


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


__snake_case            =   getLogger(__name__)


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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int = 8       ,  SCREAMING_SNAKE_CASE_   :		str = DEFAULT_DEVICE       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=False       ,  SCREAMING_SNAKE_CASE_   :		List[Any]="summarization"       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=None       ,  **SCREAMING_SNAKE_CASE_   :		List[Any]       ,  ):





			"""simple docstring"""




			UpperCamelCase         =					Path(snake_case__	).open("""w"""       ,  encoding="""utf-8"""	)
			UpperCamelCase         =					str(snake_case__	)
			UpperCamelCase         =					AutoModelForSeqaSeqLM.from_pretrained(snake_case__	).to(snake_case__	)
			if fpaa:
						UpperCamelCase         =					model.half()

			UpperCamelCase         =					AutoTokenizer.from_pretrained(snake_case__	)
			logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'	)  # if this is wrong, check config.model_type.

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






def 							_lowercase    (		):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str=True	):





			"""simple docstring"""




			UpperCamelCase         =					argparse.ArgumentParser()
			parser.add_argument("""model_name"""       ,  type=snake_case__       ,  help="""like facebook/bart-large-cnn,t5-base, etc."""	)
			parser.add_argument("""input_path"""       ,  type=snake_case__       ,  help="""like cnn_dm/test.source"""	)
			parser.add_argument("""save_path"""       ,  type=snake_case__       ,  help="""where to save summaries"""	)
			parser.add_argument("""--reference_path"""       ,  type=snake_case__       ,  required=snake_case__       ,  help="""like cnn_dm/test.target"""	)
			parser.add_argument("""--score_path"""       ,  type=snake_case__       ,  required=snake_case__       ,  default="""metrics.json"""       ,  help="""where to save metrics"""	)
			parser.add_argument("""--device"""       ,  type=snake_case__       ,  required=snake_case__       ,  default=snake_case__       ,  help="""cuda, cuda:1, cpu etc."""	)
			parser.add_argument(
			    """--prefix"""       ,  type=snake_case__       ,  required=snake_case__       ,  default=snake_case__       ,  help="""will be added to the begininng of src examples"""	)
			parser.add_argument("""--task"""       ,  type=snake_case__       ,  default="""summarization"""       ,  help="""used for task_specific_params + metrics"""	)
			parser.add_argument("""--bs"""       ,  type=snake_case__       ,  default=8       ,  required=snake_case__       ,  help="""batch size"""	)
			parser.add_argument(
			    """--n_obs"""       ,  type=snake_case__       ,  default=-1       ,  required=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=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
			UpperCamelCase         =					parser.parse_known_args()
			UpperCamelCase         =					parse_numeric_n_bool_cl_kwargs(snake_case__	)
			if parsed_args and verbose:
						print(f'parsed the following generate kwargs: {parsed_args}'	)
			UpperCamelCase         =					[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path	).readlines()]
			if args.n_obs > 0:
						UpperCamelCase         =					examples[: args.n_obs]
			Path(args.save_path	).parent.mkdir(exist_ok=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"""	)

			UpperCamelCase         =					generate_summaries_or_translations(
			    snake_case__       ,  args.save_path       ,  args.model_name       ,  batch_size=args.bs       ,  device=args.device       ,  fpaa=args.fpaa       ,  task=args.task       ,  prefix=args.prefix       ,  **snake_case__       ,  )

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(snake_case__	)
			if args.info:
						UpperCamelCase         =					args.info

			if verbose:
						print(snake_case__	)

			if args.score_path is not None:
						json.dump(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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			__snake_case            =   None


__snake_case            =   logging.get_logger(__name__)


__snake_case            =   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__snake_case            =   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__snake_case            =   {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
__snake_case            =   ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= MBartTokenizer

			lowercase						= []
			lowercase						= []




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		str=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		Dict="<s>"       ,       __magic_name__							:		Union[str, Any]="</s>"       ,       __magic_name__							:		List[str]="</s>"       ,       __magic_name__							:		Optional[int]="<s>"       ,       __magic_name__							:		int="<unk>"       ,       __magic_name__							:		str="<pad>"       ,       __magic_name__							:		List[str]="<mask>"       ,       __magic_name__							:		Tuple=None       ,       __magic_name__							:		Dict=None       ,       __magic_name__							:		Dict=None       ,       **__magic_name__							:		Tuple       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					AddedToken(__magic_name__       ,       lstrip=__magic_name__       ,       rstrip=__magic_name__						) if isinstance(__magic_name__       ,       __magic_name__						) else mask_token

						super().__init__(
						    vocab_file=__magic_name__       ,       tokenizer_file=__magic_name__       ,       bos_token=__magic_name__       ,       eos_token=__magic_name__       ,       sep_token=__magic_name__       ,       cls_token=__magic_name__       ,       unk_token=__magic_name__       ,       pad_token=__magic_name__       ,       mask_token=__magic_name__       ,       src_lang=__magic_name__       ,       tgt_lang=__magic_name__       ,       additional_special_tokens=__magic_name__       ,       **__magic_name__       ,       )

						UpperCamelCase         =					vocab_file
						UpperCamelCase         =					False if not self.vocab_file else True

						UpperCamelCase         =					FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

						self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}						)
						UpperCamelCase         =					{
						    lang_code: self.convert_tokens_to_ids(__magic_name__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCamelCase         =					src_lang if src_lang is not None else """en_XX"""
						UpperCamelCase         =					self.convert_tokens_to_ids(self._src_lang						)
						UpperCamelCase         =					tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						return self._src_lang




			@src_lang.setter
			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[self.sep_token_id]
						UpperCamelCase         =					[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str]       ,       __magic_name__							:		Optional[str]       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""						)
						UpperCamelCase         =					src_lang
						UpperCamelCase         =					self(__magic_name__       ,       add_special_tokens=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					tgt_lang_id
						return inputs




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		str = "en_XX"       ,       __magic_name__							:		Optional[List[str]] = None       ,       __magic_name__							:		str = "ro_RO"       ,       **__magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					src_lang
						UpperCamelCase         =					tgt_lang
						return super().prepare_seqaseq_batch(__magic_name__       ,       __magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						return self.set_src_lang_special_tokens(self.src_lang						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						return self.set_tgt_lang_special_tokens(self.tgt_lang						)




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,       self.prefix_tokens + self.suffix_tokens						)						)       ,       )




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,       self.prefix_tokens + self.suffix_tokens						)						)       ,       )





			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







						"""simple docstring"""







						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
									    """tokenizer."""						)

						if not os.path.isdir(__magic_name__						):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'						)
									return
						UpperCamelCase         =					os.path.join(
						    __magic_name__       ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(__magic_name__						):
									copyfile(self.vocab_file       ,       __magic_name__						)

						return (out_vocab_file,)




