
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'''





		snake_case_      					=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		snake_case_      					=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






		def        UpperCamelCase_						( self      :				Any							,A      :				str							,A      :				Any							,A      :				Any				):
						if isinstance(model.config							,A				):
										__A    ,							__A         =				list(tokenizer.lang_code_to_id.keys()				)[:2]
										__A         =				TranslationPipeline(model=A							,tokenizer=A							,src_lang=A							,tgt_lang=A				)
						else:
										__A         =				TranslationPipeline(model=A							,tokenizer=A				)
						return translator, ["Some string", "Some other text"]






		def        UpperCamelCase_						( self      :				List[Any]							,A      :				List[str]							,A      :				List[str]				):
						__A         =				translator("Some string"				)
						self.assertEqual(A							,[{"translation_text": ANY(A				)}]				)

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

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






		@require_torch
		def        UpperCamelCase_						( self      :				str				):
						__A         =				pipeline("translation_en_to_ro"							,model="patrickvonplaten/t5-tiny-random"							,framework="pt"				)
						__A         =				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        UpperCamelCase_						( self      :				Any				):
						__A         =				pipeline("translation_en_to_ro"							,model="patrickvonplaten/t5-tiny-random"							,framework="tf"				)
						__A         =				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        UpperCamelCase_						( self      :				Optional[int]				):
						__A         =				pipeline("translation_en_to_de"							,model="patrickvonplaten/t5-tiny-random"							,framework="pt"				)
						__A         =				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        UpperCamelCase_						( self      :				Tuple				):
						__A         =				pipeline("translation_en_to_de"							,model="patrickvonplaten/t5-tiny-random"							,framework="tf"				)
						__A         =				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 							UpperCAmelCase   (						unittest.TestCase						):




		'''simple docstring'''





		@require_torch
		@slow
		def        UpperCamelCase_						( 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
						__A         =				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        UpperCamelCase_						( self      :				Optional[Any]				):
						__A         =				MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"				)
						__A         =				MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"				)

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

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

						__A         =				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
						__A         =				pipeline(task="translation"							,model=A							,tokenizer=A							,src_lang="en_XX"							,tgt_lang="ar_AR"				)
						__A         =				translator("This is a test"				)
						self.assertEqual(A							,[{"translation_text": "هذا إختبار"}]				)






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


from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
					from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
