import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
		import tensorflow as tf

		tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset


lowerCamelCase__           =				5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors



def 							_lowerCamelCase(				__snake_case	)  ->   List[str]:
		return PTtoTFCommand(
		    args.model_name						,						args.local_dir						,						args.max_error						,						args.new_weights						,						args.no_pr						,						args.push						,						args.extra_commit_description						,						args.override_model_class						,						)





class 					UpperCamelCase	(     snake_case__      ):




					@staticmethod
					def 							UpperCamelCase_						( _lowerCAmelCase :					ArgumentParser						):


							"""simple docstring"""




							__snake_case		       =     parser.add_parser(
							    "pt-to-tf"      ,help=(
							        "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
							        " Can also be used to validate existing weights without opening PRs, with --no-pr."
							    )      ,)
							train_parser.add_argument(
							    "--model-name"      ,type=_lowerCAmelCase      ,required=_lowerCAmelCase      ,help="The model name, including owner/organization, as seen on the hub."      ,)
							train_parser.add_argument(
							    "--local-dir"      ,type=_lowerCAmelCase      ,default=""      ,help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"      ,)
							train_parser.add_argument(
							    "--max-error"      ,type=_lowerCAmelCase      ,default=_lowerCAmelCase      ,help=(
							        F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
							    )      ,)
							train_parser.add_argument(
							    "--new-weights"      ,action="store_true"      ,help="Optional flag to create new TensorFlow weights, even if they already exist."      ,)
							train_parser.add_argument(
							    "--no-pr"      ,action="store_true"      ,help="Optional flag to NOT open a PR with converted weights."						)
							train_parser.add_argument(
							    "--push"      ,action="store_true"      ,help="Optional flag to push the weights directly to `main` (requires permissions)"      ,)
							train_parser.add_argument(
							    "--extra-commit-description"      ,type=_lowerCAmelCase      ,default=""      ,help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)."      ,)
							train_parser.add_argument(
							    "--override-model-class"      ,type=_lowerCAmelCase      ,default=_lowerCAmelCase      ,help="If you think you know better than the auto-detector, you can specify the model class here. "
							    "Can be either an AutoModel class or a specific model class like BertForSequenceClassification."      ,)
							train_parser.set_defaults(func=_lowerCAmelCase						)




					@staticmethod
					def 							UpperCamelCase_						( _lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     set(pt_outputs.keys()						)
							__snake_case		       =     set(tf_outputs.keys()						)
							if pt_out_attrs != tf_out_attrs:
									raise ValueError(
									    F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
									    F""" {tf_out_attrs})"""						)

							# 2. For each output attribute, computes the difference
							def _find_pt_tf_differences(_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					int=""						):
									# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
									# recursivelly, keeping the name of the attribute.
									if isinstance(_lowerCAmelCase      ,torch.Tensor						):
											__snake_case		       =     np.max(np.abs(pt_out.numpy() - tf_out.numpy()						)						)
											__snake_case		       =     tensor_difference
									else:
											__snake_case		       =     attr_name
											for i, pt_item in enumerate(_lowerCAmelCase						):
													# If it is a named attribute, we keep the name. Otherwise, just its index.
													if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
															__snake_case		       =     root_name + pt_item
															__snake_case		       =     tf_out[pt_item]
															__snake_case		       =     pt_out[pt_item]
													else:
															__snake_case		       =     root_name + F"""[{i}]"""
															__snake_case		       =     tf_out[i]
													__snake_case		       =     _find_pt_tf_differences(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)

									return differences

							return _find_pt_tf_differences(_lowerCAmelCase      ,_lowerCAmelCase      ,{}						)




					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					float      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str      ,*_lowerCAmelCase :					List[Any]      ,):


							"""simple docstring"""




							__snake_case		       =     logging.get_logger("transformers-cli/pt_to_tf"						)
							__snake_case		       =     model_name
							__snake_case		       =     local_dir if local_dir else os.path.join("/tmp"      ,_lowerCAmelCase						)
							__snake_case		       =     max_error
							__snake_case		       =     new_weights
							__snake_case		       =     no_pr
							__snake_case		       =     push
							__snake_case		       =     extra_commit_description
							__snake_case		       =     override_model_class




					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							def _get_audio_input():
									__snake_case		       =     load_dataset("hf-internal-testing/librispeech_asr_dummy"      ,"clean"      ,split="validation"						)
									__snake_case		       =     ds.sort("id"						).select(range(2						)						)[:2]["audio"]
									__snake_case		       =     [x["array"] for x in speech_samples]
									return raw_samples

