





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"""



   __A       :    List[Any]	  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __A       :    Any	  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

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

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







          '''simple docstring'''

          a__						:     Optional[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'))

          a__						:     List[Any]			=    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__)}],
              ] ,  )

          a__						:     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 							__lowercase		( self)      ->						str:







          '''simple docstring'''

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

          a__						:     Optional[int]			=    3
          a__						:     Union[str, Any]			=    generator(
              'Something there' ,  num_return_sequences=a__ ,  num_beams=a__ ,  )
          a__						:     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(a__ ,  a__)

          a__						:     Dict			=    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)},
              ] ,  )
          a__						:     Any			=    generator.model.config.eos_token_id
          a__						:     Tuple			=    '<pad>'
          a__						:     str			=    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 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

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









import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def A_     (		A__		,				A__		,				A__    )  ->		Any:
       # Construct model
       if gpta_config_file == "":
              a__						:     Optional[int]			=    GPTaConfig()
       else:
              a__						:     List[str]			=    GPTaConfig.from_json_file(A__    )
       a__						:     List[str]			=    GPTaModel(A__    )

       # Load weights from numpy
       load_tf_weights_in_gpta(A__		,				A__		,				A__    )

       # Save pytorch-model
       a__						:     Optional[Any]			=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       a__						:     Any			=    pytorch_dump_folder_path + '/' + CONFIG_NAME
       print(F'Save PyTorch model to {pytorch_weights_dump_path}'    )
       torch.save(model.state_dict()		,				A__    )
       print(F'Save configuration file to {pytorch_config_dump_path}'    )
       with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
              f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	Dict      						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    lowercase				:	int      						=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


