
'''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 		__lowercase			(					unittest.TestCase      ):
			lowerCamelCase     :						List[Any]		 =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			lowerCamelCase     :						Tuple		 =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):

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

			def     UpperCAmelCase__	(self     ,				A     ,				A ):
								lowerCamelCase_   : Tuple       =						translator('''Some string''' )
								self.assertEqual(A     ,				[{'''translation_text''': ANY(A )}] )

								lowerCamelCase_   : Dict       =						translator(['''Some string'''] )
								self.assertEqual(A     ,				[{'''translation_text''': ANY(A )}] )

								lowerCamelCase_   : List[str]       =						translator(['''Some string''', '''other string'''] )
								self.assertEqual(A     ,				[{'''translation_text''': ANY(A )}, {'''translation_text''': ANY(A )}] )

			@require_torch
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						pipeline('''translation_en_to_ro'''     ,				model='''patrickvonplaten/t5-tiny-random'''     ,				framework='''pt''' )
								lowerCamelCase_   : List[str]       =						translator('''This is a test string'''     ,				max_length=2_0 )
								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 ):
								lowerCamelCase_   : Any       =						pipeline('''translation_en_to_ro'''     ,				model='''patrickvonplaten/t5-tiny-random'''     ,				framework='''tf''' )
								lowerCamelCase_   : Dict       =						translator('''This is a test string'''     ,				max_length=2_0 )
								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 ):
								lowerCamelCase_   : str       =						pipeline('''translation_en_to_de'''     ,				model='''patrickvonplaten/t5-tiny-random'''     ,				framework='''pt''' )
								lowerCamelCase_   : Dict       =						translator('''This is a test string'''     ,				max_length=2_0 )
								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 ):
								lowerCamelCase_   : Optional[Any]       =						pipeline('''translation_en_to_de'''     ,				model='''patrickvonplaten/t5-tiny-random'''     ,				framework='''tf''' )
								lowerCamelCase_   : List[Any]       =						translator('''This is a test string'''     ,				max_length=2_0 )
								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 		__lowercase			(					unittest.TestCase      ):

			@require_torch
			@slow
			def     UpperCAmelCase__	(self ):

								with self.assertRaises(A ):
													pipeline(task='''translation_cn_to_ar''' )

								# but we do for this one
								lowerCamelCase_   : 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     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[Any]       =						MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt''' )
								lowerCamelCase_   : List[str]       =						MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt''' )

								lowerCamelCase_   : Optional[int]       =						pipeline(task='''translation'''     ,				model=A     ,				tokenizer=A )
								# Missing src_lang, tgt_lang
								with self.assertRaises(A ):
													translator('''This is a test''' )

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

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

			@require_torch
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						"patrickvonplaten/t5-tiny-random"
								lowerCamelCase_   : List[str]       =						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 ):
								lowerCamelCase_   : Tuple       =						"patrickvonplaten/t5-tiny-random"
								with pytest.warns(A     ,				match=R'''.*translation_en_to_de.*''' ):
													lowerCamelCase_   : str       =						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 ):

								with self.assertRaises(A ):
													pipeline(task='''translation''' )




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


