
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 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						lowerCAmelCase_							=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






						def 	snake_case__						(						self							:					str	,     __lowercase							:					Tuple	,     __lowercase							:					Dict	,     __lowercase							:					str						):






							"""simple docstring"""






							if isinstance(model.config	,     __lowercase						):
								snake_case_		   =					list(tokenizer.lang_code_to_id.keys()						)[:2]
								snake_case_		   =					TranslationPipeline(model=__lowercase	,     tokenizer=__lowercase	,     src_lang=__lowercase	,     tgt_lang=__lowercase						)
							else:
								snake_case_		   =					TranslationPipeline(model=__lowercase	,     tokenizer=__lowercase						)
							return translator, ["Some string", "Some other text"]






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					List[Any]	,     __lowercase							:					str						):






							"""simple docstring"""






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

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

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






						@require_torch
						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							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(
							    __lowercase	,     [
							        {
							            "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							:					Dict						):






							"""simple docstring"""






							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(
							    __lowercase	,     [
							        {
							            "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							:					Optional[int]						):






							"""simple docstring"""






							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(
							    __lowercase	,     [
							        {
							            "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							:					Any						):






							"""simple docstring"""






							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(
							    __lowercase	,     [
							        {
							            "translation_text": (
							                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
							                " urine urine urine urine urine urine urine"
							            )
							        }
							    ]	,     )







class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						@require_torch
						@slow
						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							with self.assertRaises(__lowercase						):
								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 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							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=__lowercase	,     tokenizer=__lowercase						)
							# Missing src_lang, tgt_lang
							with self.assertRaises(__lowercase						):
								translator("This is a test"						)

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

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

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






						@require_torch
						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							snake_case_		   =					"patrickvonplaten/t5-tiny-random"
							snake_case_		   =					pipeline(task="translation_cn_to_ar"	,     model=__lowercase						)
							self.assertEqual(translator._preprocess_params["src_lang"]	,     "cn"						)
							self.assertEqual(translator._preprocess_params["tgt_lang"]	,     "ar"						)






						@require_torch
						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					"patrickvonplaten/t5-tiny-random"
							with pytest.warns(__lowercase	,     match=r".*translation_en_to_de.*"						):
								snake_case_		   =					pipeline(task="translation"	,     model=__lowercase						)
							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							:					Union[str, Any]						):






							"""simple docstring"""






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






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							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=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

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

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

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

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

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

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

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

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

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

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

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

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

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

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

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
