




"""simple docstring"""


import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
  import bitsandbytes as bnb

from copy import deepcopy


_A													=      logging.getLogger(__name__)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = False    ,		)						->    Optional[Any]:
      lowerCAmelCase__      :			Any   =	bnb_quantization_config.load_in_abit
      lowerCAmelCase__      :			int   =	bnb_quantization_config.load_in_abit

      if load_in_abit and not is_abit_bnb_available():
            raise ImportError(
                """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
                """ make sure you have the latest version of `bitsandbytes` installed."""				)
      if load_in_abit and not is_abit_bnb_available():
            raise ValueError(
                """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
                """make sure you have the latest version of `bitsandbytes` installed."""				)

      lowerCAmelCase__      :			List[str]   =	[]
      # custom device map
      if isinstance(__UpperCAmelCase    ,		__UpperCAmelCase				) and len(device_map.keys()				) > 1:
            lowerCAmelCase__      :			str   =	[key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

      # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
      if bnb_quantization_config.skip_modules is None:
            lowerCAmelCase__      :			str   =	get_keys_to_not_convert(__UpperCAmelCase				)

      # add cpu modules to skip modules only for 4-bit modules
      if load_in_abit:
            bnb_quantization_config.skip_modules.extend(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[int]   =	bnb_quantization_config.skip_modules

      # We add the modules we want to keep in full precision
      if bnb_quantization_config.keep_in_fpaa_modules is None:
            lowerCAmelCase__      :			Any   =	[]
      lowerCAmelCase__      :			str   =	bnb_quantization_config.keep_in_fpaa_modules
      modules_to_not_convert.extend(__UpperCAmelCase				)

      # compatibility with peft
      lowerCAmelCase__      :			Tuple   =	load_in_abit
      lowerCAmelCase__      :			Optional[Any]   =	load_in_abit

      lowerCAmelCase__      :			Tuple   =	get_parameter_device(__UpperCAmelCase				)
      if model_device.type != "meta":
            # quantization of an already loaded model
            logger.warning(
                """It is not recommended to quantize a loaded model. """
                """The model should be instantiated under the `init_empty_weights` context manager."""				)
            lowerCAmelCase__      :			int   =	replace_with_bnb_layers(__UpperCAmelCase    ,		__UpperCAmelCase    ,		modules_to_not_convert=__UpperCAmelCase				)
            # convert param to the right dtype
            lowerCAmelCase__      :			int   =	bnb_quantization_config.torch_dtype
            for name, param in model.state_dict().items():
                  if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules				):
                        param.to(torch.floataa				)
                        if param.dtype != torch.floataa:
                              lowerCAmelCase__      :			int   =	name.replace(""".weight"""    ,		""""""				).replace(""".bias"""    ,		""""""				)
                              lowerCAmelCase__      :			List[Any]   =	getattr(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
                              if param is not None:
                                    param.to(torch.floataa				)
                  elif torch.is_floating_point(__UpperCAmelCase				):
                        param.to(__UpperCAmelCase				)
            if model_device.type == "cuda":
                  # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
                  model.cuda(torch.cuda.current_device()				)
                  torch.cuda.empty_cache()
            elif torch.cuda.is_available():
                  model.to(torch.cuda.current_device()				)
            else:
                  raise RuntimeError("""No GPU found. A GPU is needed for quantization."""				)
            logger.info(
                f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
                """We move the model to cuda."""				)
            return model

      elif weights_location is None:
            raise RuntimeError(
                f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """				)

      else:
            with init_empty_weights():
                  lowerCAmelCase__      :			Optional[Any]   =	replace_with_bnb_layers(
                      __UpperCAmelCase    ,		__UpperCAmelCase    ,		modules_to_not_convert=__UpperCAmelCase				)

            lowerCAmelCase__      :			Optional[int]   =	get_quantized_model_device_map(
                __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		max_memory=__UpperCAmelCase    ,		no_split_module_classes=__UpperCAmelCase    ,		)
            if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
                  lowerCAmelCase__      :			Any   =	True

            lowerCAmelCase__      :			int   =	any(x in list(device_map.values()				) for x in ["""cpu""", """disk"""]				)

