






'''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


__lowerCamelCase   :					str   							=					getLogger(__name__)


__lowerCamelCase   :					Any   							=					'''cuda''' if torch.cuda.is_available() else '''cpu'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__ = 8     ,__magic_name__ = DEFAULT_DEVICE     ,__magic_name__=False     ,__magic_name__="summarization"     ,__magic_name__=None     ,**__magic_name__     ,)->    Dict:



			"""simple docstring"""



			snake_case_				:					int   =       Path(__magic_name__						).open("w"     ,encoding="utf-8"						)
			snake_case_				:					Union[str, Any]   =       str(__magic_name__						)
			snake_case_				:					Any   =       AutoModelForSeqaSeqLM.from_pretrained(__magic_name__						).to(__magic_name__						)
			if fpaa:
						snake_case_				:					Union[str, Any]   =       model.half()

			snake_case_				:					Optional[Any]   =       AutoTokenizer.from_pretrained(__magic_name__						)
			logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''						)  # if this is wrong, check config.model_type.

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

def 	__UpperCAmelCase       ( )->    Optional[int]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__=True						)->    Dict:



			"""simple docstring"""



			snake_case_				:					str   =       argparse.ArgumentParser()
			parser.add_argument("model_name"     ,type=__magic_name__     ,help="like facebook/bart-large-cnn,t5-base, etc."						)
			parser.add_argument("input_path"     ,type=__magic_name__     ,help="like cnn_dm/test.source"						)
			parser.add_argument("save_path"     ,type=__magic_name__     ,help="where to save summaries"						)
			parser.add_argument("--reference_path"     ,type=__magic_name__     ,required=__magic_name__     ,help="like cnn_dm/test.target"						)
			parser.add_argument("--score_path"     ,type=__magic_name__     ,required=__magic_name__     ,default="metrics.json"     ,help="where to save metrics"						)
			parser.add_argument("--device"     ,type=__magic_name__     ,required=__magic_name__     ,default=__magic_name__     ,help="cuda, cuda:1, cpu etc."						)
			parser.add_argument(
			    "--prefix"     ,type=__magic_name__     ,required=__magic_name__     ,default=__magic_name__     ,help="will be added to the begininng of src examples"						)
			parser.add_argument("--task"     ,type=__magic_name__     ,default="summarization"     ,help="used for task_specific_params + metrics"						)
			parser.add_argument("--bs"     ,type=__magic_name__     ,default=8     ,required=__magic_name__     ,help="batch size"						)
			parser.add_argument(
			    "--n_obs"     ,type=__magic_name__     ,default=-1     ,required=__magic_name__     ,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=__magic_name__     ,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_				:					List[Any]   =       parser.parse_known_args()
			snake_case_				:					List[Any]   =       parse_numeric_n_bool_cl_kwargs(__magic_name__						)
			if parsed_args and verbose:
						print(F'''parsed the following generate kwargs: {parsed_args}'''						)
			snake_case_				:					Dict   =       [" " + 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=__magic_name__						)

			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_				:					int   =       generate_summaries_or_translations(
			    __magic_name__     ,args.save_path     ,args.model_name     ,batch_size=args.bs     ,device=args.device     ,fpaa=args.fpaa     ,task=args.task     ,prefix=args.prefix     ,**__magic_name__     ,)

			if args.reference_path is None:
						return {}

			# Compute scores
			snake_case_				:					Dict   =       calculate_bleu if "translation" in args.task else calculate_rouge
			snake_case_				:					str   =       [x.rstrip() for x in open(args.save_path						).readlines()]
			snake_case_				:					List[str]   =       [x.rstrip() for x in open(args.reference_path						).readlines()][: len(__magic_name__						)]
			snake_case_				:					dict   =       score_fn(__magic_name__     ,__magic_name__						)
			scores.update(__magic_name__						)

			if args.dump_args:
						scores.update(__magic_name__						)
			if args.info:
						snake_case_				:					Union[str, Any]   =       args.info

			if verbose:
						print(__magic_name__						)

			if args.score_path is not None:
						json.dump(__magic_name__     ,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 inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase   :					Any   							=					'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase   :					List[str]   							=					direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCamelCase   :					Optional[Any]   							=					transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__lowerCamelCase   :					Union[str, Any]   							=					re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


__lowerCamelCase   :					Any   							=					{
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       None

			# source code of `config_class`
			snake_case_				:					List[Any]   =       inspect.getsource(__magic_name__						)
			snake_case_				:					List[str]   =       _re_checkpoint.findall(__magic_name__						)

			# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
			# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
			for ckpt_name, ckpt_link in checkpoints:
						# allow the link to end with `/`
						if ckpt_link.endswith("/"						):
									snake_case_				:					Optional[Any]   =       ckpt_link[:-1]

						# verify the checkpoint name corresponds to the checkpoint link
						snake_case_				:					str   =       F'''https://huggingface.co/{ckpt_name}'''
						if ckpt_link == ckpt_link_from_name:
									snake_case_				:					Dict   =       ckpt_name
									break

			return checkpoint

def 	__UpperCAmelCase       ( )->    Dict:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       []

			for config_class in list(CONFIG_MAPPING.values()						):
						# Skip deprecated models
						if "models.deprecated" in config_class.__module__:
									continue
						snake_case_				:					str   =       get_checkpoint_from_config_class(__magic_name__						)

						snake_case_				:					Union[str, Any]   =       config_class.__name__
						if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
									configs_without_checkpoint.append(__magic_name__						)

			if len(__magic_name__						) > 0:
						snake_case_				:					Tuple   =       "\n".join(sorted(__magic_name__						)						)
						raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}'''						)


if __name__ == "__main__":
		check_config_docstrings_have_checkpoints()




