

"""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,
)
_SCREAMING_SNAKE_CASE				:     int       				=       logging.getLogger(__name__)



def 				_lowerCAmelCase					(  UpperCAmelCase   :							Tuple ):


					'''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'''






					UpperCamelCase__						:			Dict				     =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=20 )
					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=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__						:			List[Any]				     =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
					UpperCamelCase__						:			Optional[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(lowerCAmelCase_ )

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

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

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

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

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

										# 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=lowerCAmelCase_ ) ) :]
										)

										generated_sequences.append(lowerCAmelCase_ )
										print(lowerCAmelCase_ )

					return generated_sequences


if __name__ == "__main__":
				main()








import argparse

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


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

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

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

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

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


if __name__ == "__main__":
     UpperCamelCase								=     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.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
