
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

lowercase		:    Dict			=       logging.get_logger(__name__)

lowercase		:    Dict			=       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase		:    str			=       {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

lowercase		:    int			=       {
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}


@lru_cache()
def      lowerCamelCase_     (						)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    List[str]		     =       (
					    list(range(ord("""!"""			)						,					ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)						,					ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)						,					ord("""ÿ"""			) + 1			)			)
					)
					__a:    Any		     =       bs[:]
					__a:    Union[str, Any]		     =       0
					for b in range(2**8			):
										if b not in bs:
															bs.append(lowerCamelCase_			)
															cs.append(2**8 + n			)
															n += 1
					__a:    Union[str, Any]		     =       [chr(lowerCamelCase_			) for n in cs]
					return dict(zip(lowerCamelCase_						,					lowerCamelCase_			)			)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


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




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






							def __init__(       self    :      Any    ,			snake_case__    :      int    ,			snake_case__    :      Dict    ,			snake_case__    :      Dict="replace"    ,			snake_case__    :      List[Any]="<|endoftext|>"    ,			snake_case__    :      List[Any]="<|endoftext|>"    ,			snake_case__    :      Any="<|endoftext|>"    ,			snake_case__    :      List[str]=None    ,			snake_case__    :      str=False    ,			snake_case__    :      Tuple=False    ,			**snake_case__    :      List[str]    ,			):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else bos_token
												__a:    Union[str, Any]		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else eos_token
												__a:    str		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else unk_token
												__a:    Optional[Any]		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else pad_token
												super().__init__(
												    errors=snake_case__    ,			unk_token=snake_case__    ,			bos_token=snake_case__    ,			eos_token=snake_case__    ,			pad_token=snake_case__    ,			add_prefix_space=snake_case__    ,			add_bos_token=snake_case__    ,			**snake_case__    ,			)
												__a:    int		     =       add_bos_token

												with open(snake_case__    ,			encoding="""utf-8"""     ) as vocab_handle:
																	__a:    Optional[int]		     =       json.load(snake_case__     )
												__a:    Dict		     =       {v: k for k, v in self.encoder.items()}
												__a:    List[Any]		     =       errors  # how to handle errors in decoding
												__a:    Optional[Any]		     =       bytes_to_unicode()
												__a:    Tuple		     =       {v: k for k, v in self.byte_encoder.items()}
												with open(snake_case__    ,			encoding="""utf-8"""     ) as merges_handle:
																	__a:    List[Any]		     =       merges_handle.read().split("""\n"""     )[1:-1]
												__a:    int		     =       [tuple(merge.split()     ) for merge in bpe_merges]
												__a:    List[str]		     =       dict(zip(snake_case__    ,			range(len(snake_case__     )     )     )     )
												__a:    List[str]		     =       {}
												__a:    List[str]		     =       add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												__a:    Any		     =       re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""     )






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






												"""simple docstring"""



												return len(self.encoder     )






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



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






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






												"""simple docstring"""



												if token in self.cache:
																	return self.cache[token]
												__a:    Union[str, Any]		     =       tuple(snake_case__     )
												__a:    Optional[Any]		     =       get_pairs(snake_case__     )

												if not pairs:
																	return token

												while True:
																	__a:    str		     =       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:    List[str]		     =       bigram
																	__a:    int		     =       []
																	__a:    Optional[int]		     =       0
																	while i < len(snake_case__     ):
																						try:
																											__a:    Union[str, Any]		     =       word.index(snake_case__    ,			snake_case__     )
																						except ValueError:
																											new_word.extend(word[i:]     )
																											break
																						else:
																											new_word.extend(word[i:j]     )
																											__a:    Optional[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:    Tuple		     =       tuple(snake_case__     )
																	__a:    List[Any]		     =       new_word
																	if len(snake_case__     ) == 1:
																						break
																	else:
																						__a:    Optional[int]		     =       get_pairs(snake_case__     )
												__a:    List[Any]		     =       """ """.join(snake_case__     )
												__a:    Any		     =       word
												return word






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






												"""simple docstring"""



												if self.add_bos_token:
																	__a:    str		     =       [self.bos_token_id]
												else:
																	__a:    List[Any]		     =       []

												__a:    Tuple		     =       bos_token_ids + token_ids_a

												if token_ids_a is None:
																	return output

												return output + bos_token_ids + token_ids_a






							def 				__lowercase	(       self    :      int    ,			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__     )

												if not self.add_bos_token:
																	return super().get_special_tokens_mask(
																	    token_ids_a=snake_case__    ,			token_ids_a=snake_case__    ,			already_has_special_tokens=snake_case__     )

												if token_ids_a is None:
																	return [1] + ([0] * len(snake_case__     ))
												return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     ))






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






												"""simple docstring"""



												__a:    Union[str, Any]		     =       []
												for token in re.findall(self.pat    ,			snake_case__     ):
																	__a:    Any		     =       """""".join(
																	    self.byte_encoder[b] for b in token.encode("""utf-8"""     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																	bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__     ).split(""" """     )     )
												return bpe_tokens






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






												"""simple docstring"""



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






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






												"""simple docstring"""



												return self.decoder.get(snake_case__     )






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






												"""simple docstring"""



												__a:    Any		     =       """""".join(snake_case__     )
												__a:    int		     =       bytearray([self.byte_decoder[c] for c in text]     ).decode("""utf-8"""    ,			errors=self.errors     )
												return text






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

												__a:    Optional[int]		     =       0
												with open(snake_case__    ,			"""w"""    ,			encoding="""utf-8"""     ) as writer:
																	writer.write("""#version: 0.2\n"""     )
																	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 {merge_file}: BPE merge indices are not consecutive."""
																											    """ Please check that the tokenizer is not corrupted!"""     )
																											__a:    Dict		     =       token_index
																						writer.write(""" """.join(snake_case__     ) + """\n"""     )
																						index += 1

												return vocab_file, merge_file






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Tuple    ,			snake_case__    :      Tuple=False    ,			**snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       kwargs.pop("""add_prefix_space"""    ,			self.add_prefix_space     )
												if is_split_into_words or add_prefix_space:
																	__a:    Tuple		     =       """ """ + text
												return (text, kwargs)






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






												"""simple docstring"""



												__a:    str		     =       []
												for is_user, text in conversation.iter_texts():
																	input_ids.extend(self.encode(snake_case__    ,			add_special_tokens=snake_case__     ) + [self.eos_token_id]     )
												if len(snake_case__     ) > self.model_max_length:
																	__a:    Tuple		     =       input_ids[-self.model_max_length :]
												return input_ids


"""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)
