


'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :List[Any]												=MODEL_FOR_CAUSAL_LM_MAPPING
  a_       :Union[str, Any]												=TF_MODEL_FOR_CAUSAL_LM_MAPPING

  @require_torch
  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   pipeline(task="""text-generation"""				,							model="""sshleifer/tiny-ctrl"""				,							framework="""pt"""						)
       # Using `do_sample=False` to force deterministic output
       __a       =   text_generator("""This is a test"""				,							do_sample=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               {
                   """generated_text""": (
                       """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                       """ oscope. FiliFili@@"""
                   )
               }
           ]				,							)

       __a       =   text_generator(["""This is a test""", """This is a second test"""]						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               [
                   {
                       """generated_text""": (
                           """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                           """ oscope. FiliFili@@"""
                       )
                   }
               ],
               [
                   {
                       """generated_text""": (
                           """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
                           """ oscope. oscope. FiliFili@@"""
                       )
                   }
               ],
           ]				,							)

       __a       =   text_generator("""This is a test"""				,							do_sample=SCREAMING_SNAKE_CASE__				,							num_return_sequences=2				,							return_tensors=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               {"""generated_token_ids""": ANY(SCREAMING_SNAKE_CASE__						)},
               {"""generated_token_ids""": ANY(SCREAMING_SNAKE_CASE__						)},
           ]				,							)
       __a       =   text_generator.model.config.eos_token_id
       __a       =   """<pad>"""
       __a       =   text_generator(
           ["""This is a test""", """This is a second test"""]				,							do_sample=SCREAMING_SNAKE_CASE__				,							num_return_sequences=2				,							batch_size=2				,							return_tensors=SCREAMING_SNAKE_CASE__				,							)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               [
                   {"""generated_token_ids""": ANY(SCREAMING_SNAKE_CASE__						)},
                   {"""generated_token_ids""": ANY(SCREAMING_SNAKE_CASE__						)},
               ],
               [
                   {"""generated_token_ids""": ANY(SCREAMING_SNAKE_CASE__						)},
                   {"""generated_token_ids""": ANY(SCREAMING_SNAKE_CASE__						)},
               ],
           ]				,							)

  @require_tf
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   pipeline(task="""text-generation"""				,							model="""sshleifer/tiny-ctrl"""				,							framework="""tf"""						)

       # Using `do_sample=False` to force deterministic output
       __a       =   text_generator("""This is a test"""				,							do_sample=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               {
                   """generated_text""": (
                       """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                       """ please,"""
                   )
               }
           ]				,							)

       __a       =   text_generator(["""This is a test""", """This is a second test"""]				,							do_sample=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               [
                   {
                       """generated_text""": (
                           """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                           """ please,"""
                       )
                   }
               ],
               [
                   {
                       """generated_text""": (
                           """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
                           """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
                       )
                   }
               ],
           ]				,							)

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       __a       =   TextGenerationPipeline(model=SCREAMING_SNAKE_CASE__				,							tokenizer=SCREAMING_SNAKE_CASE__						)
       return text_generator, ["This is a test", "Another test"]

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   """Hello I believe in"""
       __a       =   pipeline("""text-generation"""				,							model="""hf-internal-testing/tiny-random-gpt2"""						)
       __a       =   text_generator(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]				,							)

       __a       =   text_generator(SCREAMING_SNAKE_CASE__				,							stop_sequence=""" fe"""						)
       self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": """Hello I believe in fe"""}]						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   text_generator.model
       __a       =   text_generator.tokenizer

       __a       =   text_generator("""This is a test"""						)
       self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""						)						)

       __a       =   text_generator("""This is a test"""				,							return_full_text=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       self.assertNotIn("""This is a test"""				,							outputs[0]["""generated_text"""]						)

       __a       =   pipeline(task="""text-generation"""				,							model=SCREAMING_SNAKE_CASE__				,							tokenizer=SCREAMING_SNAKE_CASE__				,							return_full_text=SCREAMING_SNAKE_CASE__						)
       __a       =   text_generator("""This is a test"""						)
       self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       self.assertNotIn("""This is a test"""				,							outputs[0]["""generated_text"""]						)

       __a       =   text_generator("""This is a test"""				,							return_full_text=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""						)						)

       __a       =   text_generator(["""This is great !""", """Something else"""]				,							num_return_sequences=2				,							do_sample=SCREAMING_SNAKE_CASE__						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               [{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}, {"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}],
               [{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}, {"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}],
           ]				,							)

       if text_generator.tokenizer.pad_token is not None:
            __a       =   text_generator(
                ["""This is great !""", """Something else"""]				,							num_return_sequences=2				,							batch_size=2				,							do_sample=SCREAMING_SNAKE_CASE__						)
            self.assertEqual(
                SCREAMING_SNAKE_CASE__				,							[
                    [{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}, {"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}],
                    [{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}, {"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}],
                ]				,							)

       with self.assertRaises(SCREAMING_SNAKE_CASE__						):
            __a       =   text_generator("""test"""				,							return_full_text=SCREAMING_SNAKE_CASE__				,							return_text=SCREAMING_SNAKE_CASE__						)
       with self.assertRaises(SCREAMING_SNAKE_CASE__						):
            __a       =   text_generator("""test"""				,							return_full_text=SCREAMING_SNAKE_CASE__				,							return_tensors=SCREAMING_SNAKE_CASE__						)
       with self.assertRaises(SCREAMING_SNAKE_CASE__						):
            __a       =   text_generator("""test"""				,							return_text=SCREAMING_SNAKE_CASE__				,							return_tensors=SCREAMING_SNAKE_CASE__						)

