
'''simple docstring'''





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    					=   getLogger(__name__)


UpperCAmelCase    					=   "cuda" if torch.cuda.is_available() else "cpu"

def      A (       A_							:					List[str]    ,							A_							:					List[str]    ,							A_							:					List[str]    ,							A_							:					Tuple = 8    ,							A_							:					Any = DEFAULT_DEVICE    ,							A_							:					List[Any]=False    ,							A_							:					Tuple="summarization"    ,							A_							:					Any=None    ,							**A_							:					List[str]    ,							):
						snake_case       :     List[Any] =    Path(__SCREAMING_SNAKE_CASE       ).open('''w'''    ,							encoding='''utf-8'''       )
						snake_case       :     int =    str(__SCREAMING_SNAKE_CASE       )
						snake_case       :     Any =    AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )
						if fpaa:
												snake_case       :     Tuple =    model.half()

						snake_case       :     Union[str, Any] =    AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )
						logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""       )  # if this is wrong, check config.model_type.

						snake_case       :     List[str] =    time.time()
						# update config with task specific params
						use_task_specific_params(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE       )
						if prefix is None:
												snake_case       :     Optional[int] =    prefix or getattr(model.config    ,							'''prefix'''    ,							''''''       ) or ''''''
						for examples_chunk in tqdm(list(chunks(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE       )       )       ):
												snake_case       :     List[Any] =    [prefix + text for text in examples_chunk]
												snake_case       :     str =    tokenizer(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''    ,							truncation=__SCREAMING_SNAKE_CASE    ,							padding='''longest'''       ).to(__SCREAMING_SNAKE_CASE       )
												snake_case       :     str =    model.generate(
												    input_ids=batch.input_ids    ,							attention_mask=batch.attention_mask    ,							**__SCREAMING_SNAKE_CASE    ,							)
												snake_case       :     Any =    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()
						snake_case       :     Tuple =    int(time.time() - start_time       )  # seconds
						snake_case       :     str =    len(__SCREAMING_SNAKE_CASE       )
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,							4       )}

def      A (       ):
						return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''       )

def      A (       A_							:					Tuple=True       ):
						snake_case       :     Optional[Any] =    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
						snake_case,					snake_case       :     Optional[Any] =    parser.parse_known_args()
						snake_case       :     List[Any] =    parse_numeric_n_bool_cl_kwargs(__SCREAMING_SNAKE_CASE       )
						if parsed_args and verbose:
												print(F"""parsed the following generate kwargs: {parsed_args}"""       )
						snake_case       :     str =    [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path       ).readlines()]
						if args.n_obs > 0:
												snake_case       :     Dict =    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."""       )

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError('''Can\'t mix --fp16 and --device cpu'''       )

						snake_case       :     List[Any] =    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
						snake_case       :     str =    calculate_bleu if '''translation''' in args.task else calculate_rouge
						snake_case       :     List[Any] =    [x.rstrip() for x in open(args.save_path       ).readlines()]
						snake_case       :     Any =    [x.rstrip() for x in open(args.reference_path       ).readlines()][: len(__SCREAMING_SNAKE_CASE       )]
						snake_case       :     Optional[int] =    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:
												snake_case       :     Union[str, Any] =    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)



'''simple docstring'''





import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase    					=   {
    "Salesforce/codegen-350M-mono": 2_048,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = CodeGenTokenizer




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Dict=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				List[Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				List[str]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				int=False,    **SCREAMING_SNAKE_CASE_	:				Optional[int],    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Dict =    kwargs.pop('''name_or_path''',    ''''''      )
																raise ValueError(
																    '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
																    '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
																    F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
																    F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
																    '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
																    ''' so that the fast tokenizer works correctly.'''      )

										snake_case       :     int =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     Optional[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Union[str, Any] =    add_prefix_space
																snake_case       :     Optional[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     int =    add_prefix_space




				def 	__snake_case							(							self	:				List[Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str],    *SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     Optional[int] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],    SCREAMING_SNAKE_CASE_	:				bool = False,    SCREAMING_SNAKE_CASE_	:				bool = None,    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any],    ):
										snake_case       :     Dict =    super().decode(
										    token_ids=SCREAMING_SNAKE_CASE_,    skip_special_tokens=SCREAMING_SNAKE_CASE_,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if truncate_before_pattern is not None and len(SCREAMING_SNAKE_CASE_      ) > 0:
																snake_case       :     Optional[int] =    self.truncate(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										return decoded_text




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										def find_re(SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     Optional[Any] =    pattern.search(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																return m.start() if m else -1

										snake_case       :     Union[str, Any] =    [re.compile(SCREAMING_SNAKE_CASE_,    re.MULTILINE      ) for pattern in truncate_before_pattern]

										snake_case       :     Union[str, Any] =    list(re.finditer('''^print''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Tuple =    completion[: prints[1].start()]

										snake_case       :     List[str] =    list(re.finditer('''^def''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Optional[Any] =    completion[: defs[1].start()]

										snake_case       :     Tuple =    0

										snake_case       :     List[Any] =    [
										    pos for pos in [find_re(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) for terminal in terminals] if pos != -1
										]

										if len(SCREAMING_SNAKE_CASE_      ) > 0:
																return completion[: min(SCREAMING_SNAKE_CASE_      )]
										else:
																return completion

