



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,
)
lowerCamelCase__     =	logging.getLogger(__name__)

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[Any]      ):
    """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"""







    __a					       =						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"""			,       )
    __a					       =						parser.parse_args()

    __a					       =						torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
    __a					       =						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
    __a					       =						AutoTokenizer.from_pretrained(args.model_name_or_path      )
    __a					       =						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      )
    __a					       =						args.prompt if args.prompt else input("""Model prompt >>> """      )

    __a					       =						tokenizer(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       add_special_tokens=_SCREAMING_SNAKE_CASE      )
    __a					       =						{key: value.to(args.device      ) for key, value in inputs.items()}

    __a					       =						model.generate(
        **_SCREAMING_SNAKE_CASE			,       max_length=args.length + len(inputs["""input_ids"""][0]      )			,       penalty_alpha=args.penalty_alpha			,       top_k=args.k			,       )

    __a					       =						[]
    for generated_sequence_idx, generated_sequence in enumerate(_SCREAMING_SNAKE_CASE      ):
        print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="      )
        __a					       =						generated_sequence.tolist()

        # Decode text
        __a					       =						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
        __a					       =						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
        __a					       =						(
            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()





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







				__a					       =						filter(lambda _SCREAMING_SNAKE_CASE      : p.requires_grad			,       model.parameters()      )
				__a					       =						sum([np.prod(p.size()      ) for p in model_parameters]      )
				return params


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
											pl_module.logger.log_metrics(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

											'''simple docstring'''
											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
											__a					       =						trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
											# Log results
											__a					       =						Path(pl_module.hparams.output_dir		)
											if type_path == "test":
															__a					       =						od / """test_results.txt"""
															__a					       =						od / """test_generations.txt"""
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__a					       =						od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															__a					       =						od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=__lowercase		)
															generations_file.parent.mkdir(exist_ok=__lowercase		)
											with open(__lowercase ,			"""a+"""		) as writer:
															for key in sorted(__lowercase		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

											if "preds" in metrics:
															__a					       =						"""\n""".join(metrics["""preds"""]		)
															generations_file.open("""w+"""		).write(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# mp stands for million parameters
											trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											return self._write_logs(__lowercase ,			__lowercase ,			"""test"""		)







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						Dict		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{val_avg_loss:.4f}-{step_count}"""
				else:
								raise NotImplementedError(
								    f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
								    """ this function."""      )

				__a					       =						ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE			,       filename=_SCREAMING_SNAKE_CASE			,       monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       save_top_k=_SCREAMING_SNAKE_CASE			,       )
				return checkpoint_callback

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				return EarlyStopping(
				    monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       patience=_SCREAMING_SNAKE_CASE			,       verbose=_SCREAMING_SNAKE_CASE			,       )
