






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case			 =						logging.get_logger(__name__)

snake_case			 =						{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Optional[int]								=		'''gpt2'''
     UpperCamelCase_							:						int								=		['''past_key_values''']
     UpperCamelCase_							:						str								=		{
         '''hidden_size''': '''n_embd''',
         '''max_position_embeddings''': '''n_positions''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }


     def __init__( self				:		int    ,       UpperCAmelCase_				:		Optional[int]=5_0257    ,       UpperCAmelCase_				:		Any=1024    ,       UpperCAmelCase_				:		Dict=768    ,       UpperCAmelCase_				:		List[Any]=12    ,       UpperCAmelCase_				:		Union[str, Any]=12    ,       UpperCAmelCase_				:		Any=None    ,       UpperCAmelCase_				:		int="gelu_new"    ,       UpperCAmelCase_				:		Union[str, Any]=0.1    ,       UpperCAmelCase_				:		Optional[int]=0.1    ,       UpperCAmelCase_				:		Tuple=0.1    ,       UpperCAmelCase_				:		Union[str, Any]=1E-5    ,       UpperCAmelCase_				:		int=0.02    ,       UpperCAmelCase_				:		int="cls_index"    ,       UpperCAmelCase_				:		Optional[int]=True    ,       UpperCAmelCase_				:		Union[str, Any]=None    ,       UpperCAmelCase_				:		Optional[Any]=True    ,       UpperCAmelCase_				:		str=0.1    ,       UpperCAmelCase_				:		Optional[Any]=True    ,       UpperCAmelCase_				:		Union[str, Any]=True    ,       UpperCAmelCase_				:		List[str]=5_0256    ,       UpperCAmelCase_				:		int=5_0256    ,       UpperCAmelCase_				:		int=False    ,       UpperCAmelCase_				:		int=False    ,       **UpperCAmelCase_				:		str    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			vocab_size
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			n_positions
       SCREAMING_SNAKE_CASE				:					Dict		  =			n_embd
       SCREAMING_SNAKE_CASE				:					Tuple		  =			n_layer
       SCREAMING_SNAKE_CASE				:					int		  =			n_head
       SCREAMING_SNAKE_CASE				:					int		  =			n_inner
       SCREAMING_SNAKE_CASE				:					str		  =			activation_function
       SCREAMING_SNAKE_CASE				:					List[str]		  =			resid_pdrop
       SCREAMING_SNAKE_CASE				:					Any		  =			embd_pdrop
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			attn_pdrop
       SCREAMING_SNAKE_CASE				:					Tuple		  =			layer_norm_epsilon
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			initializer_range
       SCREAMING_SNAKE_CASE				:					int		  =			summary_type
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			summary_use_proj
       SCREAMING_SNAKE_CASE				:					List[str]		  =			summary_activation
       SCREAMING_SNAKE_CASE				:					List[str]		  =			summary_first_dropout
       SCREAMING_SNAKE_CASE				:					Tuple		  =			summary_proj_to_labels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			scale_attn_weights
       SCREAMING_SNAKE_CASE				:					Tuple		  =			use_cache
       SCREAMING_SNAKE_CASE				:					str		  =			scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE				:					Any		  =			reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE				:					str		  =			bos_token_id
       SCREAMING_SNAKE_CASE				:					Any		  =			eos_token_id

       super().__init__(bos_token_id=UpperCAmelCase_    ,       eos_token_id=UpperCAmelCase_    ,       **UpperCAmelCase_					)





class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     def __init__( self				:		List[str]    ,       UpperCAmelCase_				:		PretrainedConfig    ,       UpperCAmelCase_				:		str = "default"    ,       UpperCAmelCase_				:		List[PatchingSpec] = None    ,       UpperCAmelCase_				:		bool = False    ,       ):
       super().__init__(UpperCAmelCase_    ,       task=UpperCAmelCase_    ,       patching_specs=UpperCAmelCase_    ,       use_past=UpperCAmelCase_					)
       if not getattr(self._config    ,       "pad_token_id"    ,       UpperCAmelCase_					):
         # TODO: how to do that better?
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			0


     @property
     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
       if self.use_past:
         self.fill_with_past_key_values_(UpperCAmelCase_    ,       direction="inputs"					)
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			{0: "batch", 1: "past_sequence + sequence"}
       else:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{0: "batch", 1: "sequence"}

       return common_inputs


     @property
     def  _A				( self				:		Any					):
       return self._config.n_layer


