


"""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 	lowercase__				(					unittest.TestCase				):
   __UpperCAmelCase							       =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __UpperCAmelCase							       =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      _lowerCamelCase	:					Tuple   =		TextaTextGenerationPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)
      return generator, ["Something to write", "Something else"]





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

      _lowerCamelCase	:					Dict   =		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)}],
          ]      ,   )

      _lowerCamelCase	:					int   =		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):
         generator(4)





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

      _lowerCamelCase	:					Dict   =		3
      _lowerCamelCase	:					List[str]   =		generator(
          """Something there"""      ,   num_return_sequences=SCREAMING_SNAKE_CASE      ,   num_beams=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Tuple   =		[
          {"""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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		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(torch.Tensor)},
              {"""generated_token_ids""": ANY(torch.Tensor)},
          ]      ,   )
      _lowerCamelCase	:					Union[str, Any]   =		generator.model.config.eos_token_id
      _lowerCamelCase	:					List[Any]   =		"""<pad>"""
      _lowerCamelCase	:					str   =		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(torch.Tensor)},
                  {"""generated_token_ids""": ANY(torch.Tensor)},
              ],
              [
                  {"""generated_token_ids""": ANY(torch.Tensor)},
                  {"""generated_token_ids""": ANY(torch.Tensor)},
              ],
          ]      ,   )





   @require_tf
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					str   =		pipeline("""text2text-generation"""      ,   model="""patrickvonplaten/t5-tiny-random"""      ,   framework="""tf""")
      # do_sample=False necessary for reproducibility
      _lowerCamelCase	:					List[Any]   =		generator("""Something there"""      ,   do_sample=SCREAMING_SNAKE_CASE)
      self.assertEqual(SCREAMING_SNAKE_CASE      ,   [{"""generated_text""": """"""}])





"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		HfArgumentParser(__snake_case   )
   _lowerCamelCase	:					int   =		parser.parse_args_into_dataclasses()[0]
   _lowerCamelCase	:					Dict   =		TensorFlowBenchmark(args=__snake_case   )
   try:
      _lowerCamelCase	:					Optional[int]   =		parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      _lowerCamelCase	:					Union[str, Any]   =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
      _lowerCamelCase	:					List[str]   =		""" """.join(str(__snake_case   ).split(""" """   )[:-1]   )
      _lowerCamelCase	:					Dict   =		""""""
      _lowerCamelCase	:					List[Any]   =		eval(str(__snake_case   ).split(""" """   )[-1]   )
      _lowerCamelCase	:					Tuple   =		[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]   )
         else:
            wrong_args.append(__snake_case   )
      if len(__snake_case   ) > 0:
         _lowerCamelCase	:					Tuple   =		full_error_msg + begin_error_msg + str(__snake_case   )
      raise ValueError(__snake_case   )
   benchmark.run()


if __name__ == "__main__":
 main()