            load_checkpoint_in_model(
                __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		dtype=bnb_quantization_config.torch_dtype    ,		offload_folder=__UpperCAmelCase    ,		offload_state_dict=__UpperCAmelCase    ,		keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules    ,		offload_abit_bnb=load_in_abit and offload    ,		)
            return dispatch_model(__UpperCAmelCase    ,		device_map=__UpperCAmelCase    ,		offload_dir=__UpperCAmelCase				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=None    ,		__UpperCAmelCase=None    ,		__UpperCAmelCase=None				)						->    List[str]:
      if device_map is None:
            if torch.cuda.is_available():
                  lowerCAmelCase__      :			Dict   =	{"""""": torch.cuda.current_device()}
            else:
                  raise RuntimeError("""No GPU found. A GPU is needed for quantization."""				)
            logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""				)

      if isinstance(__UpperCAmelCase    ,		__UpperCAmelCase				):
            if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
                  raise ValueError(
                      """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
                      """'sequential'."""				)

            lowerCAmelCase__      :			str   =	{}
            special_dtypes.update(
                {
                    name: bnb_quantization_config.torch_dtype
                    for name, _ in model.named_parameters()
                    if any(m in name for m in bnb_quantization_config.skip_modules				)
                }				)
            special_dtypes.update(
                {
                    name: torch.floataa
                    for name, _ in model.named_parameters()
                    if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules				)
                }				)

            lowerCAmelCase__      :			Optional[int]   =	{}
            lowerCAmelCase__      :			Any   =	special_dtypes
            lowerCAmelCase__      :			Optional[int]   =	no_split_module_classes
            lowerCAmelCase__      :			Optional[Any]   =	bnb_quantization_config.target_dtype

            # get max_memory for each device.
            if device_map != "sequential":
                  lowerCAmelCase__      :			int   =	get_balanced_memory(
                      __UpperCAmelCase    ,		low_zero=(device_map == """balanced_low_0""")    ,		max_memory=__UpperCAmelCase    ,		**__UpperCAmelCase    ,		)

            lowerCAmelCase__      :			List[str]   =	max_memory
            lowerCAmelCase__      :			Union[str, Any]   =	infer_auto_device_map(__UpperCAmelCase    ,		**__UpperCAmelCase				)

      if isinstance(__UpperCAmelCase    ,		__UpperCAmelCase				):
            # check if don't have any quantized module on the cpu
            lowerCAmelCase__      :			str   =	bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

            lowerCAmelCase__      :			Any   =	{
                key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
            }
            for device in ["cpu", "disk"]:
                  if device in device_map_without_some_modules.values():
                        if bnb_quantization_config.load_in_abit:
                              raise ValueError(
                                  """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """				)
                        else:
                              logger.info(
                                  """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""				)
            del device_map_without_some_modules
      return device_map



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=None    ,		__UpperCAmelCase=None				)						->    Dict:

      if modules_to_not_convert is None:
            lowerCAmelCase__      :			Optional[Any]   =	[]

      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	_replace_with_bnb_layers(
          __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
      if not has_been_replaced:
            logger.warning(
                """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
                """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
                """ Please double check your model architecture, or submit an issue on github if you think this is"""
                """ a bug."""				)
      return model