							__snake_case		       =     type(pt_model.config						)
							if model_config_class in PROCESSOR_MAPPING:
									__snake_case		       =     AutoProcessor.from_pretrained(self._local_dir						)
									if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
											__snake_case		       =     processor.tokenizer.eos_token
							elif model_config_class in IMAGE_PROCESSOR_MAPPING:
									__snake_case		       =     AutoImageProcessor.from_pretrained(self._local_dir						)
							elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
									__snake_case		       =     AutoFeatureExtractor.from_pretrained(self._local_dir						)
							elif model_config_class in TOKENIZER_MAPPING:
									__snake_case		       =     AutoTokenizer.from_pretrained(self._local_dir						)
									if processor.pad_token is None:
											__snake_case		       =     processor.eos_token
							else:
									raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""						)

							__snake_case		       =     set(inspect.signature(pt_model.forward						).parameters.keys()						)
							__snake_case		       =     {}
							if "input_ids" in model_forward_signature:
									processor_inputs.update(
									    {
									        "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
									        "padding": True,
									        "truncation": True,
									    }						)
							if "pixel_values" in model_forward_signature:
									__snake_case		       =     load_dataset("cifar10"      ,"plain_text"      ,split="test"						)[:2]["img"]
									processor_inputs.update({"images": sample_images}						)
							if "input_features" in model_forward_signature:
									__snake_case		       =     inspect.signature(processor.feature_extractor						).parameters
									# Pad to the largest input length by default but take feature extractor default
									# padding value if it exists e.g. "max_length" and is not False or None
									if "padding" in feature_extractor_signature:
											__snake_case		       =     feature_extractor_signature["padding"].default
											if default_strategy is not False and default_strategy is not None:
													__snake_case		       =     default_strategy
											else:
													__snake_case		       =     True
									else:
											__snake_case		       =     True
									processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}						)
							if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
									processor_inputs.update({"audio": _get_audio_input(), "padding": True}						)
							__snake_case		       =     processor(**_lowerCAmelCase      ,return_tensors="pt"						)
							__snake_case		       =     processor(**_lowerCAmelCase      ,return_tensors="tf"						)

							# Extra input requirements, in addition to the input modality
							if (
							    config.is_encoder_decoder
							    or (hasattr(_lowerCAmelCase      ,"encoder"						) and hasattr(_lowerCAmelCase      ,"decoder"						))
							    or "decoder_input_ids" in tf_dummy_inputs
							):
									__snake_case		       =     np.asarray([[1], [1]]      ,dtype=_lowerCAmelCase						) * (pt_model.config.decoder_start_token_id or 0)
									pt_input.update({"decoder_input_ids": torch.tensor(_lowerCAmelCase						)}						)
									tf_input.update({"decoder_input_ids": tf.convert_to_tensor(_lowerCAmelCase						)}						)

							return pt_input, tf_input







					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							if version.parse(huggingface_hub.__version__						) < version.parse("0.9.0"						):
									raise ImportError(
									    "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
									    " installation."						)
							else:
									from huggingface_hub import Repository, create_commit
									from huggingface_hub._commit_api import CommitOperationAdd

							# Fetch remote data
							__snake_case		       =     Repository(local_dir=self._local_dir      ,clone_from=self._model_name						)

							# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
							__snake_case		       =     AutoConfig.from_pretrained(self._local_dir						)
							__snake_case		       =     config.architectures
							if self._override_model_class is not None:
									if self._override_model_class.startswith("TF"						):
											__snake_case		       =     [self._override_model_class[2:]]
									else:
											__snake_case		       =     [self._override_model_class]
									try:
											__snake_case		       =     getattr(import_module("transformers"						)      ,architectures[0]						)
									except AttributeError:
											raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""						)
									try:
											__snake_case		       =     getattr(import_module("transformers"						)      ,"TF" + architectures[0]						)
									except AttributeError:
											raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers."""						)
							elif architectures is None:  # No architecture defined -- use auto classes
									__snake_case		       =     getattr(import_module("transformers"						)      ,"AutoModel"						)
									__snake_case		       =     getattr(import_module("transformers"						)      ,"TFAutoModel"						)
									self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"						)
							else:  # Architecture defined -- use it
									if len(_lowerCAmelCase						) > 1:
											raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""						)
									self._logger.warning(F"""Detected architecture: {architectures[0]}"""						)
									__snake_case		       =     getattr(import_module("transformers"						)      ,architectures[0]						)
									try:
											__snake_case		       =     getattr(import_module("transformers"						)      ,"TF" + architectures[0]						)
									except AttributeError:
											raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""						)

