code: 
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import OpenAIGPTConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers.models.openai.modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTModel,
      )
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						parent
          UpperCAmelCase__ :       Dict						=						13
          UpperCAmelCase__ :       Dict						=						7
          UpperCAmelCase__ :       Dict						=						True
          UpperCAmelCase__ :       List[Any]						=						True
          UpperCAmelCase__ :       str						=						True
          UpperCAmelCase__ :       Union[str, Any]						=						True
          UpperCAmelCase__ :       Optional[Any]						=						True
          UpperCAmelCase__ :       Union[str, Any]						=						99
          UpperCAmelCase__ :       Any						=						32
          UpperCAmelCase__ :       Union[str, Any]						=						2
          UpperCAmelCase__ :       Union[str, Any]						=						4
          UpperCAmelCase__ :       str						=						37
          UpperCAmelCase__ :       int						=						"""gelu"""
          UpperCAmelCase__ :       Optional[Any]						=						0.1
          UpperCAmelCase__ :       List[Any]						=						0.1
          UpperCAmelCase__ :       List[str]						=						512
          UpperCAmelCase__ :       Optional[Any]						=						16
          UpperCAmelCase__ :       str						=						2
          UpperCAmelCase__ :       List[str]						=						0.02
          UpperCAmelCase__ :       Any						=						3
          UpperCAmelCase__ :       str						=						4
          UpperCAmelCase__ :       Any						=						None
          UpperCAmelCase__ :       Any						=						self.vocab_size - 1
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size  )
  
          UpperCAmelCase__ :       Tuple						=						None
          if self.use_input_mask:
                UpperCAmelCase__ :       List[str]						=						random_attention_mask([self.batch_size, self.seq_length]  )
  
          UpperCAmelCase__ :       Optional[int]						=						None
          if self.use_token_type_ids:
                UpperCAmelCase__ :       int						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size  )
  
          UpperCAmelCase__ :       int						=						None
          if self.use_mc_token_ids:
                UpperCAmelCase__ :       int						=						ids_tensor([self.batch_size, self.num_choices]       ,					self.seq_length  )
  
          UpperCAmelCase__ :       str						=						None
          UpperCAmelCase__ :       Dict						=						None
          UpperCAmelCase__ :       Optional[Any]						=						None
          if self.use_labels:
                UpperCAmelCase__ :       Union[str, Any]						=						ids_tensor([self.batch_size]       ,					self.type_sequence_label_size  )
                UpperCAmelCase__ :       Dict						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels  )
                UpperCAmelCase__ :       str						=						ids_tensor([self.batch_size]       ,					self.num_choices  )
  
          UpperCAmelCase__ :       Any						=						OpenAIGPTConfig(
              vocab_size=self.vocab_size       ,					n_embd=self.hidden_size       ,					n_layer=self.num_hidden_layers       ,					n_head=self.num_attention_heads       ,					n_positions=self.max_position_embeddings       ,					pad_token_id=self.pad_token_id       ,					)
  
          UpperCAmelCase__ :       Tuple						=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,					2  )
  
          return (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						TFOpenAIGPTModel(config=_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						[input_ids, input_mask]
          UpperCAmelCase__ :       Dict						=						model(_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						model(_lowerCamelCase  )
  
          self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						TFOpenAIGPTLMHeadModel(config=_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
          UpperCAmelCase__ :       str						=						model(_lowerCamelCase  )
          self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						TFOpenAIGPTDoubleHeadsModel(config=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						tf.tile(tf.expand_dims(_lowerCamelCase       ,					1  )       ,					(1, self.num_choices, 1)  )
          UpperCAmelCase__ :       Any						=						tf.tile(tf.expand_dims(_lowerCamelCase       ,					1  )       ,					(1, self.num_choices, 1)  )
          UpperCAmelCase__ :       Any						=						tf.tile(tf.expand_dims(_lowerCamelCase       ,					1  )       ,					(1, self.num_choices, 1)  )
  
          UpperCAmelCase__ :       List[Any]						=						{
              """input_ids""": multiple_choice_inputs_ids,
              """mc_token_ids""": mc_token_ids,
              """attention_mask""": multiple_choice_input_mask,
              """token_type_ids""": multiple_choice_token_type_ids,
          }
          UpperCAmelCase__ :       int						=						model(_lowerCamelCase  )
          self.parent.assertEqual(
              result.logits.shape       ,					(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)  )
          self.parent.assertEqual(result.mc_logits.shape       ,					(self.batch_size, self.num_choices)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.num_labels
          UpperCAmelCase__ :       int						=						ids_tensor([self.batch_size]       ,					self.type_sequence_label_size  )
          UpperCAmelCase__ :       Any						=						{
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
              """labels""": sequence_labels,
          }
          UpperCAmelCase__ :       Tuple						=						TFOpenAIGPTForSequenceClassification(_lowerCamelCase  )
          UpperCAmelCase__ :       Union[str, Any]						=						model(_lowerCamelCase  )
          self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.prepare_config_and_inputs()
  
          (
              UpperCAmelCase__		
          ) :       Optional[Any]						=						config_and_inputs
  
