
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowerCamelCase__        =						logging.get_logger(__name__)

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

lowerCamelCase__        =						{
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

lowerCamelCase__        =						{
    'openai-gpt': 512,
}
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			lowerCAmelCase__ : Tuple   =						text.strip()
			if not text:
						return []
			lowerCAmelCase__ : Optional[Any]   =						text.split()
			return tokens







class 	A__				(	_lowercase      ):






				def __init__(		self   : int       ,	a   : Optional[Any]=True       ,	a   : Any=None       ,	a   : Optional[int]=True       ,	a   : Any=None       ,	a   : Union[str, Any]=True       ,	):






							'''simple docstring'''







							if never_split is None:
										lowerCAmelCase__ : Any   =						[]
							lowerCAmelCase__ : Optional[Any]   =						do_lower_case
							lowerCAmelCase__ : Any   =						set(snake_case__  )
							lowerCAmelCase__ : Dict   =						tokenize_chinese_chars
							lowerCAmelCase__ : List[str]   =						strip_accents
							lowerCAmelCase__ : List[Any]   =						do_split_on_punc






				def 				_lowerCamelCase						(		self   : Any       ,	a   : Dict       ,	a   : Optional[int]=None  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						self.never_split.union(set(snake_case__  )  ) if never_split else self.never_split
							lowerCAmelCase__ : Dict   =						self._clean_text(snake_case__  )

							# This was added on November 1st, 2018 for the multilingual and Chinese
							# models. This is also applied to the English models now, but it doesn't
							# matter since the English models were not trained on any Chinese data
							# and generally don't have any Chinese data in them (there are Chinese
							# characters in the vocabulary because Wikipedia does have some Chinese
							# words in the English Wikipedia.).
							if self.tokenize_chinese_chars:
										lowerCAmelCase__ : Tuple   =						self._tokenize_chinese_chars(snake_case__  )
							# prevents treating the same character with different unicode codepoints as different characters
							lowerCAmelCase__ : Tuple   =						unicodedata.normalize('NFC'       ,	snake_case__  )
							lowerCAmelCase__ : Dict   =						whitespace_tokenize(snake_case__  )
							lowerCAmelCase__ : int   =						[]
							for token in orig_tokens:
										if token not in never_split:
													if self.do_lower_case:
																lowerCAmelCase__ : int   =						token.lower()
																if self.strip_accents is not False:
																			lowerCAmelCase__ : Optional[Any]   =						self._run_strip_accents(snake_case__  )
													elif self.strip_accents:
																lowerCAmelCase__ : Union[str, Any]   =						self._run_strip_accents(snake_case__  )
										split_tokens.extend(self._run_split_on_punc(snake_case__       ,	snake_case__  )  )

							lowerCAmelCase__ : Optional[Any]   =						whitespace_tokenize(' '.join(snake_case__  )  )
							return output_tokens






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						unicodedata.normalize('NFD'       ,	snake_case__  )
							lowerCAmelCase__ : List[str]   =						[]
							for char in text:
										lowerCAmelCase__ : List[str]   =						unicodedata.category(snake_case__  )
										if cat == "Mn":
													continue
										output.append(snake_case__  )
							return "".join(snake_case__  )






				def 				_lowerCamelCase						(		self   : int       ,	a   : Dict       ,	a   : str=None  ):






							'''simple docstring'''







							if not self.do_split_on_punc or (never_split is not None and text in never_split):
										return [text]
							lowerCAmelCase__ : Union[str, Any]   =						list(snake_case__  )
							lowerCAmelCase__ : List[str]   =						0
							lowerCAmelCase__ : Optional[Any]   =						True
							lowerCAmelCase__ : str   =						[]
							while i < len(snake_case__  ):
										lowerCAmelCase__ : int   =						chars[i]
										if _is_punctuation(snake_case__  ):
													output.append([char]  )
													lowerCAmelCase__ : int   =						True
										else:
													if start_new_word:
																output.append([]  )
													lowerCAmelCase__ : Optional[Any]   =						False
													output[-1].append(snake_case__  )
										i += 1

							return ["".join(snake_case__  ) for x in output]






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						[]
							for char in text:
										lowerCAmelCase__ : Optional[int]   =						ord(snake_case__  )
										if self._is_chinese_char(snake_case__  ):
													output.append(' '  )
													output.append(snake_case__  )
													output.append(' '  )
										else:
													output.append(snake_case__  )
							return "".join(snake_case__  )






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							if (
							    (cp >= 0X4_e00 and cp <= 0X9_fff)
							    or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
							    or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
							    or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
							    or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
							    or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
							    or (cp >= 0Xf_900 and cp <= 0Xf_aff)
							    or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
							):  #
										return True

							return False






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						[]
							for char in text:
										lowerCAmelCase__ : int   =						ord(snake_case__  )
										if cp == 0 or cp == 0Xf_ffd or _is_control(snake_case__  ):
													continue
										if _is_whitespace(snake_case__  ):
													output.append(' '  )
										else:
													output.append(snake_case__  )
							return "".join(snake_case__  )