        # Check the TF dummy inputs to see what keys we need in the forward pass
							__snake_case		       =     tf_class.from_config(_lowerCAmelCase						)
							__snake_case		       =     tf_from_pt_model.dummy_inputs

							del tf_from_pt_model  # Try to keep only one model in memory at a time

							# Load the model and get some basic inputs
							__snake_case		       =     pt_class.from_pretrained(self._local_dir						)
							pt_model.eval()

							__snake_case       ,       __snake_case		       =     self.get_inputs(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)

							with torch.no_grad():
									__snake_case		       =     pt_model(**_lowerCAmelCase      ,output_hidden_states=_lowerCAmelCase						)
							del pt_model  # will no longer be used, and may have a large memory footprint

							__snake_case		       =     tf_class.from_pretrained(self._local_dir      ,from_pt=_lowerCAmelCase						)
							__snake_case		       =     tf_from_pt_model(**_lowerCAmelCase      ,output_hidden_states=_lowerCAmelCase      ,training=_lowerCAmelCase						)

							# Confirms that cross loading PT weights into TF worked.
							__snake_case		       =     self.find_pt_tf_differences(_lowerCAmelCase      ,_lowerCAmelCase						)
							__snake_case		       =     {k: v for k, v in crossload_differences.items() if "hidden" not in k}
							__snake_case		       =     {k: v for k, v in crossload_differences.items() if "hidden" in k}
							if len(_lowerCAmelCase						) == 0 and architectures is not None:
									raise ValueError(
									    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
									    " output was found. All outputs start with 'hidden'"						)
							__snake_case		       =     max(output_differences.values()						) if output_differences else 0.0
							__snake_case		       =     max(hidden_differences.values()						)
							if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
									raise ValueError(
									    "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
									    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
									    + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]						)
									    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
									    + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]						)						)

							# Save the weights in a TF format (if needed) and confirms that the results are still good
							__snake_case		       =     os.path.join(self._local_dir      ,_lowerCAmelCase						)
							__snake_case		       =     os.path.join(self._local_dir      ,_lowerCAmelCase						)
							if (not os.path.exists(_lowerCAmelCase						) and not os.path.exists(_lowerCAmelCase						)) or self._new_weights:
									tf_from_pt_model.save_pretrained(self._local_dir						)
							del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

							__snake_case		       =     tf_class.from_pretrained(self._local_dir						)
							__snake_case		       =     tf_model(**_lowerCAmelCase      ,output_hidden_states=_lowerCAmelCase						)

							__snake_case		       =     self.find_pt_tf_differences(_lowerCAmelCase      ,_lowerCAmelCase						)
							__snake_case		       =     {k: v for k, v in conversion_differences.items() if "hidden" not in k}
							__snake_case		       =     {k: v for k, v in conversion_differences.items() if "hidden" in k}
							if len(_lowerCAmelCase						) == 0 and architectures is not None:
									raise ValueError(
									    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
									    " output was found. All outputs start with 'hidden'"						)
							__snake_case		       =     max(output_differences.values()						) if output_differences else 0.0
							__snake_case		       =     max(hidden_differences.values()						)
							if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
									raise ValueError(
									    "The converted TensorFlow model has different outputs, something went wrong!\n"
									    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
									    + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]						)
									    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
									    + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]						)						)

							__snake_case		       =     "Update TF weights" if self._new_weights else "Add TF weights"
							if self._push:
									repo.git_add(auto_lfs_track=_lowerCAmelCase						)
									repo.git_commit(_lowerCAmelCase						)
									repo.git_push(blocking=_lowerCAmelCase						)  # this prints a progress bar with the upload
									self._logger.warning(F"""TF weights pushed into {self._model_name}"""						)
							elif not self._no_pr:
									self._logger.warning("Uploading the weights into a new PR..."						)
									__snake_case		       =     (
									    "Model converted by the [`transformers`' `pt_to_tf`"
									    " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
									    "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
									    F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
									    F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
									    F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
									    F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
									)
									if self._max_error > MAX_ERROR:
											commit_descrition += (
											    F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
											)
									if self._extra_commit_description:
											commit_descrition += "\n\n" + self._extra_commit_description

