Optional[Any]        =			torch.manual_seed(0							)

					for i, t in enumerate(scheduler.timesteps							):
							snake_case						:  Union[str, Any]        =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Any        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[int]        =			scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			output.prev_sample

					snake_case						:  int        =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
					snake_case						:  Union[str, Any]        =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)

					assert abs(result_sum.item() - 3_28.99_70							) < 1E-2
					assert abs(result_mean.item() - 0.42_84							) < 1E-3



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if torch_device == "mps":
							return
					snake_case						:  List[str]        =			self.scheduler_classes[0]
					snake_case						:  List[str]        =			self.get_scheduler_config()
					snake_case						:  List[Any]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					scheduler.set_timesteps(self.num_inference_steps			,	device=SCREAMING_SNAKE_CASE							)
					snake_case						:  Dict        =			torch.manual_seed(0							)

					snake_case						:  Optional[int]        =			self.dummy_model()
					snake_case						:  Union[str, Any]        =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE							) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							snake_case						:  List[str]        =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[int]        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  List[str]        =			scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			output.prev_sample

					snake_case						:  Union[str, Any]        =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
					snake_case						:  Dict        =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)

					assert abs(result_sum.item() - 1_38_49.38_18							) < 1E-1
					assert abs(result_mean.item() - 18.03_31							) < 1E-3







"""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      lowerCamelCase__      (					unittest.TestCase     ):
			a__	: List[str]        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			a__	: List[Any]        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(model.config			,	SCREAMING_SNAKE_CASE							):
							snake_case     ,			snake_case						:  Optional[Any]        =			list(tokenizer.lang_code_to_id.keys()							)[:2]
							snake_case						:  Tuple        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang=SCREAMING_SNAKE_CASE			,	tgt_lang=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  List[Any]        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					return translator, ["Some string", "Some other text"]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			translator("Some string"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  Optional[Any]        =			translator(["Some string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  List[Any]        =			translator(["Some string", "other string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  Union[str, Any]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  int        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  List[str]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "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							):



					"""simple docstring"""







					snake_case						:  List[str]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  Tuple        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "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							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation_cn_to_ar"							)

					# but we do for this one
					snake_case						:  List[str]        =			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							):



					"""simple docstring"""







					snake_case						:  Any        =			MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
					snake_case						:  Union[str, Any]        =			MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

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

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

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

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


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"patrickvonplaten/t5-tiny-random"
					snake_case						:  Tuple        =			pipeline(task="translation_cn_to_ar"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"cn"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"ar"							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			"patrickvonplaten/t5-tiny-random"
					with pytest.warns(SCREAMING_SNAKE_CASE			,	match=r".*translation_en_to_de.*"							):
							snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE							)
					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							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation"							)