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=None    ,		__UpperCAmelCase=None    ,		)						->    Optional[Any]:
      lowerCAmelCase__      :			Optional[int]   =	False
      for name, module in model.named_children():
            if current_key_name is None:
                  lowerCAmelCase__      :			Any   =	[]
            current_key_name.append(__UpperCAmelCase				)
            if isinstance(__UpperCAmelCase    ,		nn.Linear				) and name not in modules_to_not_convert:
                  # Check if the current key is not in the `modules_to_not_convert`
                  lowerCAmelCase__      :			Dict   =	""".""".join(__UpperCAmelCase				)
                  lowerCAmelCase__      :			Dict   =	True
                  for key in modules_to_not_convert:
                        if (
                            (key in current_key_name_str) and (key + "." in current_key_name_str)
                        ) or key == current_key_name_str:
                              lowerCAmelCase__      :			Union[str, Any]   =	False
                              break
                  if proceed:
                        # Load bnb module with empty weight and replace ``nn.Linear` module
                        if bnb_quantization_config.load_in_abit:
                              lowerCAmelCase__      :			Optional[Any]   =	bnb.nn.LinearabitLt(
                                  module.in_features    ,		module.out_features    ,		module.bias is not None    ,		has_fpaa_weights=__UpperCAmelCase    ,		threshold=bnb_quantization_config.llm_inta_threshold    ,		)
                        elif bnb_quantization_config.load_in_abit:
                              lowerCAmelCase__      :			int   =	bnb.nn.Linearabit(
                                  module.in_features    ,		module.out_features    ,		module.bias is not None    ,		bnb_quantization_config.bnb_abit_compute_dtype    ,		compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant    ,		quant_type=bnb_quantization_config.bnb_abit_quant_type    ,		)
                        else:
                              raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""				)
                        lowerCAmelCase__      :			Union[str, Any]   =	module.weight.data
                        if module.bias is not None:
                              lowerCAmelCase__      :			Tuple   =	module.bias.data
                        bnb_module.requires_grad_(__UpperCAmelCase				)
                        setattr(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
                        lowerCAmelCase__      :			Optional[Any]   =	True
            if len(list(module.children()				)				) > 0:
                  lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	_replace_with_bnb_layers(
                      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
                  lowerCAmelCase__      :			Any   =	has_been_replaced | _has_been_replaced
            # Remove the last key for recursion
            current_key_name.pop(-1				)
      return model, has_been_replaced



def 	lowercase_				(			__UpperCAmelCase				)						->    int:
      # Create a copy of the model
      with init_empty_weights():
            lowerCAmelCase__      :			Optional[Any]   =	deepcopy(__UpperCAmelCase				)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

      lowerCAmelCase__      :			Union[str, Any]   =	find_tied_parameters(__UpperCAmelCase				)
      # For compatibility with Accelerate < 0.18
      if isinstance(__UpperCAmelCase    ,		__UpperCAmelCase				):
            lowerCAmelCase__      :			List[Any]   =	sum(list(tied_params.values()				)    ,		[]				) + list(tied_params.keys()				)
      else:
            lowerCAmelCase__      :			List[str]   =	sum(__UpperCAmelCase    ,		[]				)
      lowerCAmelCase__      :			Optional[Any]   =	len(__UpperCAmelCase				) > 0

      # Check if it is a base model
      lowerCAmelCase__      :			Tuple   =	False
      if hasattr(__UpperCAmelCase    ,		"""base_model_prefix"""				):
            lowerCAmelCase__      :			Optional[int]   =	not hasattr(__UpperCAmelCase    ,		model.base_model_prefix				)

      # Ignore this for base models (BertModel, GPT2Model, etc.)
      if (not has_tied_params) and is_base_model:
            return []

      # otherwise they have an attached head
      lowerCAmelCase__      :			int   =	list(model.named_children()				)
      lowerCAmelCase__      :			Tuple   =	[list_modules[-1][0]]

      # add last module together with tied weights
      lowerCAmelCase__      :			Optional[int]   =	set(__UpperCAmelCase				) - set(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	list(set(__UpperCAmelCase				)				) + list(__UpperCAmelCase				)

      # remove ".weight" from the keys
      lowerCAmelCase__      :			Tuple   =	[""".weight""", """.bias"""]
      lowerCAmelCase__      :			Optional[int]   =	[]
      for name in list_untouched:
            for name_to_remove in names_to_remove:
                  if name_to_remove in name:
                        lowerCAmelCase__      :			Any   =	name.replace(__UpperCAmelCase    ,		""""""				)
            filtered_module_names.append(__UpperCAmelCase				)

      return filtered_module_names



def 	lowercase_				(			__UpperCAmelCase				)						->    Dict:
      for m in model.modules():
            if isinstance(__UpperCAmelCase    ,		bnb.nn.Linearabit				):
                  return True
      return False



def 	lowercase_				(			__UpperCAmelCase				)						->    Tuple:
      return next(parameter.parameters()				).device



