



'''simple docstring'''



import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowercase       :						List[str]						=logging.getLogger(__name__)

_lowercase       :						int						=int(1_0000)  # Hardcoded max length to avoid infinite loop

_lowercase       :						Optional[int]						={
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_lowercase       :						List[Any]						="In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"


def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Optional[Any]:
				np.random.seed(args.seed			)
				torch.manual_seed(args.seed			)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Optional[int]			)		->						Dict:
				if args.temperature > 0.7:
								logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''			)

				snake_case__      :   str      =    tokenizer.encode(UpperCamelCase__   ,				add_special_tokens=UpperCamelCase__			)
				if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()			):
								logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''			)
				return prompt_text


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:int			)		->						List[Any]:
				# kwargs = {"language": None, "mask_token_id": None}

				# Set the language
				snake_case__      :   Union[str, Any]      =    hasattr(model.config   ,				'''use_lang_emb'''			) and model.config.use_lang_emb
				if hasattr(model.config   ,				'''lang2id'''			) and use_lang_emb:
								snake_case__      :   List[str]      =    model.config.langaid.keys()
								if args.xlm_language in available_languages:
												snake_case__      :   Tuple      =    args.xlm_language
								else:
												snake_case__      :   int      =    None
												while language not in available_languages:
																snake_case__      :   Dict      =    input('''Using XLM. Select language in ''' + str(list(UpperCamelCase__			)			) + ''' >>> '''			)

								snake_case__      :   Tuple      =    model.config.langaid[language]
								# kwargs["language"] = tokenizer.lang2id[language]

				# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
				# XLM masked-language modeling (MLM) models need masked token
				# is_xlm_mlm = "mlm" in args.model_name_or_path
				# if is_xlm_mlm:
				#     kwargs["mask_token_id"] = tokenizer.mask_token_id

				return prompt_text


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Optional[int]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				snake_case__      :   str      =    prefix + prompt_text
				return prompt_text


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]   ,				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:Dict			)		->						str:
				snake_case__      :   Dict      =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				snake_case__      :   int      =    prefix + prompt_text
				return prompt_text


_lowercase       :						List[str]						={
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Union[str, Any]			)		->						str:
				if length < 0 and max_sequence_length > 0:
								snake_case__      :   int      =    max_sequence_length
				elif 0 < max_sequence_length < length:
								snake_case__      :   str      =    max_sequence_length  # No generation bigger than model size
				elif length < 0:
								snake_case__      :   Tuple      =    MAX_LENGTH  # avoid infinite loop
				return length


def 							__UpperCAmelCase				(				UpperCamelCase__				:Any			)		->						List[str]:
				snake_case__      :   Dict      =    None
				if hasattr(UpperCamelCase__   ,				'''hidden_size'''			):
								snake_case__      :   Optional[Any]      =    model_config.hidden_size
				elif hasattr(UpperCamelCase__   ,				'''n_embed'''			):
								snake_case__      :   int      =    model_config.n_embed
				elif hasattr(UpperCamelCase__   ,				'''n_embd'''			):
								snake_case__      :   str      =    model_config.n_embd

				snake_case__      :   List[str]      =    None
				if hasattr(UpperCamelCase__   ,				'''num_attention_heads'''			):
								snake_case__      :   int      =    model_config.num_attention_heads
				elif hasattr(UpperCamelCase__   ,				'''n_head'''			):
								snake_case__      :   List[Any]      =    model_config.n_head

				if embedding_size is None or num_head is None or num_head == 0:
								raise ValueError('''Check the model config'''			)

				snake_case__      :   Union[str, Any]      =    int(embedding_size / num_head			)
				if hasattr(UpperCamelCase__   ,				'''n_layer'''			):
								snake_case__      :   Any      =    model_config.n_layer
				elif hasattr(UpperCamelCase__   ,				'''num_hidden_layers'''			):
								snake_case__      :   Optional[int]      =    model_config.num_hidden_layers
				else:
								raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''			)