     @property
     def  _A				( self				:		Any					):
       return self._config.n_head


     def  _A				( self				:		Any    ,       UpperCAmelCase_				:		PreTrainedTokenizer    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[TensorType] = None    ,       ):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			super(UpperCAmelCase_    ,       self					).generate_dummy_inputs(
           UpperCAmelCase_    ,       batch_size=UpperCAmelCase_    ,       seq_length=UpperCAmelCase_    ,       is_pair=UpperCAmelCase_    ,       framework=UpperCAmelCase_					)

       # We need to order the input in the way they appears in the forward()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			OrderedDict({"input_ids": common_inputs["input_ids"]}					)

       # Need to add the past_keys
       if self.use_past:
         if not is_torch_available():
           raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
         else:
           import torch

           SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			common_inputs["input_ids"].shape
           # Not using the same length for past_key_values
           SCREAMING_SNAKE_CASE				:					int		  =			seqlen + 2
           SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           SCREAMING_SNAKE_CASE				:					Any		  =			[
               (torch.zeros(UpperCAmelCase_					), torch.zeros(UpperCAmelCase_					)) for _ in range(self.num_layers					)
           ]

       SCREAMING_SNAKE_CASE				:					Any		  =			common_inputs["attention_mask"]
       if self.use_past:
         SCREAMING_SNAKE_CASE				:					int		  =			ordered_inputs["attention_mask"].dtype
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			torch.cat(
             [ordered_inputs["attention_mask"], torch.ones(UpperCAmelCase_    ,       UpperCAmelCase_    ,       dtype=UpperCAmelCase_					)]    ,       dim=1					)

       return ordered_inputs






     @property
     def  _A				( self				:		Optional[int]					):
       return 13














import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
snake_case			 =						logging.getLogger(__name__)


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  np.random.seed(args.seed )
  torch.manual_seed(args.seed )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed )


def      lowerCamelCase__	(   ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[str]		  =			argparse.ArgumentParser()
  parser.add_argument(
      "--model_name_or_path"  ,   default=lowercase  ,   type=lowercase  ,   required=lowercase  ,   )
  parser.add_argument("--prompt"  ,   type=lowercase  ,   default="" )
  parser.add_argument("--length"  ,   type=lowercase  ,   default=20 )
  parser.add_argument("--stop_token"  ,   type=lowercase  ,   default=lowercase  ,   help="Token at which text generation is stopped" )
  parser.add_argument(
      "--temperature"  ,   type=lowercase  ,   default=1.0  ,   help="temperature of 1.0 has no effect, lower tend toward greedy sampling"  ,   )
  parser.add_argument(
      "--repetition_penalty"  ,   type=lowercase  ,   default=1.0  ,   help="primarily useful for CTRL model; in that case, use 1.2" )
  parser.add_argument("--k"  ,   type=lowercase  ,   default=0 )
  parser.add_argument("--penalty_alpha"  ,   type=lowercase  ,   default=0.0 )
  parser.add_argument("--p"  ,   type=lowercase  ,   default=0.9 )

  parser.add_argument("--prefix"  ,   type=lowercase  ,   default=""  ,   help="Text added prior to input." )
  parser.add_argument("--padding_text"  ,   type=lowercase  ,   default=""  ,   help="Deprecated, the use of `--prefix` is preferred." )
  parser.add_argument("--xlm_language"  ,   type=lowercase  ,   default=""  ,   help="Optional language when used with the XLM model." )

  parser.add_argument("--seed"  ,   type=lowercase  ,   default=42  ,   help="random seed for initialization" )
  parser.add_argument("--no_cuda"  ,   action="store_true"  ,   help="Avoid using CUDA when available" )
  parser.add_argument(
      "--fp16"  ,   action="store_true"  ,   help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}''' )

  set_seed(lowercase )

  # Initialize the model and tokenizer
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device )

  if args.fpaa:
    model.half()

  logger.info(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model.generate(
      **lowercase  ,   max_length=args.length + len(inputs["input_ids"][0] )  ,   penalty_alpha=args.penalty_alpha  ,   top_k=args.k  ,   )

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

    # Decode text
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.decode(lowercase  ,   clean_up_tokenization_spaces=lowercase  ,   add_special_tokens=lowercase )

    # Remove all text after the stop token
    SCREAMING_SNAKE_CASE				:					List[str]		  =			text[: text.find(args.stop_token ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    SCREAMING_SNAKE_CASE				:					str		  =			(
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]  ,   clean_up_tokenization_spaces=lowercase ) ) :]
    )

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






