





"""simple docstring"""







import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


a			:    Union[str, Any]				 =		logging.get_logger(__name__)





def 						lowercase__(A ,		A ,		A ,		A=False						)    ->int:




						"""simple docstring"""



						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
												    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
												    " instructions."						)
												raise

						if not is_sharded:
												lowercase__    :				int=	os.path.abspath(__lowerCAmelCase						)
												logger.info(f'''Loading PyTorch weights from {pt_path}'''						)

												lowercase__    :				List[Any]=	torch.load(__lowerCAmelCase ,		map_location="cpu"						)
												logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters.'''						)

												lowercase__    :				List[Any]=	convert_pytorch_state_dict_to_flax(__lowerCAmelCase ,		__lowerCAmelCase						)
						else:
												# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
												lowercase__    :				Union[str, Any]=	convert_pytorch_sharded_state_dict_to_flax(__lowerCAmelCase ,		__lowerCAmelCase						)
						return flax_state_dict





def 						lowercase__(A ,		A ,		A ,		A ,		)    ->(Tuple[str], np.ndarray):




						"""simple docstring"""



						def is_key_or_prefix_key_in_dict(A						) -> bool:
												return len(set(__lowerCAmelCase						) & {key, (model_prefix,) + key}						) > 0

						# layer norm
						lowercase__    :				Optional[Any]=	pt_tuple_key[:-1] + ("scale",)
						if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCAmelCase						):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer mean
						lowercase__    :				Dict=	pt_tuple_key[:-1] + ("mean",)
						if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase						):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer var
						lowercase__    :				Optional[int]=	pt_tuple_key[:-1] + ("var",)
						if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase						):
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						lowercase__    :				Any=	pt_tuple_key[:-1] + ("embedding",)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCAmelCase						):
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowercase__    :				List[Any]=	pt_tuple_key[:-1] + ("kernel",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCAmelCase						):
												lowercase__    :				List[str]=	pt_tensor.transpose(2 ,		3 ,		1 ,		0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowercase__    :				Tuple=	pt_tuple_key[:-1] + ("kernel",)
						if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase						):
												lowercase__    :				Optional[Any]=	pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowercase__    :				int=	pt_tuple_key[:-1] + ("weight",)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowercase__    :				List[str]=	pt_tuple_key[:-1] + ("bias",)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						lowercase__    :				Optional[int]=	None
						if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
												lowercase__    :				int=	pt_tuple_key[-2] + "_g"
						elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
												lowercase__    :				Any=	pt_tuple_key[-2] + "_v"
						if name is not None:
												lowercase__    :				str=	pt_tuple_key[:-3] + (name,)
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor





def 						lowercase__(A ,		A						)    ->List[Any]:




						"""simple docstring"""



						lowercase__    :				List[Any]=	{k: v.numpy() for k, v in pt_state_dict.items()}

						lowercase__    :				Dict=	flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												lowercase__    :				Any=	flax_model.params["params"]
						else:
												lowercase__    :				Tuple=	flax_model.params
						lowercase__    :				Tuple=	flatten_dict(__lowerCAmelCase						)

						# add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
												lowercase__    :				List[str]=	flatten_dict(flax_model.params["batch_stats"]						)
												random_flax_state_dict.update(__lowerCAmelCase						)

						lowercase__    :				List[str]=	{}

						lowercase__    :				Dict=	(model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("."						)[0] for k in pt_state_dict.keys()}
						)
						lowercase__    :				Any=	(model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("."						)[0] for k in pt_state_dict.keys()}
						)

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowercase__    :				Dict=	tuple(pt_key.split("."						)						)

												# remove base model prefix if necessary
												lowercase__    :				int=	pt_tuple_key[0] == model_prefix
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		lowercase__    :				int=	pt_tuple_key[1:]

												# Correctly rename weight parameters
												lowercase__,     lowercase__    :				Tuple=	rename_key_and_reshape_tensor(
												    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase						)

												# add model prefix if necessary
												lowercase__    :				Dict=	(model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		lowercase__    :				Dict=	(model_prefix,) + flax_key

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # add batch stats if the model contains batchnorm layers
												if "batch_stats" in flax_model.params:
																		if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																								lowercase__    :				Union[str, Any]=	jnp.asarray(__lowerCAmelCase						)
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(__lowerCAmelCase ,		__lowerCAmelCase						)
																								continue

																		# also add unexpected weight so that warning is thrown
																		lowercase__    :				int=	jnp.asarray(__lowerCAmelCase						)

												else:
																		# also add unexpected weight so that warning is thrown
																		lowercase__    :				Any=	jnp.asarray(__lowerCAmelCase						)

						return unflatten_dict(__lowerCAmelCase						)





def 						lowercase__(A ,		A						)    ->str:




