






"""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 						snake_case  (  unittest.TestCase       ):
    __magic_name__         =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __magic_name__         =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					Union[str, Any]				,		A	:					Tuple				,		A	:					Any			):


      '''simple docstring'''




      if isinstance(model.config				,		A			):
        a,     a     :			int				=   list(tokenizer.lang_code_to_id.keys()			)[:2]
        a     :			Dict				=   TranslationPipeline(model=A				,		tokenizer=A				,		src_lang=A				,		tgt_lang=A			)
      else:
        a     :			List[str]				=   TranslationPipeline(model=A				,		tokenizer=A			)
      return translator, ["Some string", "Some other text"]





    def 				lowerCamelCase__      (							self	:					int				,		A	:					int				,		A	:					Tuple			):


      '''simple docstring'''




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

      a     :			Optional[int]				=   translator(['Some string']			)
      self.assertEqual(A				,		[{'translation_text': ANY(A			)}]			)

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





    @require_torch
    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			List[Any]				=   pipeline('translation_en_to_ro'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='pt'			)
      a     :			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 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   pipeline('translation_en_to_ro'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='tf'			)
      a     :			Tuple				=   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 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      a     :			str				=   pipeline('translation_en_to_de'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='pt'			)
      a     :			List[str]				=   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 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      a     :			Dict				=   pipeline('translation_en_to_de'				,		model='patrickvonplaten/t5-tiny-random'				,		framework='tf'			)
      a     :			Union[str, 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 						snake_case  (  unittest.TestCase       ):





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


      '''simple docstring'''




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

      # but we do for this one
      a     :			List[Any]				=   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	:					Optional[Any]			):


      '''simple docstring'''




      a     :			Optional[int]				=   MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'			)
      a     :			int				=   MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'			)

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

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

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





    @require_torch
    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			int				=   'patrickvonplaten/t5-tiny-random'
      a     :			Optional[int]				=   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 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			str				=   'patrickvonplaten/t5-tiny-random'
      with pytest.warns(A				,		match=R'.*translation_en_to_de.*'			):
        a     :			Optional[Any]				=   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 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




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












"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

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




