
"""simple docstring"""
import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase		:    Optional[int]			=       logging.get_logger(__name__)

lowercase		:    Optional[Any]			=       {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

lowercase		:    Tuple			=       {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

lowercase		:    Union[str, Any]			=       {'stas/tiny-wmt19-en-de': 10_24}
lowercase		:    str			=       {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 10_24,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      Dict:


					'''simple docstring'''
					__a:    Dict		     =       set()
					__a:    str		     =       word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)			)
										__a:    List[str]		     =       char
					return pairs


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


					'''simple docstring'''
					__a:    Union[str, Any]		     =       text.replace("""，"""						,					""","""			)
					__a:    str		     =       re.sub(r"""。\s*"""						,					""". """						,					lowerCamelCase_			)
					__a:    List[str]		     =       text.replace("""、"""						,					""","""			)
					__a:    Optional[Any]		     =       text.replace("""”"""						,					"""\""""			)
					__a:    Optional[int]		     =       text.replace("""“"""						,					"""\""""			)
					__a:    Union[str, Any]		     =       text.replace("""∶"""						,					""":"""			)
					__a:    List[Any]		     =       text.replace("""："""						,					""":"""			)
					__a:    Optional[Any]		     =       text.replace("""？"""						,					"""?"""			)
					__a:    Tuple		     =       text.replace("""《"""						,					"""\""""			)
					__a:    List[str]		     =       text.replace("""》"""						,					"""\""""			)
					__a:    Union[str, Any]		     =       text.replace("""）"""						,					""")"""			)
					__a:    List[str]		     =       text.replace("""！"""						,					"""!"""			)
					__a:    int		     =       text.replace("""（"""						,					"""("""			)
					__a:    List[str]		     =       text.replace("""；"""						,					""";"""			)
					__a:    List[Any]		     =       text.replace("""１"""						,					"""1"""			)
					__a:    str		     =       text.replace("""」"""						,					"""\""""			)
					__a:    List[str]		     =       text.replace("""「"""						,					"""\""""			)
					__a:    Union[str, Any]		     =       text.replace("""０"""						,					"""0"""			)
					__a:    Optional[int]		     =       text.replace("""３"""						,					"""3"""			)
					__a:    Optional[int]		     =       text.replace("""２"""						,					"""2"""			)
					__a:    int		     =       text.replace("""５"""						,					"""5"""			)
					__a:    str		     =       text.replace("""６"""						,					"""6"""			)
					__a:    Optional[int]		     =       text.replace("""９"""						,					"""9"""			)
					__a:    Optional[Any]		     =       text.replace("""７"""						,					"""7"""			)
					__a:    Dict		     =       text.replace("""８"""						,					"""8"""			)
					__a:    Dict		     =       text.replace("""４"""						,					"""4"""			)
					__a:    List[str]		     =       re.sub(r"""．\s*"""						,					""". """						,					lowerCamelCase_			)
					__a:    int		     =       text.replace("""～"""						,					"""~"""			)
					__a:    Any		     =       text.replace("""’"""						,					"""'"""			)
					__a:    str		     =       text.replace("""…"""						,					"""..."""			)
					__a:    Any		     =       text.replace("""━"""						,					"""-"""			)
					__a:    Union[str, Any]		     =       text.replace("""〈"""						,					"""<"""			)
					__a:    Dict		     =       text.replace("""〉"""						,					""">"""			)
					__a:    Tuple		     =       text.replace("""【"""						,					"""["""			)
					__a:    int		     =       text.replace("""】"""						,					"""]"""			)
					__a:    Optional[int]		     =       text.replace("""％"""						,					"""%"""			)
					return text


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      Dict:


					'''simple docstring'''
					__a:    Optional[Any]		     =       []
					for char in text:
										__a:    Tuple		     =       unicodedata.category(lowerCamelCase_			)
										if cat.startswith("""C"""			):
															continue
										output.append(lowerCamelCase_			)
					return "".join(lowerCamelCase_			)




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE		  =    PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE		  =    PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE		  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE		  =    ['''input_ids''', '''attention_mask''']






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






												"""simple docstring"""



												super().__init__(
												    langs=snake_case__    ,			src_vocab_file=snake_case__    ,			tgt_vocab_file=snake_case__    ,			merges_file=snake_case__    ,			do_lower_case=snake_case__    ,			unk_token=snake_case__    ,			bos_token=snake_case__    ,			sep_token=snake_case__    ,			pad_token=snake_case__    ,			**snake_case__    ,			)

												try:
																	import sacremoses
												except ImportError:
																	raise ImportError(
																	    """You need to install sacremoses to use XLMTokenizer. """
																	    """See https://pypi.org/project/sacremoses/ for installation."""     )

												__a:    Tuple		     =       sacremoses

												__a:    Any		     =       src_vocab_file
												__a:    Optional[int]		     =       tgt_vocab_file
												__a:    Any		     =       merges_file
												__a:    Union[str, Any]		     =       do_lower_case

