"""simple docstring"""
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        __snake_case	(					unittest.TestCase    ):
      a__				=		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      a__				=		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Optional[int]      							=     TextaTextGenerationPipeline(model=lowercase   ,      tokenizer=lowercase)
        return generator, ["Something to write", "Something else"]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     generator('Something there')
        self.assertEqual(lowercase   ,      [{'generated_text': ANY(lowercase)}])
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'))

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

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

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



      @require_torch
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Any      							=     pipeline('text2text-generation'   ,      model='patrickvonplaten/t5-tiny-random'   ,      framework='pt')
        # do_sample=False necessary for reproducibility
        a__:					int      							=     generator('Something there'   ,      do_sample=lowercase)
        self.assertEqual(lowercase   ,      [{'generated_text': ''}])

        a__:					int      							=     3
        a__:					str      							=     generator(
            'Something there'   ,      num_return_sequences=lowercase   ,      num_beams=lowercase   ,      )
        a__:					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(lowercase   ,      lowercase)

        a__:					Optional[int]      							=     generator('This is a test'   ,      do_sample=lowercase   ,      num_return_sequences=2   ,      return_tensors=lowercase)
        self.assertEqual(
            lowercase   ,      [
                {'generated_token_ids': ANY(torch.Tensor)},
                {'generated_token_ids': ANY(torch.Tensor)},
            ]   ,      )
        a__:					int      							=     generator.model.config.eos_token_id
        a__:					Optional[Any]      							=     '<pad>'
        a__:					Optional[int]      							=     generator(
            ['This is a test', 'This is a second test']   ,      do_sample=lowercase   ,      num_return_sequences=2   ,      batch_size=2   ,      return_tensors=lowercase   ,      )
        self.assertEqual(
            lowercase   ,      [
                [
                    {'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   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     pipeline('text2text-generation'   ,      model='patrickvonplaten/t5-tiny-random'   ,      framework='tf')
        # do_sample=False necessary for reproducibility
        a__:					str      							=     generator('Something there'   ,      do_sample=lowercase)
        self.assertEqual(lowercase   ,      [{'generated_text': ''}])



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