				return num_layer, num_head, num_embedding_size_per_head


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]   ,				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple			)		->						int:
				snake_case__  ,      snake_case__  ,      snake_case__      :   Dict      =    sparse_model_config(model.config			)
				if model.config.model_type == "bloom":
								snake_case__      :   Union[str, Any]      =    tuple(
								    (
								        torch.empty(int(num_attention_heads * batch_size			)   ,				UpperCamelCase__   ,				UpperCamelCase__			)
								        .to(model.dtype			)
								        .to(model.device			),
								        torch.empty(int(num_attention_heads * batch_size			)   ,				UpperCamelCase__   ,				UpperCamelCase__			)
								        .to(model.dtype			)
								        .to(model.device			),
								    )
								    for _ in range(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    tuple(
								    (
								        torch.empty(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)
								        .to(model.dtype			)
								        .to(model.device			),
								        torch.empty(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)
								        .to(model.dtype			)
								        .to(model.device			),
								    )
								    for _ in range(UpperCamelCase__			)			)
				return past_key_values


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:int			)		->						Optional[Any]:
				snake_case__      :   int      =    len(UpperCamelCase__			)
				snake_case__      :   int      =    tokenizer.batch_encode_plus(UpperCamelCase__   ,				return_tensors='''pt'''			)
				snake_case__      :   Optional[Any]      =    dummy_input.to(model.device			)
				if model.config.use_cache:
								snake_case__      :   Tuple      =    generate_past_key_values(UpperCamelCase__   ,				UpperCamelCase__   ,				1			)
				snake_case__      :   Optional[int]      =    torch.cat(
				    [
				        torch.zeros(dummy_input['''attention_mask'''].shape[0]   ,				1			)
				        .to(dummy_input['''attention_mask'''].dtype			)
				        .to(model.device			),
				        dummy_input['''attention_mask'''],
				    ]   ,				-1   ,				)
				return dummy_input






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ('_optimized', '_default')



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    optimized
							snake_case__      :   Optional[Any]      =    default



			def __call__(     self		:						Dict    ,			*__UpperCamelCase		:						Optional[int]    ,			**__UpperCamelCase		:						List[str]			)      ->      Optional[int]:
							"""simple docstring"""





							if kwargs["past_key_values"] is None and self._default.config.use_cache:
											snake_case__      :   Any      =    generate_past_key_values(self._default    ,			kwargs['''input_ids'''].shape[0]    ,			0			)
							kwargs.pop('''position_ids'''    ,			__UpperCamelCase			)
							for k in list(kwargs.keys()			):
											if kwargs[k] is None or isinstance(kwargs[k]    ,			__UpperCamelCase			):
															kwargs.pop(__UpperCamelCase			)
							snake_case__      :   Optional[Any]      =    self._optimized(**__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    outputs[0]
							snake_case__      :   Any      =    outputs[1]
							snake_case__      :   Dict      =    CausalLMOutputWithPast(
							    loss=__UpperCamelCase    ,			logits=__UpperCamelCase    ,			past_key_values=__UpperCamelCase    ,			hidden_states=__UpperCamelCase    ,			attentions=__UpperCamelCase    ,			)
							return fixed_output



			def __getattr__(     self		:						Tuple    ,			__UpperCamelCase		:						str			)      ->      Dict:
							"""simple docstring"""





							return getattr(self._default    ,			__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						str=None    ,			__UpperCamelCase		:						int=None    ,			**__UpperCamelCase		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							return self._default.prepare_inputs_for_generation(
							    __UpperCamelCase    ,			past_key_values=__UpperCamelCase    ,			inputs_embeds=__UpperCamelCase    ,			use_cache=__UpperCamelCase    ,			**__UpperCamelCase			)







