


import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Dict							):
     # Initialize PyTorch model
     __UpperCamelCase       				=CanineConfig()
     __UpperCamelCase       				=CanineModel(SCREAMING_SNAKE_CASE__							)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'							)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     __UpperCamelCase       				=CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       default=None,
       type=str,
       required=True,
       help='Path to a folder where the PyTorch model will be placed.',
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









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									=		logging.getLogger(__name__)

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

def 		_UpperCAmelCase							(     ):
     __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()





