





"""simple docstring"""
import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_snake_case	:						Tuple					=					logging.getLogger(__name__)






def    A__							(     UpperCamelCase			):
							np.random.seed(args.seed			)
							torch.manual_seed(args.seed			)
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed			)






def    A__							(     ):
							A						=    argparse.ArgumentParser()
							parser.add_argument(
							    "--model_name_or_path"   ,    default=UpperCamelCase   ,    type=UpperCamelCase   ,    required=UpperCamelCase   ,    )
							parser.add_argument("--prompt"   ,    type=UpperCamelCase   ,    default=""			)
							parser.add_argument("--length"   ,    type=UpperCamelCase   ,    default=20			)
							parser.add_argument("--stop_token"   ,    type=UpperCamelCase   ,    default=UpperCamelCase   ,    help="Token at which text generation is stopped"			)
							parser.add_argument(
							    "--temperature"   ,    type=UpperCamelCase   ,    default=1.0   ,    help="temperature of 1.0 has no effect, lower tend toward greedy sampling"   ,    )
							parser.add_argument(
							    "--repetition_penalty"   ,    type=UpperCamelCase   ,    default=1.0   ,    help="primarily useful for CTRL model; in that case, use 1.2"			)
							parser.add_argument("--k"   ,    type=UpperCamelCase   ,    default=0			)
							parser.add_argument("--penalty_alpha"   ,    type=UpperCamelCase   ,    default=0.0			)
							parser.add_argument("--p"   ,    type=UpperCamelCase   ,    default=0.9			)

							parser.add_argument("--prefix"   ,    type=UpperCamelCase   ,    default=""   ,    help="Text added prior to input."			)
							parser.add_argument("--padding_text"   ,    type=UpperCamelCase   ,    default=""   ,    help="Deprecated, the use of `--prefix` is preferred."			)
							parser.add_argument("--xlm_language"   ,    type=UpperCamelCase   ,    default=""   ,    help="Optional language when used with the XLM model."			)

							parser.add_argument("--seed"   ,    type=UpperCamelCase   ,    default=42   ,    help="random seed for initialization"			)
							parser.add_argument("--no_cuda"   ,    action="store_true"   ,    help="Avoid using CUDA when available"			)
							parser.add_argument(
							    "--fp16"   ,    action="store_true"   ,    help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   ,    )
							A						=    parser.parse_args()

							A						=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"			)
							A						=    0 if args.no_cuda else torch.cuda.device_count()

							logger.warning(F"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"			)

							set_seed(UpperCamelCase			)

							# Initialize the model and tokenizer
							A						=    AutoTokenizer.from_pretrained(args.model_name_or_path			)
							A						=    AutoModelForCausalLM.from_pretrained(args.model_name_or_path			)

							# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
							# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
							model.to(args.device			)

							if args.fpaa:
														model.half()

							logger.info(UpperCamelCase			)
							A						=    args.prompt if args.prompt else input("Model prompt >>> "			)

							A						=    tokenizer(UpperCamelCase   ,    return_tensors="pt"   ,    add_special_tokens=UpperCamelCase			)
							A						=    {key: value.to(args.device			) for key, value in inputs.items()}

							A						=    model.generate(
							    **UpperCamelCase   ,    max_length=args.length + len(inputs["input_ids"][0]			)   ,    penalty_alpha=args.penalty_alpha   ,    top_k=args.k   ,    )

							A						=    []
							for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase			):
														print(F"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="			)
														A						=    generated_sequence.tolist()

														# Decode text
														A						=    tokenizer.decode(UpperCamelCase   ,    clean_up_tokenization_spaces=UpperCamelCase   ,    add_special_tokens=UpperCamelCase			)

														# Remove all text after the stop token
														A						=    text[: text.find(args.stop_token			) if args.stop_token else None]

														# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
														A						=    (
														    prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]   ,    clean_up_tokenization_spaces=UpperCamelCase			)			) :]
														)

														generated_sequences.append(UpperCamelCase			)
														print(UpperCamelCase			)

							return generated_sequences


if __name__ == "__main__":
		main()









"""simple docstring"""
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_mbartaa import MBartaaTokenizer
else:
		_snake_case	:						Dict					=					None


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[Any]					=					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case	:						str					=					{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case	:						Union[str, Any]					=					{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
_snake_case	:						int					=					['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', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				VOCAB_FILES_NAMES
					UpperCamelCase						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase						=				PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase						=				['''input_ids''', '''attention_mask''']
					UpperCamelCase						=				MBartaaTokenizer

					UpperCamelCase						=				[]
					UpperCamelCase						=				[]


					def __init__(   self     :Any	,			__UpperCamelCase     :str=None	,			__UpperCamelCase     :Any=None	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :Union[str, Any]=None	,			__UpperCamelCase     :Optional[Any]="</s>"	,			__UpperCamelCase     :Tuple="</s>"	,			__UpperCamelCase     :Any="<s>"	,			__UpperCamelCase     :Optional[int]="<unk>"	,			__UpperCamelCase     :str="<pad>"	,			__UpperCamelCase     :int="<mask>"	,			**__UpperCamelCase     :List[str]	,			):
												# Mask token behave like a normal word, i.e. include the space before it
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else mask_token

												A						=    kwargs.get("additional_special_tokens"	,			[]						)
												kwargs["additional_special_tokens"] += [
												    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
												]

												super().__init__(
												    __UpperCamelCase	,			src_lang=__UpperCamelCase	,			tgt_lang=__UpperCamelCase	,			tokenizer_file=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			sep_token=__UpperCamelCase	,			cls_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			mask_token=__UpperCamelCase	,			**__UpperCamelCase	,			)

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

												A						=    {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												A						=    src_lang if src_lang is not None else "en_XX"
												A						=    tgt_lang
												A						=    self.lang_code_to_id[self._src_lang]
												self.set_src_lang_special_tokens(self._src_lang						)


					@property
					def       lowerCamelCase			(   self     :int						):
												return self._src_lang


					@src_lang.setter
					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    new_src_lang
												self.set_src_lang_special_tokens(self._src_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :Optional[List[int]] = None						):
												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     :Optional[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :str = "en_XX"	,			__UpperCamelCase     :Optional[List[str]] = None	,			__UpperCamelCase     :str = "ro_RO"	,			**__UpperCamelCase     :Tuple	,			):
												A						=    src_lang
												A						=    tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str						):
												return self.set_src_lang_special_tokens(self.src_lang						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return self.set_tgt_lang_special_tokens(self.tgt_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

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

												A						=    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     :Optional[Any]	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

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

												A						=    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     :Optional[Any]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str]	,			__UpperCamelCase     :Optional[str]	,			**__UpperCamelCase     :Union[str, Any]						):
												if src_lang is None or tgt_lang is None:
																			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
												A						=    src_lang
												A						=    self(__UpperCamelCase	,			add_special_tokens=__UpperCamelCase	,			return_tensors=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    tgt_lang_id
												return inputs


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str] = None						):
												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(__UpperCamelCase						):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
																			return
												A						=    os.path.join(
												    __UpperCamelCase	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

												return (out_vocab_file,)