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Union[str, Any]:
      # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
      if fpaa_statistics is None:
            set_module_tensor_to_device(__UpperCAmelCase    ,		__UpperCAmelCase    ,		0    ,		dtype=__UpperCAmelCase    ,		value=__UpperCAmelCase				)
            lowerCAmelCase__      :			List[Any]   =	param_name
            lowerCAmelCase__      :			str   =	model
            if "." in tensor_name:
                  lowerCAmelCase__      :			Optional[int]   =	tensor_name.split("""."""				)
                  for split in splits[:-1]:
                        lowerCAmelCase__      :			Any   =	getattr(__UpperCAmelCase    ,		__UpperCAmelCase				)
                        if new_module is None:
                              raise ValueError(f"""{module} has no attribute {split}."""				)
                        lowerCAmelCase__      :			Tuple   =	new_module
                  lowerCAmelCase__      :			List[Any]   =	splits[-1]
            # offload weights
            lowerCAmelCase__      :			int   =	False
            offload_weight(module._parameters[tensor_name]    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		index=__UpperCAmelCase				)
            if hasattr(module._parameters[tensor_name]    ,		"""SCB"""				):
                  offload_weight(
                      module._parameters[tensor_name].SCB    ,		param_name.replace("""weight"""    ,		"""SCB"""				)    ,		__UpperCAmelCase    ,		index=__UpperCAmelCase    ,		)
      else:
            offload_weight(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		index=__UpperCAmelCase				)
            offload_weight(__UpperCAmelCase    ,		param_name.replace("""weight"""    ,		"""SCB"""				)    ,		__UpperCAmelCase    ,		index=__UpperCAmelCase				)

      set_module_tensor_to_device(__UpperCAmelCase    ,		__UpperCAmelCase    ,		"""meta"""    ,		dtype=__UpperCAmelCase    ,		value=torch.empty(*param.size()				)				)











"""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

_A													=      logging.get_logger(__name__)


_A													=      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_A													=      {
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

_A													=      {"""facebook/blenderbot-3B""": 1_2_8}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	lowercase_				(			)						->    Tuple:
      lowerCAmelCase__      :			int   =	(
          list(range(ord("""!"""				)    ,		ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				)    ,		ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				)    ,		ord("""ÿ"""				) + 1				)				)
      )
      lowerCAmelCase__      :			Any   =	bs[:]
      lowerCAmelCase__      :			Optional[int]   =	0
      for b in range(2**8				):
            if b not in bs:
                  bs.append(__UpperCAmelCase				)
                  cs.append(2**8 + n				)
                  n += 1
      lowerCAmelCase__      :			Dict   =	[chr(__UpperCAmelCase				) for n in cs]
      return dict(zip(__UpperCAmelCase    ,		__UpperCAmelCase				)				)



def 	lowercase_				(			__UpperCAmelCase				)						->    List[Any]:
      lowerCAmelCase__      :			List[Any]   =	set()
      lowerCAmelCase__      :			Union[str, Any]   =	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)				)
            lowerCAmelCase__      :			Optional[Any]   =	char
      return pairs


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :List[Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase    :Optional[Any]     				=     ["input_ids", "attention_mask"]






   def __init__(		self       :		Any   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Any="replace"   ,     UpperCamelCase       :		Optional[Any]="<s>"   ,     UpperCamelCase       :		Union[str, Any]="</s>"   ,     UpperCamelCase       :		Optional[int]="</s>"   ,     UpperCamelCase       :		str="<s>"   ,     UpperCamelCase       :		int="<unk>"   ,     UpperCamelCase       :		int="<pad>"   ,     UpperCamelCase       :		Dict="<mask>"   ,     UpperCamelCase       :		Optional[int]=False   ,     **UpperCamelCase       :		Optional[Any]   ,     )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else bos_token
         lowerCAmelCase__      :			int   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else eos_token
         lowerCAmelCase__      :			Dict   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else sep_token
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else cls_token
         lowerCAmelCase__      :			int   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else unk_token
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else mask_token

         super().__init__(
             errors=UpperCamelCase   ,     bos_token=UpperCamelCase   ,     eos_token=UpperCamelCase   ,     unk_token=UpperCamelCase   ,     sep_token=UpperCamelCase   ,     cls_token=UpperCamelCase   ,     pad_token=UpperCamelCase   ,     mask_token=UpperCamelCase   ,     add_prefix_space=UpperCamelCase   ,     **UpperCamelCase   ,     )