						"""simple docstring"""



						import torch

						# Load the index
						lowercase__    :				Dict=	{}
						for shard_file in shard_filenames:
												# load using msgpack utils
												lowercase__    :				Any=	torch.load(__lowerCAmelCase						)
												lowercase__    :				Optional[Any]=	{k: v.numpy() for k, v in pt_state_dict.items()}

												lowercase__    :				Optional[int]=	flax_model.base_model_prefix

												# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
												if "batch_stats" in flax_model.params:
																		lowercase__    :				Optional[Any]=	flax_model.params["params"]

																		lowercase__    :				str=	flatten_dict(__lowerCAmelCase						)
																		random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]						)						)
												else:
																		lowercase__    :				str=	flax_model.params
																		lowercase__    :				Tuple=	flatten_dict(__lowerCAmelCase						)

												lowercase__    :				Union[str, Any]=	(model_prefix not in flax_model_params) and (
												    model_prefix in {k.split("."						)[0] for k in pt_state_dict.keys()}
												)
												lowercase__    :				List[Any]=	(model_prefix in flax_model_params) and (
												    model_prefix not in {k.split("."						)[0] for k in pt_state_dict.keys()}
												)
												# Need to change some parameters name to match Flax names
												for pt_key, pt_tensor in pt_state_dict.items():
																		lowercase__    :				int=	tuple(pt_key.split("."						)						)

																		# remove base model prefix if necessary
																		lowercase__    :				Dict=	pt_tuple_key[0] == model_prefix
																		if load_model_with_head_into_base_model and has_base_model_prefix:
																								lowercase__    :				int=	pt_tuple_key[1:]

																		# Correctly rename weight parameters
																		lowercase__,     lowercase__    :				Dict=	rename_key_and_reshape_tensor(
																		    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase						)
																		# add model prefix if necessary
																		lowercase__    :				List[Any]=	(model_prefix,) + flax_key in random_flax_state_dict
																		if load_base_model_into_model_with_head and require_base_model_prefix:
																								lowercase__    :				Tuple=	(model_prefix,) + flax_key

																		if flax_key in random_flax_state_dict:
																								if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																														raise ValueError(
																														    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																														    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

																								# add batch stats if the model contains batchnorm layers
																		if "batch_stats" in flax_model.params:
																								if "mean" in flax_key[-1]:
																														lowercase__    :				Dict=	jnp.asarray(__lowerCAmelCase						)
																														continue
																								if "var" in flax_key[-1]:
																														lowercase__    :				int=	jnp.asarray(__lowerCAmelCase						)
																														continue
																								# remove num_batches_tracked key
																								if "num_batches_tracked" in flax_key[-1]:
																														flax_state_dict.pop(__lowerCAmelCase ,		__lowerCAmelCase						)
																														continue

																								# also add unexpected weight so that warning is thrown
																								lowercase__    :				Optional[int]=	jnp.asarray(__lowerCAmelCase						)

																		else:
																								# also add unexpected weight so that warning is thrown
																								lowercase__    :				Union[str, Any]=	jnp.asarray(__lowerCAmelCase						)
						return unflatten_dict(__lowerCAmelCase						)





def 						lowercase__(A ,		A						)    ->str:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	os.path.abspath(__lowerCAmelCase						)
						logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''						)

						# import correct flax class
						lowercase__    :				Optional[Any]=	getattr(__lowerCAmelCase ,		"Flax" + model.__class__.__name__						)

						# load flax weight dict
						with open(__lowerCAmelCase ,		"rb"						) as state_f:
												try:
																		lowercase__    :				Dict=	from_bytes(__lowerCAmelCase ,		state_f.read()						)
												except UnpicklingError:
																		raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''						)

						return load_flax_weights_in_pytorch_model(__lowerCAmelCase ,		__lowerCAmelCase						)







def 						lowercase__(A ,		A						)    ->int:




						"""simple docstring"""



						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
												    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
												    " instructions."						)
												raise

						# check if we have bf16 weights
						lowercase__    :				str=	flatten_dict(jax.tree_util.tree_map(lambda A						: x.dtype == jnp.bfloataa ,		__lowerCAmelCase						)						).values()
						if any(__lowerCAmelCase						):
												# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
												# and bf16 is not fully supported in PT yet.
												logger.warning(
												    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
												    "before loading those in PyTorch model."						)
												lowercase__    :				str=	jax.tree_util.tree_map(
												    lambda A						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params ,		__lowerCAmelCase						)

						lowercase__    :				Any=	flatten_dict(__lowerCAmelCase						)
						lowercase__    :				Optional[int]=	pt_model.state_dict()

						lowercase__    :				Tuple=	(pt_model.base_model_prefix in flax_state) and (
						    pt_model.base_model_prefix not in {k.split("."						)[0] for k in pt_model_dict.keys()}
						)
						lowercase__    :				List[Any]=	(pt_model.base_model_prefix not in flax_state) and (
						    pt_model.base_model_prefix in {k.split("."						)[0] for k in pt_model_dict.keys()}
						)

