






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_   :	List[Any]							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( ):


		'''simple docstring'''

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

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

		parser.add_argument("--seed"					,     type=lowerCAmelCase__					,     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(lowerCAmelCase__	)

		# 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(lowerCAmelCase__	)
		__A    =							args.prompt if args.prompt else input("Model prompt >>> "	)

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

		__A    =							model.generate(
		    **lowerCAmelCase__					,     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(lowerCAmelCase__	):
				print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""	)
				__A    =							generated_sequence.tolist()

				# Decode text
				__A    =							tokenizer.decode(lowerCAmelCase__					,     clean_up_tokenization_spaces=lowerCAmelCase__					,     add_special_tokens=lowerCAmelCase__	)

				# 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=lowerCAmelCase__	)	) :]
				)

				generated_sequences.append(lowerCAmelCase__	)
				print(lowerCAmelCase__	)

		return generated_sequences


if __name__ == "__main__":
					main()









import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
					from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
					snake_case_   :	Optional[int]							      =None


snake_case_   :	List[str]							      =logging.get_logger(__name__)

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

snake_case_   :	Union[str, Any]							      ={
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case_   :	int							      ={
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       int 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       str 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       int 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[int] 					= XLMRobertaTokenizer






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

				super().__init__(
				    lowercase__							, tokenizer_file=lowercase__							, bos_token=lowercase__							, eos_token=lowercase__							, sep_token=lowercase__							, cls_token=lowercase__							, unk_token=lowercase__							, pad_token=lowercase__							, mask_token=lowercase__							, **lowercase__							, )

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__A    =							[self.cls_token_id]
				__A    =							[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[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							, lowercase__							, lowercase__ = None						)				->				Tuple[str]:
				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(lowercase__						):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
						return
				__A    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

				return (out_vocab_file,)