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

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






   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Union[str, Any]:

         """simple docstring"""
         return len(self.encoder   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         return dict(self.encoder   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		str   )			->						Union[str, Any]:

         """simple docstring"""
         if token in self.cache:
               return self.cache[token]
         lowerCAmelCase__      :			Union[str, Any]   =	tuple(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	get_pairs(UpperCamelCase   )

         if not pairs:
               return token

         while True:
               lowerCAmelCase__      :			List[str]   =	min(UpperCamelCase   ,     key=lambda UpperCamelCase   : self.bpe_ranks.get(UpperCamelCase   ,     float("""inf"""   )   )   )
               if bigram not in self.bpe_ranks:
                     break
               lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	bigram
               lowerCAmelCase__      :			List[str]   =	[]
               lowerCAmelCase__      :			List[str]   =	0
               while i < len(UpperCamelCase   ):
                     try:
                           lowerCAmelCase__      :			Optional[Any]   =	word.index(UpperCamelCase   ,     UpperCamelCase   )
                     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(UpperCamelCase   ) - 1 and word[i + 1] == second:
                           new_word.append(first + second   )
                           i += 2
                     else:
                           new_word.append(word[i]   )
                           i += 1
               lowerCAmelCase__      :			List[Any]   =	tuple(UpperCamelCase   )
               lowerCAmelCase__      :			Tuple   =	new_word
               if len(UpperCamelCase   ) == 1:
                     break
               else:
                     lowerCAmelCase__      :			Any   =	get_pairs(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	""" """.join(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	word
         return word






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		List[str]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	[]
         for token in re.findall(self.pat   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			List[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(UpperCamelCase   ).split(""" """   )   )
         return bpe_tokens






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         return self.encoder.get(UpperCamelCase   ,     self.encoder.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	"""""".join(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""   ,     errors=self.errors   )
         return text






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
         lowerCAmelCase__      :			Union[str, Any]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
         lowerCAmelCase__      :			int   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as f:
               f.write(json.dumps(self.encoder   ,     indent=2   ,     sort_keys=UpperCamelCase   ,     ensure_ascii=UpperCamelCase   ) + """\n"""   )

         lowerCAmelCase__      :			Optional[Any]   =	0
         with open(UpperCamelCase   ,     """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 UpperCamelCase   : 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__      :			Dict   =	token_index
                     writer.write(""" """.join(UpperCamelCase   ) + """\n"""   )
                     index += 1

         return vocab_file, merge_file






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   ,     UpperCamelCase       :		bool = False   )			->						List[int]:

         """simple docstring"""
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=UpperCamelCase   ,     token_ids_a=UpperCamelCase   ,     already_has_special_tokens=UpperCamelCase   )

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






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   )			->						List[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	[self.sep_token_id]
         lowerCAmelCase__      :			List[str]   =	[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       :		Union[str, Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Optional[int]=False   ,     **UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	kwargs.pop("""add_prefix_space"""   ,     self.add_prefix_space   )
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase   ) > 0 and not text[0].isspace()):
               lowerCAmelCase__      :			Tuple   =	""" """ + text
         return (text, kwargs)






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   )			->						Any:

         """simple docstring"""
         return token_ids_a + [self.eos_token_id]






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		"Conversation"   )			->						List[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	[]
         for is_user, text in conversation.iter_texts():
               if is_user:
                     # We need to space prefix as it's being done within blenderbot
                     inputs.append(""" """ + text   )
               else:
                     # Generated responses should contain them already.
                     inputs.append(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	"""  """.join(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	self.encode(UpperCamelCase   )
         if len(UpperCamelCase   ) > self.model_max_length:
               lowerCAmelCase__      :			List[str]   =	input_ids[-self.model_max_length :]
               logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""   )
         return input_ids





