


"""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 _lowerCAmelCase				(      unittest.TestCase	):
   __UpperCAmelCase :       int							  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __UpperCAmelCase :       List[str]							  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    TextaTextGenerationPipeline(model=UpperCamelCase__						,	tokenizer=UpperCamelCase__						)
       return generator, ["Something to write", "Something else"]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




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

       snake_case    :      Any      					=    generator(["This is great !", "Something else"]						,	num_return_sequences=2						,	do_sample=UpperCamelCase__						)
       self.assertEqual(
           UpperCamelCase__						,	[
               [{"generated_text": ANY(UpperCamelCase__						)}, {"generated_text": ANY(UpperCamelCase__						)}],
               [{"generated_text": ANY(UpperCamelCase__						)}, {"generated_text": ANY(UpperCamelCase__						)}],
           ]						,	)

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

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







   @require_torch
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




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

       snake_case    :      int      					=    3
       snake_case    :      Optional[int]      					=    generator(
           "Something there"						,	num_return_sequences=UpperCamelCase__						,	num_beams=UpperCamelCase__						,	)
       snake_case    :      Union[str, Any]      					=    [
           {"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(UpperCamelCase__						,	UpperCamelCase__						)

       snake_case    :      str      					=    generator("This is a test"						,	do_sample=UpperCamelCase__						,	num_return_sequences=2						,	return_tensors=UpperCamelCase__						)
       self.assertEqual(
           UpperCamelCase__						,	[
               {"generated_token_ids": ANY(torch.Tensor						)},
               {"generated_token_ids": ANY(torch.Tensor						)},
           ]						,	)
       snake_case    :      List[Any]      					=    generator.model.config.eos_token_id
       snake_case    :      Dict      					=    "<pad>"
       snake_case    :      Optional[int]      					=    generator(
           ["This is a test", "This is a second test"]						,	do_sample=UpperCamelCase__						,	num_return_sequences=2						,	batch_size=2						,	return_tensors=UpperCamelCase__						,	)
       self.assertEqual(
           UpperCamelCase__						,	[
               [
                   {"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'''




       snake_case    :      Tuple      					=    pipeline("text2text-generation"						,	model="patrickvonplaten/t5-tiny-random"						,	framework="tf"						)
       # do_sample=False necessary for reproducibility
       snake_case    :      str      					=    generator("Something there"						,	do_sample=UpperCamelCase__						)
       self.assertEqual(UpperCamelCase__						,	[{"generated_text": ""}]						)









"""simple docstring"""



def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      Dict      					=    []
    snake_case    :      List[Any]      					=    1

    while len(lowercase ) < 1e6:
        constant.append(str(lowercase ) )
        i += 1

    snake_case    :      Tuple      					=    "".join(lowercase )

    return (
        int(constant[0] )
        * int(constant[9] )
        * int(constant[99] )
        * int(constant[999] )
        * int(constant[9999] )
        * int(constant[9_9999] )
        * int(constant[99_9999] )
    )


if __name__ == "__main__":
  print(solution())





