


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
       import torch


@is_pipeline_test
class 	A_ (	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 					_snake_case     (    self:	Tuple						,				a:	Tuple						,				a:	int						,				a:	int    ) ->    List[Any]:
       __lowerCamelCase		:							str		 =						TextaTextGenerationPipeline(model=a__						,				tokenizer=a__    )
       return generator, ["Something to write", "Something else"]




      def 					_snake_case     (    self:	List[str]						,				a:	List[Any]						,				a:	List[str]    ) ->    Tuple:
       __lowerCamelCase		:							List[Any]		 =						generator('Something there'    )
       self.assertEqual(a__						,				[{'generated_text': ANY(a__    )}]    )
       # These are encoder decoder, they don't just append to incoming string
       self.assertFalse(outputs[0]['generated_text'].startswith('Something there'    )    )

       __lowerCamelCase		:							int		 =						generator(['This is great !', 'Something else']						,				num_return_sequences=2						,				do_sample=a__    )
       self.assertEqual(
           a__						,				[
               [{'generated_text': ANY(a__    )}, {'generated_text': ANY(a__    )}],
               [{'generated_text': ANY(a__    )}, {'generated_text': ANY(a__    )}],
           ]						,				)

       __lowerCamelCase		:							List[Any]		 =						generator(
           ['This is great !', 'Something else']						,				num_return_sequences=2						,				batch_size=2						,				do_sample=a__    )
       self.assertEqual(
           a__						,				[
               [{'generated_text': ANY(a__    )}, {'generated_text': ANY(a__    )}],
               [{'generated_text': ANY(a__    )}, {'generated_text': ANY(a__    )}],
           ]						,				)

       with self.assertRaises(a__    ):
        generator(4    )




      @require_torch
      def 					_snake_case     (    self:	Any    ) ->    Dict:
       __lowerCamelCase		:							Union[str, Any]		 =						pipeline('text2text-generation'						,				model='patrickvonplaten/t5-tiny-random'						,				framework='pt'    )
       # do_sample=False necessary for reproducibility
       __lowerCamelCase		:							str		 =						generator('Something there'						,				do_sample=a__    )
       self.assertEqual(a__						,				[{'generated_text': ''}]    )

       __lowerCamelCase		:							Union[str, Any]		 =						3
       __lowerCamelCase		:							Tuple		 =						generator(
           'Something there'						,				num_return_sequences=a__						,				num_beams=a__						,				)
       __lowerCamelCase		:							int		 =						[
           {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
           {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
           {'generated_text': ''},
       ]
       self.assertEqual(a__						,				a__    )

       __lowerCamelCase		:							Union[str, Any]		 =						generator('This is a test'						,				do_sample=a__						,				num_return_sequences=2						,				return_tensors=a__    )
       self.assertEqual(
           a__						,				[
               {'generated_token_ids': ANY(torch.Tensor    )},
               {'generated_token_ids': ANY(torch.Tensor    )},
           ]						,				)
       __lowerCamelCase		:							List[Any]		 =						generator.model.config.eos_token_id
       __lowerCamelCase		:							Tuple		 =						'<pad>'
       __lowerCamelCase		:							Union[str, Any]		 =						generator(
           ['This is a test', 'This is a second test']						,				do_sample=a__						,				num_return_sequences=2						,				batch_size=2						,				return_tensors=a__						,				)
       self.assertEqual(
           a__						,				[
               [
                   {'generated_token_ids': ANY(torch.Tensor    )},
                   {'generated_token_ids': ANY(torch.Tensor    )},
               ],
               [
                   {'generated_token_ids': ANY(torch.Tensor    )},
                   {'generated_token_ids': ANY(torch.Tensor    )},
               ],
           ]						,				)




      @require_tf
      def 					_snake_case     (    self:	int    ) ->    Tuple:
       __lowerCamelCase		:							Optional[int]		 =						pipeline('text2text-generation'						,				model='patrickvonplaten/t5-tiny-random'						,				framework='tf'    )
       # do_sample=False necessary for reproducibility
       __lowerCamelCase		:							str		 =						generator('Something there'						,				do_sample=a__    )
       self.assertEqual(a__						,				[{'generated_text': ''}]    )





import warnings

from .generation import TFGenerationMixin


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,				__UpperCamelCase ,				)