						# keep track of unexpected & missing keys
						lowercase__    :				Union[str, Any]=	[]
						lowercase__    :				Any=	set(pt_model_dict.keys()						)

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												lowercase__    :				Dict=	flax_key_tuple[0] == pt_model.base_model_prefix
												lowercase__    :				Tuple=	".".join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

												# adapt flax_key to prepare for loading from/to base model only
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		lowercase__    :				Optional[int]=	flax_key_tuple[1:]
												elif load_base_model_into_model_with_head and require_base_model_prefix:
																		lowercase__    :				str=	(pt_model.base_model_prefix,) + flax_key_tuple

												# rename flax weights to PyTorch format
												if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCAmelCase						) not in pt_model_dict:
																		# conv layer
																		lowercase__    :				Optional[Any]=	flax_key_tuple[:-1] + ("weight",)
																		lowercase__    :				Dict=	jnp.transpose(__lowerCAmelCase ,		(3, 2, 0, 1)						)
												elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase						) not in pt_model_dict:
																		# linear layer
																		lowercase__    :				Optional[int]=	flax_key_tuple[:-1] + ("weight",)
																		lowercase__    :				str=	flax_tensor.T
												elif flax_key_tuple[-1] in ["scale", "embedding"]:
																		lowercase__    :				List[Any]=	flax_key_tuple[:-1] + ("weight",)

												# adding batch stats from flax batch norm to pt
												elif "mean" in flax_key_tuple[-1]:
																		lowercase__    :				Union[str, Any]=	flax_key_tuple[:-1] + ("running_mean",)
												elif "var" in flax_key_tuple[-1]:
																		lowercase__    :				Optional[Any]=	flax_key_tuple[:-1] + ("running_var",)

												if "batch_stats" in flax_state:
																		lowercase__    :				List[str]=	".".join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
												else:
																		lowercase__    :				Tuple=	".".join(__lowerCAmelCase						)

												# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
												lowercase__    :				List[str]=	{}
												# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
												for key in pt_model_dict:
																		lowercase__    :				Optional[Any]=	key.split("."						)
																		lowercase__    :				Tuple=	None
																		if key_components[-3::2] == ["parametrizations", "original0"]:
																								lowercase__    :				List[Any]=	key_components[-2] + "_g"
																		elif key_components[-3::2] == ["parametrizations", "original1"]:
																								lowercase__    :				Tuple=	key_components[-2] + "_v"
																		if name is not None:
																								lowercase__    :				List[Any]=	key_components[:-3] + [name]
																								lowercase__    :				Any=	".".join(__lowerCAmelCase						)
																								lowercase__    :				Any=	key

												if flax_key in special_pt_names:
																		lowercase__    :				Optional[int]=	special_pt_names[flax_key]

												if flax_key in pt_model_dict:
																		if flax_tensor.shape != pt_model_dict[flax_key].shape:
																								raise ValueError(
																								    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																								    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)
																		else:
																								# add weight to pytorch dict
																								lowercase__    :				Optional[Any]=	np.asarray(__lowerCAmelCase						) if not isinstance(__lowerCAmelCase ,		np.ndarray						) else flax_tensor
																								lowercase__    :				str=	torch.from_numpy(__lowerCAmelCase						)
																								# remove from missing keys
																								missing_keys.remove(__lowerCAmelCase						)
												else:
																		# weight is not expected by PyTorch model
																		unexpected_keys.append(__lowerCAmelCase						)

						pt_model.load_state_dict(__lowerCAmelCase						)

						# re-transform missing_keys to list
						lowercase__    :				Optional[Any]=	list(__lowerCAmelCase						)

						if len(__lowerCAmelCase						) > 0:
												logger.warning(
												    "Some weights of the Flax model were not used when initializing the PyTorch model"
												    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
												    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
												    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
												    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
												    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
												    " FlaxBertForSequenceClassification model)."						)
						else:
												logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''						)
						if len(__lowerCAmelCase						) > 0:
												logger.warning(
												    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
												    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
												    " use it for predictions and inference."						)
						else:
												logger.warning(
												    f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
												    "If your task is similar to the task the model of the checkpoint was trained on, "
												    f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''						)

						return pt_model














"""simple docstring"""