def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						int:
			lowerCAmelCase__ : Dict   =						set()
			lowerCAmelCase__ : Optional[int]   =						word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						lowerCAmelCase__ : List[Any]   =						char
			return pairs
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Tuple:
			lowerCAmelCase__ : Tuple   =						text.replace('—'			,				'-'	)
			lowerCAmelCase__ : List[str]   =						text.replace('–'			,				'-'	)
			lowerCAmelCase__ : Tuple   =						text.replace('―'			,				'-'	)
			lowerCAmelCase__ : Optional[int]   =						text.replace('…'			,				'...'	)
			lowerCAmelCase__ : Any   =						text.replace('´'			,				'\''	)
			lowerCAmelCase__ : int   =						re.sub(r'(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'			,				r' \1 '			,				lowerCamelCase_	)
			lowerCAmelCase__ : Union[str, Any]   =						re.sub(r'\s*\n\s*'			,				' \n '			,				lowerCamelCase_	)
			lowerCAmelCase__ : Optional[int]   =						re.sub(r'[^\S\n]+'			,				' '			,				lowerCamelCase_	)
			return text.strip()







class 	A__				(	_lowercase      ):
				lowercase            =    VOCAB_FILES_NAMES
				lowercase            =    PRETRAINED_VOCAB_FILES_MAP
				lowercase            =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase            =    ['input_ids', 'attention_mask']






				def __init__(		self   : Tuple       ,	a   : Tuple       ,	a   : str       ,	a   : List[str]="<unk>"       ,	**a   : Union[str, Any]  ):






							'''simple docstring'''







							super().__init__(unk_token=snake_case__       ,	**snake_case__  )

							try:
										import ftfy
										from spacy.lang.en import English

										lowerCAmelCase__ : List[Any]   =						English()
										lowerCAmelCase__ : str   =						_nlp.tokenizer
										lowerCAmelCase__ : Tuple   =						ftfy.fix_text
							except ImportError:
										logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'  )
										lowerCAmelCase__ : Tuple   =						BasicTokenizer(do_lower_case=snake_case__  )
										lowerCAmelCase__ : Tuple   =						None

							with open(snake_case__       ,	encoding='utf-8'  ) as vocab_handle:
										lowerCAmelCase__ : Optional[Any]   =						json.load(snake_case__  )
							lowerCAmelCase__ : List[Any]   =						{v: k for k, v in self.encoder.items()}
							with open(snake_case__       ,	encoding='utf-8'  ) as merges_handle:
										lowerCAmelCase__ : Dict   =						merges_handle.read().split('\n'  )[1:-1]
							lowerCAmelCase__ : List[Any]   =						[tuple(merge.split()  ) for merge in merges]
							lowerCAmelCase__ : List[Any]   =						dict(zip(snake_case__       ,	range(len(snake_case__  )  )  )  )
							lowerCAmelCase__ : Tuple   =						{}






				@property
				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							return True






				@property
				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							return len(self.encoder  )






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Union[str, Any]       ,	a   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						tuple(token[:-1]  ) + (token[-1] + """</w>""",)
							if token in self.cache:
										return self.cache[token]
							lowerCAmelCase__ : Optional[Any]   =						get_pairs(snake_case__  )

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

							while True:
										lowerCAmelCase__ : int   =						min(snake_case__       ,	key=lambda a  : self.bpe_ranks.get(snake_case__       ,	float('inf'  )  )  )
										if bigram not in self.bpe_ranks:
													break
										lowerCAmelCase__ : Tuple   =						bigram
										lowerCAmelCase__ : int   =						[]
										lowerCAmelCase__ : Union[str, Any]   =						0
										while i < len(snake_case__  ):
													try:
																lowerCAmelCase__ : List[Any]   =						word.index(snake_case__       ,	snake_case__  )
													except ValueError:
																new_word.extend(word[i:]  )
																break
													else:
																new_word.extend(word[i:j]  )
																lowerCAmelCase__ : List[str]   =						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
										lowerCAmelCase__ : Any   =						tuple(snake_case__  )
										lowerCAmelCase__ : Optional[Any]   =						new_word
										if len(snake_case__  ) == 1:
													break
										else:
													lowerCAmelCase__ : Union[str, Any]   =						get_pairs(snake_case__  )
							lowerCAmelCase__ : List[str]   =						""" """.join(snake_case__  )
							if word == "\n  </w>":
										lowerCAmelCase__ : Union[str, Any]   =						"""\n</w>"""
							lowerCAmelCase__ : Any   =						word
							return word






				def 				_lowerCamelCase						(		self   : Any       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						[]
							if self.fix_text is None:
										# Using BERT's BasicTokenizer
										lowerCAmelCase__ : List[Any]   =						self.nlp.tokenize(snake_case__  )
										for token in text:
													split_tokens.extend(list(self.bpe(snake_case__  ).split(' '  )  )  )
							else:
										# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
										lowerCAmelCase__ : Optional[int]   =						self.nlp(text_standardize(self.fix_text(snake_case__  )  )  )
										for token in text:
													split_tokens.extend(list(self.bpe(token.text.lower()  ).split(' '  )  )  )
							return split_tokens






				def 				_lowerCamelCase						(		self   : Union[str, Any]       ,	a   : int  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : int       ,	a   : Optional[int]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						"""""".join(snake_case__  ).replace('</w>'       ,	' '  ).strip()
							return out_string






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : str       ,	a   : Optional[str] = None  ):






							'''simple docstring'''







							if not os.path.isdir(snake_case__  ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
										return
							lowerCAmelCase__ : List[Any]   =						os.path.join(
							    snake_case__       ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
							lowerCAmelCase__ : List[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'  )

							lowerCAmelCase__ : 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 a  : 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!'  )
																lowerCAmelCase__ : Union[str, Any]   =						token_index
													writer.write(' '.join(snake_case__  ) + '\n'  )
													index += 1

							return vocab_file, merge_file

"""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)