          UpperCAmelCase__ :       Tuple						=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    SCREAMING_SNAKE_CASE							=     (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    SCREAMING_SNAKE_CASE							=     (
        {
            'feature-extraction': TFOpenAIGPTModel,
            'text-classification': TFOpenAIGPTForSequenceClassification,
            'text-generation': TFOpenAIGPTLMHeadModel,
            'zero-shot': TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                # tiny config could not be created.
                return True
  
          return False
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						TFOpenAIGPTModelTester(self  )
          UpperCAmelCase__ :       Optional[int]						=						ConfigTester(self       ,					config_class=_lowerCamelCase       ,					n_embd=37  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          self.config_tester.run_common_tests()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_model(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_lm_head(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_double_head(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowerCamelCase  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__ :       int						=						TFOpenAIGPTModel.from_pretrained(_lowerCamelCase  )
                self.assertIsNotNone(_lowerCamelCase  )
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						TFOpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""  )
          UpperCAmelCase__ :       Optional[int]						=						tf.convert_to_tensor([[481, 4735, 544]]       ,					dtype=tf.intaa  )  # the president is
          UpperCAmelCase__ :       Union[str, Any]						=						[
              481,
              4735,
              544,
              246,
              963,
              870,
              762,
              239,
              244,
              40477,
              244,
              249,
              719,
              881,
              487,
              544,
              240,
              244,
              603,
              481,
          ]  # the president is a very good man. " \n " i\'m sure he is, " said the
  
          UpperCAmelCase__ :       Any						=						model.generate(_lowerCamelCase       ,					do_sample=_lowerCamelCase  )
          self.assertListEqual(output_ids[0].numpy().tolist()       ,					_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  _A						=     {
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  _A						=     [
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
        parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
        parser.add_argument(
            """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
        parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
        parser.add_argument(
            """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
        parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
        parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()
  
        # Sanitize args
        if args.model_name in DEFAULT_MODELS:
              UpperCAmelCase__ :       Any						=						DEFAULT_MODELS[args.model_name]
              UpperCAmelCase__ :       Tuple						=						defaults["""model"""]
              if args.tokenizer_name is None:
                    UpperCAmelCase__ :       Optional[Any]						=						defaults.get("""tokenizer""" ,  args.model_name						)
              if args.is_causal is None:
                    UpperCAmelCase__ :       List[Any]						=						defaults["""is_causal"""]
              if args.model_revision is None:
                    UpperCAmelCase__ :       Optional[int]						=						defaults.get("""model_revision""" ,  """main"""						)
  
        if args.is_causal is None:
              raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""						)
        if args.tokenizer_name is None:
              UpperCAmelCase__ :       str						=						args.model_name
        if args.model_revision is None:
              UpperCAmelCase__ :       List[str]						=						"""main"""
  
        return args
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        transformers.utils.logging.set_verbosity_error()
        UpperCAmelCase__ :       Optional[int]						=						parse_args()
  
        if args.torch_dtype is None:
              UpperCAmelCase__ :       List[str]						=						AutoConfig.from_pretrained(args.model_name						)
              UpperCAmelCase__ :       Optional[Any]						=						getattr(lowerCAmelCase ,  """torch_dtype""" ,  torch.floataa						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						getattr(lowerCAmelCase ,  args.torch_dtype						)
        UpperCAmelCase__ :       List[Any]						=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
        UpperCAmelCase__ :       List[str]						=						{
            """torch_dtype""": torch_dtype,
            """revision""": args.model_revision,
        }
        if args.disk_offload:
              UpperCAmelCase__ :       List[str]						=						"""tmp_offload"""
              UpperCAmelCase__ :       int						=						True
  
        UpperCAmelCase__ :       Any						=						start_measure()
        UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model loading"""						)
  
        UpperCAmelCase__ :       List[str]						=						compute_module_sizes(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						{v: 0 for v in model.hf_device_map.values()}
        for module, device in model.hf_device_map.items():
              device_size[device] += module_sizes[module]
        UpperCAmelCase__ :       Optional[Any]						=						"""\n""".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
        print(F"""\nTheoretical use:\n{message}"""						)
  
        UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)
  
        UpperCAmelCase__ :       List[str]						=						start_measure()
        UpperCAmelCase__ :       List[Any]						=						[]
        UpperCAmelCase__ :       Tuple						=						[]
        UpperCAmelCase__ :       Optional[Any]						=						[]
        for prompt in PROMPTS:
              UpperCAmelCase__ :       Any						=						tokenizer(lowerCAmelCase ,  return_tensors="""pt"""						).to(0						)
              UpperCAmelCase__ :       List[str]						=						inputs["""input_ids"""][0].tolist()
              UpperCAmelCase__ :       Dict						=						time.time()
              UpperCAmelCase__ :       List[str]						=						model.generate(inputs["""input_ids"""]						)
              UpperCAmelCase__ :       str						=						time.time()
              UpperCAmelCase__ :       str						=						outputs[0].tolist()
              UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) if outputs[: len(lowerCAmelCase						)] != tokens else len(lowerCAmelCase						) - len(lowerCAmelCase						)
              UpperCAmelCase__ :       Tuple						=						after_generate - before_generate
  
              UpperCAmelCase__ :       Optional[Any]						=						tokenizer.decode(lowerCAmelCase ,  skip_special_tokens=lowerCAmelCase						)
              texts_outs.append(lowerCAmelCase						)
              generation_times.append(lowerCAmelCase						)
              gen_tokens.append(lowerCAmelCase						)
              print(F"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""						)
  
        UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model generation"""						)
  
        UpperCAmelCase__ :       Any						=						[gen / tok for gen, tok in zip(lowerCAmelCase ,  lowerCAmelCase						)]
        UpperCAmelCase__ :       List[Any]						=						sum(lowerCAmelCase						) / len(lowerCAmelCase						)
        print(F"""Average time of generation per token: {avg_gen:.2f}s"""						)
        print(F"""First generation (avg time per token): {generation_times_per_token[0]:.2f}s"""						)
        UpperCAmelCase__ :       Any						=						sum(generation_times_per_token[1:]						) / (len(lowerCAmelCase						) - 1)
        print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""						)
  
  
  if __name__ == "__main__":
      main()
  
  