       # Empty prompt is slighly special
       # it requires BOS token to exist.
       # Special case for Pegasus which will always append EOS so will
       # work even without BOS.
       if (
           text_generator.tokenizer.bos_token_id is not None
           or "Pegasus" in tokenizer.__class__.__name__
           or "Git" in model.__class__.__name__
       ):
            __a       =   text_generator(""""""						)
            self.assertEqual(SCREAMING_SNAKE_CASE__				,							[{"""generated_text""": ANY(SCREAMING_SNAKE_CASE__						)}]						)
       else:
            with self.assertRaises((ValueError, AssertionError)						):
                 __a       =   text_generator(""""""						)

       if text_generator.framework == "tf":
            # TF generation does not support max_new_tokens, and it's impossible
            # to control long generation with only max_length without
            # fancy calculation, dismissing tests for now.
            return
       # We don't care about infinite range models.
       # They already work.
       # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
       __a       =   ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
       if (
           tokenizer.model_max_length < 1_0_0_0_0
           and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
       ):
            # Handling of large generations
            with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)						):
                 text_generator("""This is a test""" * 5_0_0				,							max_new_tokens=2_0						)

            __a       =   text_generator("""This is a test""" * 5_0_0				,							handle_long_generation="""hole"""				,							max_new_tokens=2_0						)
            # Hole strategy cannot work
            with self.assertRaises(SCREAMING_SNAKE_CASE__						):
                 text_generator(
                     """This is a test""" * 5_0_0				,							handle_long_generation="""hole"""				,							max_new_tokens=tokenizer.model_max_length + 1_0				,							)

  @require_torch
  @require_accelerate
  @require_torch_gpu
  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       import torch

       # Classic `model_kwargs`
       __a       =   pipeline(
           model="""hf-internal-testing/tiny-random-bloom"""				,							model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa}				,							)
       self.assertEqual(pipe.model.device				,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype				,							torch.bfloataa						)
       __a       =   pipe("""This is a test"""						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               {
                   """generated_text""": (
                       """This is a test test test test test test test test test test test test test test test test"""
                       """ test"""
                   )
               }
           ]				,							)

       # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
       __a       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""				,							device_map="""auto"""				,							torch_dtype=torch.bfloataa						)
       self.assertEqual(pipe.model.device				,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype				,							torch.bfloataa						)
       __a       =   pipe("""This is a test"""						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               {
                   """generated_text""": (
                       """This is a test test test test test test test test test test test test test test test test"""
                       """ test"""
                   )
               }
           ]				,							)

       # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
       __a       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""				,							device_map="""auto"""						)
       self.assertEqual(pipe.model.device				,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype				,							torch.floataa						)
       __a       =   pipe("""This is a test"""						)
       self.assertEqual(
           SCREAMING_SNAKE_CASE__				,							[
               {
                   """generated_text""": (
                       """This is a test test test test test test test test test test test test test test test test"""
                       """ test"""
                   )
               }
           ]				,							)

  @require_torch
  @require_torch_gpu
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       import torch

       __a       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""				,							device=0				,							torch_dtype=torch.floataa						)
       pipe("""This is a test"""						)

  @require_torch
  @require_accelerate
  @require_torch_gpu
  def __a						(					self     :							int						):





       '''simple docstring'''

       import torch

       __a       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""				,							device_map="""auto"""				,							torch_dtype=torch.floataa						)
       pipe("""This is a test"""				,							do_sample=SCREAMING_SNAKE_CASE__				,							top_p=0.5						)


  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """Hello world"""
       __a       =   pipeline("""text-generation"""				,							model="""hf-internal-testing/tiny-random-gpt2"""						)
       if text_generator.model.framework == "tf":
            __a       =   logging.get_logger("""transformers.generation.tf_utils"""						)
       else:
            __a       =   logging.get_logger("""transformers.generation.utils"""						)
       __a       =   """Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

       # Both are set by the user -> log warning
       with CaptureLogger(SCREAMING_SNAKE_CASE__						) as cl:
            __a       =   text_generator(SCREAMING_SNAKE_CASE__				,							max_length=1_0				,							max_new_tokens=1						)
       self.assertIn(SCREAMING_SNAKE_CASE__				,							cl.out						)

       # The user only sets one -> no warning
       with CaptureLogger(SCREAMING_SNAKE_CASE__						) as cl:
            __a       =   text_generator(SCREAMING_SNAKE_CASE__				,							max_new_tokens=1						)
       self.assertNotIn(SCREAMING_SNAKE_CASE__				,							cl.out						)

       with CaptureLogger(SCREAMING_SNAKE_CASE__						) as cl:
            __a       =   text_generator(SCREAMING_SNAKE_CASE__				,							max_length=1_0						)
       self.assertNotIn(SCREAMING_SNAKE_CASE__				,							cl.out						)










'''simple docstring'''







from math import pi, sqrt




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




     if num <= 0:
          raise ValueError("""math domain error"""		)
     if num > 171.5:
          raise OverflowError("""math range error"""		)
     elif num - int(__SCREAMING_SNAKE_CASE		) not in (0, 0.5):
          raise NotImplementedError("""num must be an integer or a half-integer"""		)
     elif num == 0.5:
          return sqrt(__SCREAMING_SNAKE_CASE		)
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1		)




def        __lowercase						(      )				->					None:
     """simple docstring"""




     assert gamma(0.5		) == sqrt(__SCREAMING_SNAKE_CASE		)
     assert gamma(1		) == 1.0
     assert gamma(2		) == 1.0


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 SCREAMING_SNAKE_CASE_      =			1.0
 while num:
  SCREAMING_SNAKE_CASE_      =			float(input('Gamma of: '))
  print(f"""gamma({num}) = {gamma(num)}""")
  print('\nEnter 0 to exit...')