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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


a			:    Dict				 =		logging.get_logger(__name__)


a			:    Optional[Any]				 =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
a			:    List[str]				 =		{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

a			:    Dict				 =		{
    """allenai/led-base-16384""": 16384,
}





@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						lowercase__()    ->List[Any]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	(
						    list(range(ord("!"						) ,		ord("~"						) + 1						)						) + list(range(ord("¡"						) ,		ord("¬"						) + 1						)						) + list(range(ord("®"						) ,		ord("ÿ"						) + 1						)						)
						)
						lowercase__    :				str=	bs[:]
						lowercase__    :				List[str]=	0
						for b in range(2**8						):
												if b not in bs:
																		bs.append(A						)
																		cs.append(2**8 + n						)
																		n += 1
						lowercase__    :				Union[str, Any]=	[chr(A						) for n in cs]
						return dict(zip(A ,		A						)						)





def 						lowercase__(A						)    ->str:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	set()
						lowercase__    :				Optional[int]=	word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)						)
												lowercase__    :				Tuple=	char
						return pairs







class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




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




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__="replace"	,  snake_case__="<s>"	,  snake_case__="</s>"	,  snake_case__="</s>"	,  snake_case__="<s>"	,  snake_case__="<unk>"	,  snake_case__="<pad>"	,  snake_case__="<mask>"	,  snake_case__=False	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Dict=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else bos_token
												lowercase__    :				Dict=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else eos_token
												lowercase__    :				str=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else sep_token
												lowercase__    :				Optional[Any]=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else cls_token
												lowercase__    :				List[str]=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else unk_token
												lowercase__    :				Any=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else pad_token

												# Mask token behave like a normal word, i.e. include the space before it
												lowercase__    :				int=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else mask_token

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

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

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												lowercase__    :				Optional[int]=	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.bart.tokenization_bart.BartTokenizer.vocab_size
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return len(self.encoder      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return dict(self.encoder	,  **self.added_tokens_encoder      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												if token in self.cache:
																		return self.cache[token]
												lowercase__    :				Any=	tuple(snake_case__      )
												lowercase__    :				Union[str, Any]=	get_pairs(snake_case__      )

												if not pairs:
																		return token

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




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	[]
												for token in re.findall(self.pat	,  snake_case__      ):
																		lowercase__    :				int=	"".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 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												return self.encoder.get(snake_case__	,  self.encoder.get(self.unk_token      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												return self.decoder.get(snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	"".join(snake_case__      )
												lowercase__    :				Optional[int]=	bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"	,  errors=self.errors      )
												return text




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												if not os.path.isdir(snake_case__      ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																		return
												lowercase__    :				Dict=	os.path.join(
												    snake_case__	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
												lowercase__    :				Tuple=	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"      )

												lowercase__    :				Any=	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!"      )
																														lowercase__    :				Optional[Any]=	token_index
																								writer.write(" ".join(snake_case__      ) + "\n"      )
																								index += 1

												return vocab_file, merge_file




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase__    :				Any=	[self.cls_token_id]
												lowercase__    :				Dict=	[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = False      ):
												'''simple docstring'''
												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case__	,  token_ids_a=snake_case__	,  already_has_special_tokens=snake_case__      )

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




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	[self.sep_token_id]
												lowercase__    :				Optional[int]=	[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 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__=False	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	kwargs.pop("add_prefix_space"	,  self.add_prefix_space      )
												if (is_split_into_words or add_prefix_space) and (len(snake_case__      ) > 0 and not text[0].isspace()):
																		lowercase__    :				List[Any]=	" " + text
												return (text, kwargs)




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = PaddingStrategy.DO_NOT_PAD	,  snake_case__ = None	,  snake_case__ = None	,  ):
												'''simple docstring'''
												lowercase__    :				List[str]=	super()._pad(
												    encoded_inputs=snake_case__	,  max_length=snake_case__	,  padding_strategy=snake_case__	,  pad_to_multiple_of=snake_case__	,  return_attention_mask=snake_case__	,  )

												# Load from model defaults
												if return_attention_mask is None:
																		lowercase__    :				Tuple=	"attention_mask" in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		lowercase__    :				Any=	encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		lowercase__    :				int=	len(encoded_inputs["global_attention_mask"]      ) != len(snake_case__      )

																		if needs_to_be_padded:
																								lowercase__    :				Dict=	len(snake_case__      ) - len(encoded_inputs["global_attention_mask"]      )

																								if self.padding_side == "right":
																														# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																														lowercase__    :				Tuple=	(
																														    encoded_inputs["global_attention_mask"] + [-1] * difference
																														)
																								elif self.padding_side == "left":
																														lowercase__    :				Any=	[-1] * difference + encoded_inputs[
																														    "global_attention_mask"
																														]
																								else:
																														raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return encoded_inputs