												# cache of sm.MosesPunctNormalizer instance
												__a:    Any		     =       {}
												# cache of sm.MosesTokenizer instance
												__a:    Union[str, Any]		     =       {}
												__a:    Tuple		     =       {}

												if langs and len(snake_case__     ) == 2:
																	__a     ,					__a:    Optional[int]		     =       langs
												else:
																	raise ValueError(
																	    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
																	    """Usually that means that tokenizer can't find a mapping for the given model path """
																	    """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""     )

												with open(snake_case__    ,			encoding="""utf-8"""     ) as src_vocab_handle:
																	__a:    List[str]		     =       json.load(snake_case__     )
												with open(snake_case__    ,			encoding="""utf-8"""     ) as tgt_vocab_handle:
																	__a:    List[str]		     =       json.load(snake_case__     )
																	__a:    Optional[Any]		     =       {v: k for k, v in tgt_vocab.items()}
												with open(snake_case__    ,			encoding="""utf-8"""     ) as merges_handle:
																	__a:    str		     =       merges_handle.read().split("""\n"""     )[:-1]
												__a:    str		     =       [tuple(merge.split()[:2]     ) for merge in merges]
												__a:    str		     =       dict(zip(snake_case__    ,			range(len(snake_case__     )     )     )     )
												__a:    List[Any]		     =       {}






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												return self.get_src_vocab()






							@property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return self.src_vocab_size






							def 				__lowercase	(       self    :      Any    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												if lang not in self.cache_moses_punct_normalizer:
																	__a:    Union[str, Any]		     =       self.sm.MosesPunctNormalizer(lang=snake_case__     )
																	__a:    List[Any]		     =       punct_normalizer
												return self.cache_moses_punct_normalizer[lang].normalize(snake_case__     )






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												if lang not in self.cache_moses_tokenizer:
																	__a:    Any		     =       self.sm.MosesTokenizer(lang=snake_case__     )
																	__a:    Tuple		     =       moses_tokenizer
												return self.cache_moses_tokenizer[lang].tokenize(
												    snake_case__    ,			aggressive_dash_splits=snake_case__    ,			return_str=snake_case__    ,			escape=snake_case__     )






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      Any    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												if lang not in self.cache_moses_detokenizer:
																	__a:    Any		     =       self.sm.MosesDetokenizer(lang=snake_case__     )
																	__a:    Tuple		     =       moses_detokenizer
												return self.cache_moses_detokenizer[lang].detokenize(snake_case__     )






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												__a:    str		     =       replace_unicode_punct(snake_case__     )
												__a:    Tuple		     =       self.moses_punct_norm(snake_case__    ,			snake_case__     )
												__a:    Optional[int]		     =       remove_non_printing_char(snake_case__     )
												return text






							@property
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												return len(self.encoder     )






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return len(self.decoder     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												return dict(self.encoder    ,			**self.added_tokens_encoder     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												return dict(self.decoder    ,			**self.added_tokens_decoder     )






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    int		     =       tuple(token[:-1]     ) + (token[-1] + """</w>""",)
												if token in self.cache:
																	return self.cache[token]
												__a:    Tuple		     =       get_pairs(snake_case__     )

												if not pairs:
																	return token + "</w>"

												while True:
																	__a:    Tuple		     =       min(snake_case__    ,			key=lambda snake_case__     : self.bpe_ranks.get(snake_case__    ,			float("""inf"""     )     )     )
																	if bigram not in self.bpe_ranks:
																						break
																	__a     ,					__a:    Optional[int]		     =       bigram
																	__a:    int		     =       []
																	__a:    Tuple		     =       0
																	while i < len(snake_case__     ):
																						try:
																											__a:    Tuple		     =       word.index(snake_case__    ,			snake_case__     )
																						except ValueError:
																											new_word.extend(word[i:]     )
																											break
																						else:
																											new_word.extend(word[i:j]     )
																											__a:    Any		     =       j

																						if word[i] == first and i < len(snake_case__     ) - 1 and word[i + 1] == second:
																											new_word.append(first + second     )
																											i += 2
																						else:
																											new_word.append(word[i]     )
																											i += 1
																	__a:    Dict		     =       tuple(snake_case__     )
																	__a:    List[Any]		     =       new_word
																	if len(snake_case__     ) == 1:
																						break
																	else:
																						__a:    Optional[int]		     =       get_pairs(snake_case__     )
												__a:    Tuple		     =       """ """.join(snake_case__     )
												if word == "\n  </w>":
																	__a:    int		     =       """\n</w>"""
												__a:    Union[str, Any]		     =       word
												return word






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Dict    ,			snake_case__    :      Dict="en"    ,			snake_case__    :      int=False     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       self.src_lang

												if self.do_lower_case:
																	__a:    Optional[int]		     =       text.lower()

												if bypass_tokenizer:
																	__a:    List[Any]		     =       text.split()
												else:
																	__a:    Tuple		     =       self.moses_pipeline(snake_case__    ,			lang=snake_case__     )
																	__a:    Union[str, Any]		     =       self.moses_tokenize(snake_case__    ,			lang=snake_case__     )

