





import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCAmelCase__  :	str     =getLogger(__name__)


UpperCAmelCase__  :	Tuple     ="cuda" if torch.cuda.is_available() else "cpu"





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase = 8	,					_UpperCAmelCase = DEFAULT_DEVICE	,					_UpperCAmelCase=False	,					_UpperCAmelCase="summarization"	,					_UpperCAmelCase=None	,					**_UpperCAmelCase	,					)  ->     Any:
				lowerCamelCase		       =Path(_SCREAMING_SNAKE_CASE ).open("""w"""	,					encoding="""utf-8""" )
				lowerCamelCase		       =str(_SCREAMING_SNAKE_CASE )
				lowerCamelCase		       =AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
				if fpaa:
								lowerCamelCase		       =model.half()

				lowerCamelCase		       =AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
				logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

				lowerCamelCase		       =time.time()
				# update config with task specific params
				use_task_specific_params(_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE )
				if prefix is None:
								lowerCamelCase		       =prefix or getattr(model.config	,					"""prefix"""	,					"""""" ) or """"""
				for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE ) ) ):
								lowerCamelCase		       =[prefix + text for text in examples_chunk]
								lowerCamelCase		       =tokenizer(_SCREAMING_SNAKE_CASE	,					return_tensors="""pt"""	,					truncation=_SCREAMING_SNAKE_CASE	,					padding="""longest""" ).to(_SCREAMING_SNAKE_CASE )
								lowerCamelCase		       =model.generate(
								    input_ids=batch.input_ids	,					attention_mask=batch.attention_mask	,					**_SCREAMING_SNAKE_CASE	,					)
								lowerCamelCase		       =tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	,					skip_special_tokens=_SCREAMING_SNAKE_CASE	,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE )
								for hypothesis in dec:
												fout.write(hypothesis + """\n""" )
												fout.flush()
				fout.close()
				lowerCamelCase		       =int(time.time() - start_time )  # seconds
				lowerCamelCase		       =len(_SCREAMING_SNAKE_CASE )
				return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,					4 )}





def    _lowercase    (  )  ->     str:
				return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S""" )





def    _lowercase    (  _UpperCAmelCase=True )  ->     int:
				lowerCamelCase		       =argparse.ArgumentParser()
				parser.add_argument("""model_name"""	,					type=_SCREAMING_SNAKE_CASE	,					help="""like facebook/bart-large-cnn,t5-base, etc.""" )
				parser.add_argument("""input_path"""	,					type=_SCREAMING_SNAKE_CASE	,					help="""like cnn_dm/test.source""" )
				parser.add_argument("""save_path"""	,					type=_SCREAMING_SNAKE_CASE	,					help="""where to save summaries""" )
				parser.add_argument("""--reference_path"""	,					type=_SCREAMING_SNAKE_CASE	,					required=_SCREAMING_SNAKE_CASE	,					help="""like cnn_dm/test.target""" )
				parser.add_argument("""--score_path"""	,					type=_SCREAMING_SNAKE_CASE	,					required=_SCREAMING_SNAKE_CASE	,					default="""metrics.json"""	,					help="""where to save metrics""" )
				parser.add_argument("""--device"""	,					type=_SCREAMING_SNAKE_CASE	,					required=_SCREAMING_SNAKE_CASE	,					default=_SCREAMING_SNAKE_CASE	,					help="""cuda, cuda:1, cpu etc.""" )
				parser.add_argument(
				    """--prefix"""	,					type=_SCREAMING_SNAKE_CASE	,					required=_SCREAMING_SNAKE_CASE	,					default=_SCREAMING_SNAKE_CASE	,					help="""will be added to the begininng of src examples""" )
				parser.add_argument("""--task"""	,					type=_SCREAMING_SNAKE_CASE	,					default="""summarization"""	,					help="""used for task_specific_params + metrics""" )
				parser.add_argument("""--bs"""	,					type=_SCREAMING_SNAKE_CASE	,					default=8	,					required=_SCREAMING_SNAKE_CASE	,					help="""batch size""" )
				parser.add_argument(
				    """--n_obs"""	,					type=_SCREAMING_SNAKE_CASE	,					default=-1	,					required=_SCREAMING_SNAKE_CASE	,					help="""How many observations. Defaults to all.""" )
				parser.add_argument("""--fp16"""	,					action="""store_true""" )
				parser.add_argument("""--dump-args"""	,					action="""store_true"""	,					help="""print the custom hparams with the results""" )
				parser.add_argument(
				    """--info"""	,					nargs="""?"""	,					type=_SCREAMING_SNAKE_CASE	,					const=datetime_now()	,					help=(
				        """use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g."""
				        """ lang=en-ru. If no value is passed, the current datetime string will be used."""
				    )	,					)
				# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
				lowerCamelCase       ,     lowerCamelCase		       =parser.parse_known_args()
				lowerCamelCase		       =parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE )
				if parsed_args and verbose:
								print(F"""parsed the following generate kwargs: {parsed_args}""" )
				with open(args.input_path ) as f:
								lowerCamelCase		       =[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								lowerCamelCase		       =examples[: args.n_obs]
				Path(args.save_path ).parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
				if args.reference_path is None and Path(args.score_path ).exists():
								warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
				lowerCamelCase		       =generate_summaries_or_translations(
				    _SCREAMING_SNAKE_CASE	,					args.save_path	,					args.model_name	,					batch_size=args.bs	,					device=args.device	,					fpaa=args.fpaa	,					task=args.task	,					prefix=args.prefix	,					**_SCREAMING_SNAKE_CASE	,					)

				if args.reference_path is None:
								return {}

				# Compute scores
				lowerCamelCase		       =calculate_bleu if """translation""" in args.task else calculate_rouge
				lowerCamelCase		       =[x.rstrip() for x in open(args.save_path ).readlines()]
				lowerCamelCase		       =[x.rstrip() for x in open(args.reference_path ).readlines()][: len(_SCREAMING_SNAKE_CASE )]
				lowerCamelCase		       =score_fn(_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE )
				scores.update(_SCREAMING_SNAKE_CASE )

				if args.dump_args:
								scores.update(_SCREAMING_SNAKE_CASE )
				if args.info:
								lowerCamelCase		       =args.info

				if verbose:
								print(_SCREAMING_SNAKE_CASE )

				if args.score_path is not None:
								json.dump(_SCREAMING_SNAKE_CASE	,					open(args.score_path	,					"""w""" ) )

				return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)








import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoConfig(
        hidden_size=config_json['n_embd']       ,      num_layers=config_json['n_layer']       ,      num_heads=config_json['n_head']       ,      attention_types=config_json['attention_types']       ,      max_position_embeddings=config_json['n_positions']       ,      resid_dropout=config_json['res_dropout']       ,      embed_dropout=config_json['embed_dropout']       ,      attention_dropout=config_json['attn_dropout']       ,      )
    print(f"""Building PyTorch model from configuration: {config}"""				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoForCausalLM(_SCREAMING_SNAKE_CASE				)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
    model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
       UpperCamelCase__     :					Tuple								=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained mesh-tf model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


