



"""simple docstring"""
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,
)
_lowerCAmelCase					:Optional[int]  =      logging.getLogger(__name__)





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





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

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

    parser.add_argument('''--seed'''  , type=UpperCamelCase__  , 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'''  , )
    _UpperCAmelCase							:						str       							=					parser.parse_args()

    _UpperCAmelCase							:						Optional[Any]       							=					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
    _UpperCAmelCase							:						Any       							=					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(UpperCamelCase__							)

    # Initialize the model and tokenizer
    _UpperCAmelCase							:						Tuple       							=					AutoTokenizer.from_pretrained(args.model_name_or_path							)
    _UpperCAmelCase							:						Optional[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(UpperCamelCase__							)
    _UpperCAmelCase							:						str       							=					args.prompt if args.prompt else input('''Model prompt >>> '''							)

    _UpperCAmelCase							:						Tuple       							=					tokenizer(UpperCamelCase__  , return_tensors='''pt'''  , add_special_tokens=UpperCamelCase__							)
    _UpperCAmelCase							:						Dict       							=					{key: value.to(args.device							) for key, value in inputs.items()}

    _UpperCAmelCase							:						List[Any]       							=					model.generate(
        **UpperCamelCase__  , max_length=args.length + len(inputs['''input_ids'''][0]							)  , penalty_alpha=args.penalty_alpha  , top_k=args.k  , )

    _UpperCAmelCase							:						Tuple       							=					[]
    for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__							):
        print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
        _UpperCAmelCase							:						Optional[Any]       							=					generated_sequence.tolist()

        # Decode text
        _UpperCAmelCase							:						Dict       							=					tokenizer.decode(UpperCamelCase__  , clean_up_tokenization_spaces=UpperCamelCase__  , add_special_tokens=UpperCamelCase__							)

        # Remove all text after the stop token
        _UpperCAmelCase							:						Any       							=					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
        _UpperCAmelCase							:						List[Any]       							=					(
            prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]  , clean_up_tokenization_spaces=UpperCamelCase__							)							) :]
        )

        generated_sequences.append(UpperCamelCase__							)
        print(UpperCamelCase__							)

    return generated_sequences


if __name__ == "__main__":
  main()









"""simple docstring"""
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


_lowerCAmelCase					:Union[str, Any]  =      logging.get_logger(__name__)

_lowerCAmelCase					:List[str]  =      {
    '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 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     a__             ='''gpt2'''
     a__             =['''past_key_values''']
     a__             ={
         '''hidden_size''': '''n_embd''',
         '''max_position_embeddings''': '''n_positions''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }





     def __init__(      self ,			A=5_0_2_5_7 ,			A=1_0_2_4 ,			A=7_6_8 ,			A=1_2 ,			A=1_2 ,			A=None ,			A="gelu_new" ,			A=0.1 ,			A=0.1 ,			A=0.1 ,			A=1E-5 ,			A=0.02 ,			A="cls_index" ,			A=True ,			A=None ,			A=True ,			A=0.1 ,			A=True ,			A=True ,			A=5_0_2_5_6 ,			A=5_0_2_5_6 ,			A=False ,			A=False ,			**A ,			)							->      Union[str, Any]:
         _UpperCAmelCase							:						Optional[int]       							=					vocab_size
         _UpperCAmelCase							:						Union[str, Any]       							=					n_positions
         _UpperCAmelCase							:						Optional[Any]       							=					n_embd
         _UpperCAmelCase							:						Tuple       							=					n_layer
         _UpperCAmelCase							:						Any       							=					n_head
         _UpperCAmelCase							:						Union[str, Any]       							=					n_inner
         _UpperCAmelCase							:						Optional[int]       							=					activation_function
         _UpperCAmelCase							:						Optional[int]       							=					resid_pdrop
         _UpperCAmelCase							:						Dict       							=					embd_pdrop
         _UpperCAmelCase							:						List[Any]       							=					attn_pdrop
         _UpperCAmelCase							:						Tuple       							=					layer_norm_epsilon
         _UpperCAmelCase							:						Optional[int]       							=					initializer_range
         _UpperCAmelCase							:						List[str]       							=					summary_type
         _UpperCAmelCase							:						Tuple       							=					summary_use_proj
         _UpperCAmelCase							:						List[Any]       							=					summary_activation
         _UpperCAmelCase							:						Optional[int]       							=					summary_first_dropout
         _UpperCAmelCase							:						int       							=					summary_proj_to_labels
         _UpperCAmelCase							:						Union[str, Any]       							=					scale_attn_weights
         _UpperCAmelCase							:						Any       							=					use_cache
         _UpperCAmelCase							:						Tuple       							=					scale_attn_by_inverse_layer_idx
         _UpperCAmelCase							:						Optional[Any]       							=					reorder_and_upcast_attn

         _UpperCAmelCase							:						Union[str, Any]       							=					bos_token_id
         _UpperCAmelCase							:						Optional[int]       							=					eos_token_id

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



class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     def __init__(      self ,			A ,			A = "default" ,			A = None ,			A = False ,			)							->      str:
         super().__init__(A ,			task=A ,			patching_specs=A ,			use_past=A	)
         if not getattr(self._config ,			'''pad_token_id''' ,			A	):
             # TODO: how to do that better?
             _UpperCAmelCase							:						Optional[int]       							=					0





     @property
     def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
         _UpperCAmelCase							:						List[str]       							=					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
         if self.use_past:
             self.fill_with_past_key_values_(A ,			direction='''inputs'''	)
             _UpperCAmelCase							:						Union[str, Any]       							=					{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             _UpperCAmelCase							:						Tuple       							=					{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase			(      self	)							->      int:
         return self._config.n_layer





     @property
     def 				__lowerCAmelCase			(      self	)							->      int:
         return self._config.n_head





     def 				__lowerCAmelCase			(      self ,			A ,			A = -1 ,			A = -1 ,			A = False ,			A = None ,			)							->      Mapping[str, Any]:
         _UpperCAmelCase							:						int       							=					super(A ,			self	).generate_dummy_inputs(
             A ,			batch_size=A ,			seq_length=A ,			is_pair=A ,			framework=A	)

         # We need to order the input in the way they appears in the forward()
         _UpperCAmelCase							:						List[Any]       							=					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

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

         _UpperCAmelCase							:						int       							=					common_inputs['''attention_mask''']
         if self.use_past:
             _UpperCAmelCase							:						int       							=					ordered_inputs['''attention_mask'''].dtype
             _UpperCAmelCase							:						Dict       							=					torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(A ,			A ,			dtype=A	)] ,			dim=1	)

         return ordered_inputs





     @property
     def 				__lowerCAmelCase			(      self	)							->      int:
         return 1_3