									# sharded model -> adds all related files (index and .h5 shards)
									if os.path.exists(_lowerCAmelCase						):
											__snake_case		       =     [
											    CommitOperationAdd(path_in_repo=_lowerCAmelCase      ,path_or_fileobj=_lowerCAmelCase						)
											]
											for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"						):
													operations += [
													    CommitOperationAdd(path_in_repo=os.path.basename(_lowerCAmelCase						)      ,path_or_fileobj=_lowerCAmelCase						)
													]
									else:
											__snake_case		       =     [CommitOperationAdd(path_in_repo=_lowerCAmelCase      ,path_or_fileobj=_lowerCAmelCase						)]

									__snake_case		       =     create_commit(
									    repo_id=self._model_name      ,operations=_lowerCAmelCase      ,commit_message=_lowerCAmelCase      ,commit_description=_lowerCAmelCase      ,repo_type="model"      ,create_pr=_lowerCAmelCase      ,).pr_url
									self._logger.warning(F"""PR open in {hub_pr_url}"""						)






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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__           =				logging.get_logger(__name__)

lowerCamelCase__           =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__           =				{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase__           =				{
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}



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



def 							_lowerCamelCase(				__snake_case	)  ->   Optional[Any]:
		__snake_case		       =     set()
		__snake_case		       =     word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				__snake_case		       =     char
		return pairs





class 					UpperCamelCase	(     snake_case__      ):
					__UpperCamelCase						=   VOCAB_FILES_NAMES
					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]




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


							"""simple docstring"""




							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else bos_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else eos_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else sep_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else cls_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else unk_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else pad_token

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

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

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

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




					@property
					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							return len(self.encoder						)




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							if token in self.cache:
									return self.cache[token]
							__snake_case		       =     tuple(_lowerCAmelCase						)
							__snake_case		       =     get_pairs(_lowerCAmelCase						)

							if not pairs:
									return token

							while True:
									__snake_case		       =     min(_lowerCAmelCase      ,key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase      ,float("inf"						)						)						)
									if bigram not in self.bpe_ranks:
											break
									__snake_case       ,       __snake_case		       =     bigram
									__snake_case		       =     []
									__snake_case		       =     0
									while i < len(_lowerCAmelCase						):
											try:
													__snake_case		       =     word.index(_lowerCAmelCase      ,_lowerCAmelCase						)
											except ValueError:
													new_word.extend(word[i:]						)
													break
											else:
													new_word.extend(word[i:j]						)
													__snake_case		       =     j

											if word[i] == first and i < len(_lowerCAmelCase						) - 1 and word[i + 1] == second:
													new_word.append(first + second						)
													i += 2
											else:
													new_word.append(word[i]						)
													i += 1
									__snake_case		       =     tuple(_lowerCAmelCase						)
									__snake_case		       =     new_word
									if len(_lowerCAmelCase						) == 1:
											break
									else:
											__snake_case		       =     get_pairs(_lowerCAmelCase						)
							__snake_case		       =     " ".join(_lowerCAmelCase						)
							__snake_case		       =     word
							return word




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




							__snake_case		       =     []
							for token in re.findall(self.pat      ,_lowerCAmelCase						):
									__snake_case		       =     "".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(_lowerCAmelCase						).split(" "						)						)
							return bpe_tokens




					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					Union[str, Any]						):


							"""simple docstring"""




							return self.decoder.get(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):


							"""simple docstring"""




							if not os.path.isdir(_lowerCAmelCase						):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
									return
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

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

							__snake_case		       =     0
							with open(_lowerCAmelCase      ,"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 _lowerCAmelCase						: 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!"						)
													__snake_case		       =     token_index
											writer.write(" ".join(_lowerCAmelCase						) + "\n"						)
											index += 1

							return vocab_file, merge_file




					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None      ,_lowerCAmelCase :					bool = False						):


							"""simple docstring"""




							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_lowerCAmelCase      ,token_ids_a=_lowerCAmelCase      ,already_has_special_tokens=_lowerCAmelCase						)

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




					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):


							"""simple docstring"""




							__snake_case		       =     [self.sep_token_id]
							__snake_case		       =     [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 :					int      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					Optional[Any]=False      ,**_lowerCAmelCase :					List[str]						):


							"""simple docstring"""




							__snake_case		       =     kwargs.pop("add_prefix_space"      ,self.add_prefix_space						)
							if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase						) > 0 and not text[0].isspace()):
									__snake_case		       =     " " + text
							return (text, kwargs)