			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						Tuple[Tuple[torch.Tensor]]    ,			__UpperCamelCase		:						torch.Tensor			)      ->      Tuple[Tuple[torch.Tensor]]:
							"""simple docstring"""





							return self._default._reorder_cache(__UpperCamelCase    ,			__UpperCamelCase			)



def 							__UpperCAmelCase				(				)		->						str:
				snake_case__      :   Union[str, Any]      =    argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_type'''   ,				default=UpperCamelCase__   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__   ,				help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()			)   ,				)
				parser.add_argument(
				    '''--model_name_or_path'''   ,				default=UpperCamelCase__   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__   ,				help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()			)   ,				)

				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('''--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('''--num_return_sequences'''   ,				type=UpperCamelCase__   ,				default=1   ,				help='''The number of samples to generate.'''			)
				parser.add_argument(
				    '''--fp16'''   ,				action='''store_true'''   ,				help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,				)
				parser.add_argument('''--jit'''   ,				action='''store_true'''   ,				help='''Whether or not to use jit trace to accelerate inference'''			)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()

				snake_case__      :   Any      =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
				snake_case__      :   Union[str, Any]      =    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
				try:
								snake_case__      :   List[str]      =    args.model_type.lower()
								snake_case__  ,      snake_case__      :   List[Any]      =    MODEL_CLASSES[args.model_type]
				except KeyError:
								raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''			)

				snake_case__      :   int      =    tokenizer_class.from_pretrained(args.model_name_or_path			)
				if tokenizer.pad_token is None:
								snake_case__      :   Union[str, Any]      =    tokenizer.eos_token
				snake_case__      :   str      =    model_class.from_pretrained(args.model_name_or_path			)
				model.to(args.device			)

				if args.fpaa:
								model.half()
				snake_case__      :   Any      =    getattr(model.config   ,				'''max_position_embeddings'''   ,				0			)
				snake_case__      :   List[Any]      =    adjust_length_to_model(args.length   ,				max_sequence_length=UpperCamelCase__			)
				logger.info(UpperCamelCase__			)

				snake_case__      :   Optional[Any]      =    args.prompt if args.prompt else input('''Model prompt >>> '''			)

				# Different models need different input formatting and/or extra arguments
				snake_case__      :   str      =    args.model_type in PREPROCESSING_FUNCTIONS.keys()
				if requires_preprocessing:
								snake_case__      :   str      =    PREPROCESSING_FUNCTIONS.get(args.model_type			)
								snake_case__      :   Dict      =    prepare_input(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

								if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												snake_case__      :   List[str]      =    {'''add_space_before_punct_symbol''': True}
								else:
												snake_case__      :   Dict      =    {}

								snake_case__      :   Dict      =    tokenizer.encode(
								    UpperCamelCase__   ,				add_special_tokens=UpperCamelCase__   ,				return_tensors='''pt'''   ,				**UpperCamelCase__			)
				else:
								snake_case__      :   Dict      =    args.prefix if args.prefix else args.padding_text
								snake_case__      :   Union[str, Any]      =    tokenizer.encode(prefix + prompt_text   ,				add_special_tokens=UpperCamelCase__   ,				return_tensors='''pt'''			)
				snake_case__      :   List[Any]      =    encoded_prompt.to(args.device			)

				if encoded_prompt.size()[-1] == 0:
								snake_case__      :   int      =    None
				else:
								snake_case__      :   List[Any]      =    encoded_prompt

				if args.jit:
								snake_case__      :   Any      =    ['''enable jit''']
								snake_case__      :   Optional[int]      =    prepare_jit_inputs(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)
								torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__			)
								snake_case__      :   Any      =    False
								if hasattr(UpperCamelCase__   ,				'''forward'''			):
												snake_case__      :   Any      =    inspect.signature(model.forward			)
								else:
												snake_case__      :   Dict      =    inspect.signature(model.__call__			)
								snake_case__      :   Tuple      =    tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__   ,				UpperCamelCase__			) is not None			)
								snake_case__      :   Tuple      =    torch.jit.trace(UpperCamelCase__   ,				UpperCamelCase__   ,				strict=UpperCamelCase__			)
								snake_case__      :   List[Any]      =    torch.jit.freeze(traced_model.eval()			)
								traced_model(*UpperCamelCase__			)
								traced_model(*UpperCamelCase__			)