												__a:    List[str]		     =       []
												for token in text:
																	if token:
																						split_tokens.extend(list(self.bpe(snake_case__     ).split(""" """     )     )     )

												return split_tokens






							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      List[Any]     ):






												"""simple docstring"""



												return self.encoder.get(snake_case__    ,			self.encoder.get(self.unk_token     )     )






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												return self.decoder.get(snake_case__    ,			self.unk_token     )






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Tuple     ):






												"""simple docstring"""



												__a:    int		     =       [t.replace(""" """    ,			""""""     ).replace("""</w>"""    ,			""" """     ) for t in tokens]
												__a:    int		     =       """""".join(snake_case__     ).split()
												# detokenize
												__a:    List[Any]		     =       self.moses_detokenize(snake_case__    ,			self.tgt_lang     )
												return text






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      List[int]    ,			snake_case__    :      Optional[List[int]] = None     ):






												"""simple docstring"""



												__a:    str		     =       [self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																	return token_ids_a + sep
												return token_ids_a + sep + token_ids_a + sep






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      List[int]    ,			snake_case__    :      Optional[List[int]] = None    ,			snake_case__    :      bool = False     ):






												"""simple docstring"""



												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=snake_case__    ,			token_ids_a=snake_case__    ,			already_has_special_tokens=snake_case__     )
												# no bos used in fairseq
												if token_ids_a is not None:
																	return ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
												return ([0] * len(snake_case__     )) + [1]






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      List[int]    ,			snake_case__    :      Optional[List[int]] = None     ):






												"""simple docstring"""



												__a:    Dict		     =       [self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																	return len(token_ids_a + sep     ) * [0]
												return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      str    ,			snake_case__    :      Optional[str] = None     ):






												"""simple docstring"""



												if not os.path.isdir(snake_case__     ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
																	return

												__a:    str		     =       os.path.join(
												    snake_case__    ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]     )
												__a:    Optional[Any]		     =       os.path.join(
												    snake_case__    ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]     )
												__a:    Union[str, Any]		     =       os.path.join(
												    snake_case__    ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]     )

												with open(snake_case__    ,			"""w"""    ,			encoding="""utf-8"""     ) as f:
																	f.write(json.dumps(self.encoder    ,			indent=2    ,			sort_keys=snake_case__    ,			ensure_ascii=snake_case__     ) + """\n"""     )

												with open(snake_case__    ,			"""w"""    ,			encoding="""utf-8"""     ) as f:
																	__a:    Optional[int]		     =       {v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(snake_case__    ,			indent=2    ,			sort_keys=snake_case__    ,			ensure_ascii=snake_case__     ) + """\n"""     )

												__a:    List[Any]		     =       0
												with open(snake_case__    ,			"""w"""    ,			encoding="""utf-8"""     ) as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,			key=lambda snake_case__     : kv[1]     ):
																						if index != token_index:
																											logger.warning(
																											    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																											    """ Please check that the tokenizer is not corrupted!"""     )
																											__a:    Tuple		     =       token_index
																						writer.write(""" """.join(snake_case__     ) + """\n"""     )
																						index += 1

												return src_vocab_file, tgt_vocab_file, merges_file






							def __getstate__(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Tuple		     =       self.__dict__.copy()
												__a:    Optional[Any]		     =       None
												return state






							def __setstate__(       self    :      Any    ,			snake_case__    :      str     ):






												"""simple docstring"""



												__a:    List[Any]		     =       d

												try:
																	import sacremoses
												except ImportError:
																	raise ImportError(
																	    """You need to install sacremoses to use XLMTokenizer. """
																	    """See https://pypi.org/project/sacremoses/ for installation."""     )

												__a:    Any		     =       sacremoses


"""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


lowercase		:    Dict			=       getLogger(__name__)


lowercase		:    Dict			=       'cuda' if torch.cuda.is_available() else 'cpu'


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


					'''simple docstring'''
					__a:    str		     =       Path(lowerCamelCase_			).open("""w"""						,					encoding="""utf-8"""			)
					__a:    Tuple		     =       str(lowerCamelCase_			)
					__a:    List[Any]		     =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).to(lowerCamelCase_			)
					if fpaa:
										__a:    List[str]		     =       model.half()

					__a:    Optional[int]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_			)
					logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

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


def      lowerCamelCase_     (						)  ->      str:


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


def      lowerCamelCase_     (						lowerCamelCase_=True			)  ->      Any:


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

					if args.reference_path is None:
										return {}

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

					if args.dump_args:
										scores.update(lowerCamelCase_			)
					if args.info:
										__a:    Optional[Any]		     =       args.info

					if verbose:
										print(lowerCamelCase_			)

					if args.score_path is not None:
										json.dump(lowerCamelCase_						,					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)
