

'''simple docstring'''




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_a			:	Optional[int]								=  logging.get_logger(__name__)

_a			:	int								=  {
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}



class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Optional[int]      					=				"deberta"
  def __init__(       self				,	a_=5_0_2_6_5				,	a_=7_6_8				,	a_=1_2				,	a_=1_2				,	a_=3_0_7_2				,	a_="gelu"				,	a_=0.1				,	a_=0.1				,	a_=5_1_2				,	a_=0				,	a_=0.02				,	a_=1E-7				,	a_=False				,	a_=-1				,	a_=0				,	a_=True				,	a_=None				,	a_=0				,	a_="gelu"				,	**a_				,	)							->			int:


    """simple docstring"""
    super().__init__(**a_       )

    UpperCAmelCase  							=		hidden_size
    UpperCAmelCase  							=		num_hidden_layers
    UpperCAmelCase  							=		num_attention_heads
    UpperCAmelCase  							=		intermediate_size
    UpperCAmelCase  							=		hidden_act
    UpperCAmelCase  							=		hidden_dropout_prob
    UpperCAmelCase  							=		attention_probs_dropout_prob
    UpperCAmelCase  							=		max_position_embeddings
    UpperCAmelCase  							=		type_vocab_size
    UpperCAmelCase  							=		initializer_range
    UpperCAmelCase  							=		relative_attention
    UpperCAmelCase  							=		max_relative_positions
    UpperCAmelCase  							=		pad_token_id
    UpperCAmelCase  							=		position_biased_input

    # Backwards compatibility
    if type(a_       ) == str:
      UpperCAmelCase  							=		[x.strip() for x in pos_att_type.lower().split('|'       )]

    UpperCAmelCase  							=		pos_att_type
    UpperCAmelCase  							=		vocab_size
    UpperCAmelCase  							=		layer_norm_eps

    UpperCAmelCase  							=		kwargs.get('pooler_hidden_size'				,	a_       )
    UpperCAmelCase  							=		pooler_dropout
    UpperCAmelCase  							=		pooler_hidden_act




class     lowercase_		(						a	):

  '''simple docstring'''





  @property
  def   snake_case_     (       self       )							->			Mapping[str, Mapping[int, str]]:


    """simple docstring"""
    if self.task == "multiple-choice":
      UpperCAmelCase  							=		{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      UpperCAmelCase  							=		{0: 'batch', 1: 'sequence'}
    if self._config.type_vocab_size > 0:
      return OrderedDict(
          [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]       )
    else:
      return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]       )
  @property
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    return 1_2





  def   snake_case_     (       self				,	a_				,	a_ = -1				,	a_ = -1				,	a_ = -1				,	a_ = False				,	a_ = None				,	a_ = 3				,	a_ = 4_0				,	a_ = 4_0				,	a_ = None				,	)							->			Mapping[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		super().generate_dummy_inputs(preprocessor=a_				,	framework=a_       )
    if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
      del dummy_inputs["token_type_ids"]
    return dummy_inputs





'''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,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


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

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

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				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  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		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(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		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(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

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

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