								snake_case__      :   Optional[Any]      =    _ModelFallbackWrapper(UpperCamelCase__   ,				UpperCamelCase__			)

				snake_case__      :   Union[str, Any]      =    model.generate(
				    input_ids=UpperCamelCase__   ,				max_length=args.length + len(encoded_prompt[0]			)   ,				temperature=args.temperature   ,				top_k=args.k   ,				top_p=args.p   ,				repetition_penalty=args.repetition_penalty   ,				do_sample=UpperCamelCase__   ,				num_return_sequences=args.num_return_sequences   ,				)

				# Remove the batch dimension when returning multiple sequences
				if len(output_sequences.shape			) > 2:
								output_sequences.squeeze_()

				snake_case__      :   List[str]      =    []

				for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__			):
								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''			)
								snake_case__      :   List[Any]      =    generated_sequence.tolist()

								# Decode text
								snake_case__      :   Tuple      =    tokenizer.decode(UpperCamelCase__   ,				clean_up_tokenization_spaces=UpperCamelCase__			)

								# Remove all text after the stop token
								snake_case__      :   Optional[Any]      =    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
								snake_case__      :   str      =    (
								    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]   ,				clean_up_tokenization_spaces=UpperCamelCase__			)			) :]
								)

								generated_sequences.append(UpperCamelCase__			)
								print(UpperCamelCase__			)

				return generated_sequences


if __name__ == "__main__":
				main()






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
	if is_torch_available():
		import torch
	if is_tf_available():
		import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__a							  =						logging.get_logger(__name__)

__a							  =						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

__a							  =						{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

__a							  =						{
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}

@lru_cache()
def    UpperCamelCase_ (	)     ->List[Any]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Union[str, Any]:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask"]







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





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=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				=add_bos_token

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

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







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





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







	def        _a					(      self				:			str							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=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				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=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(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Optional[Any]							,			snake_case__				:			Any							,			snake_case__				:			Optional[Any]=None ):
			"""simple docstring"""





			if self.add_bos_token:
					A				=[self.bos_token_id]
			else:
					A				=[]

			A				=bos_token_ids + token_ids_a

			if token_ids_a is None:
					return output

			return output + bos_token_ids + token_ids_a







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".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        _a					(      self				:			Tuple							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





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







	def        _a					(      self				:			str							,			snake_case__				:			Dict ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





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







	def        _a					(      self				:			List[str]							,			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				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=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				=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				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			str							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[int]=False							,			**snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if is_split_into_words or add_prefix_space:
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			str							,			snake_case__				:			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,			snake_case__				:			bool = False							,			snake_case__				:			bool = None							,			snake_case__				:			Optional[List[str]] = None							,			**snake_case__				:			Union[str, Any]							,			):
			"""simple docstring"""





			A				=super()._decode(
			    token_ids=snake_case__							,			skip_special_tokens=snake_case__							,			clean_up_tokenization_spaces=snake_case__							,			**snake_case__							,			)

			if truncate_before_pattern is not None and len(snake_case__ ) > 0:
					A				=self.truncate(snake_case__							,			snake_case__ )

			return decoded_text







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any]							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			def find_re(snake_case__				:			Dict							,			snake_case__				:			Tuple							,			snake_case__				:			int ):
					A				=pattern.search(snake_case__							,			snake_case__ )
					return m.start() if m else -1

			A				=[re.compile(snake_case__							,			re.MULTILINE ) for pattern in truncate_before_pattern]

			A				=list(re.finditer("^print"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: prints[1].start()]

			A				=list(re.finditer("^def"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: defs[1].start()]

			A				=0

			A				=[
			    pos for pos in [find_re(snake_case__							,			snake_case__							,			snake_case__ ) for terminal in terminals] if pos != -1
			]

			if len(snake_case__ ) > 0:
					return completion[: min(snake_case__ )]
			else:
					return completion




