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,
)
UpperCAmelCase							=       logging.getLogger(__name__)






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






def 					SCREAMING_SNAKE_CASE_			(		):
      snake_case_     :			int       =       argparse.ArgumentParser()
      parser.add_argument(
          "--model_name_or_path" ,	default=lowerCAmelCase_ ,	type=lowerCAmelCase_ ,	required=lowerCAmelCase_ ,	)
      parser.add_argument("--prompt" ,	type=lowerCAmelCase_ ,	default=""     )
      parser.add_argument("--length" ,	type=lowerCAmelCase_ ,	default=2_0     )
      parser.add_argument("--stop_token" ,	type=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	help="Token at which text generation is stopped"     )
      parser.add_argument(
          "--temperature" ,	type=lowerCAmelCase_ ,	default=1.0 ,	help="temperature of 1.0 has no effect, lower tend toward greedy sampling" ,	)
      parser.add_argument(
          "--repetition_penalty" ,	type=lowerCAmelCase_ ,	default=1.0 ,	help="primarily useful for CTRL model; in that case, use 1.2"     )
      parser.add_argument("--k" ,	type=lowerCAmelCase_ ,	default=0     )
      parser.add_argument("--penalty_alpha" ,	type=lowerCAmelCase_ ,	default=0.0     )
      parser.add_argument("--p" ,	type=lowerCAmelCase_ ,	default=0.9     )

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

      parser.add_argument("--seed" ,	type=lowerCAmelCase_ ,	default=4_2 ,	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" ,	)
      snake_case_     :			str       =       parser.parse_args()

      snake_case_     :			Tuple       =       torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"     )
      snake_case_     :			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(lowerCAmelCase_     )

      # Initialize the model and tokenizer
      snake_case_     :			Any       =       AutoTokenizer.from_pretrained(args.model_name_or_path     )
      snake_case_     :			List[str]       =       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(lowerCAmelCase_     )
      snake_case_     :			Dict       =       args.prompt if args.prompt else input("Model prompt >>> "     )

      snake_case_     :			Dict       =       tokenizer(lowerCAmelCase_ ,	return_tensors="pt" ,	add_special_tokens=lowerCAmelCase_     )
      snake_case_     :			Union[str, Any]       =       {key: value.to(args.device     ) for key, value in inputs.items()}

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

      snake_case_     :			int       =       []
      for generated_sequence_idx, generated_sequence in enumerate(lowerCAmelCase_     ):
            print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="     )
            snake_case_     :			Dict       =       generated_sequence.tolist()

            # Decode text
            snake_case_     :			Tuple       =       tokenizer.decode(lowerCAmelCase_ ,	clean_up_tokenization_spaces=lowerCAmelCase_ ,	add_special_tokens=lowerCAmelCase_     )

            # Remove all text after the stop token
            snake_case_     :			Optional[int]       =       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
            snake_case_     :			Tuple       =       (
                prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0] ,	clean_up_tokenization_spaces=lowerCAmelCase_     )     ) :]
            )

            generated_sequences.append(lowerCAmelCase_     )
            print(lowerCAmelCase_     )

      return generated_sequences


if __name__ == "__main__":
       main()




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



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




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



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




         '''simple docstring'''




         if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )
         else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )



