






'''simple docstring'''

import json
import os
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:   List[str]						=					logging.get_logger(__name__)

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

A:   Tuple						=					{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

A:   Optional[int]						=					{
    "microsoft/deberta-base": 5_1_2,
    "microsoft/deberta-large": 5_1_2,
    "microsoft/deberta-xlarge": 5_1_2,
    "microsoft/deberta-base-mnli": 5_1_2,
    "microsoft/deberta-large-mnli": 5_1_2,
    "microsoft/deberta-xlarge-mnli": 5_1_2,
}

A:   Any						=					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





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






def 						_UpperCAmelCase     (	a		:						Optional[int]				)      ->      Union[str, Any]:





						"""simple docstring"""
						lowercase_   : str   	=      set()
						lowercase_   : Optional[int]   	=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)				)
												lowercase_   : str   	=      char
						return pairs






class 				__magic_name__  (						UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   List[str]   =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_       :   Tuple   =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_       :   Dict   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_       :   Dict   =	['input_ids', 'attention_mask', 'token_type_ids']
	def __init__(				self	,   _lowercase	,   _lowercase	,   _lowercase="replace"	,   _lowercase="[CLS]"	,   _lowercase="[SEP]"	,   _lowercase="[SEP]"	,   _lowercase="[CLS]"	,   _lowercase="[UNK]"	,   _lowercase="[PAD]"	,   _lowercase="[MASK]"	,   _lowercase=False	,   _lowercase=False	,   **_lowercase	,   )    ->	Union[str, Any]:
							lowercase_   : List[str]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else bos_token
							lowercase_   : str   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else eos_token
							lowercase_   : Tuple   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else sep_token
							lowercase_   : List[Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else cls_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else unk_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else pad_token

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

							super().__init__(
							    errors=_lowercase	,   bos_token=_lowercase	,   eos_token=_lowercase	,   unk_token=_lowercase	,   sep_token=_lowercase	,   cls_token=_lowercase	,   pad_token=_lowercase	,   mask_token=_lowercase	,   add_prefix_space=_lowercase	,   add_bos_token=_lowercase	,   **_lowercase	,   )
							lowercase_   : str   	=      add_bos_token

							with open(_lowercase	,   encoding='utf-8'							) as vocab_handle:
													lowercase_   : Union[str, Any]   	=      json.load(_lowercase							)
							lowercase_   : List[Any]   	=      {v: k for k, v in self.encoder.items()}
							lowercase_   : int   	=      errors  # how to handle errors in decoding
							lowercase_   : str   	=      bytes_to_unicode()
							lowercase_   : List[Any]   	=      {v: k for k, v in self.byte_encoder.items()}
							with open(_lowercase	,   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_   : Dict   	=      dict(zip(_lowercase	,   range(len(_lowercase							)							)							)							)
							lowercase_   : Any   	=      {}
							lowercase_   : str   	=      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowercase_   : 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.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							return len(self.encoder							)
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return dict(self.encoder	,   **self.added_tokens_encoder							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							if token in self.cache:
													return self.cache[token]
							lowercase_   : Any   	=      tuple(_lowercase							)
							lowercase_   : Union[str, Any]   	=      get_pairs(_lowercase							)

							if not pairs:
													return token

							while True:
													lowercase_   : int   	=      min(_lowercase	,   key=lambda _lowercase							: self.bpe_ranks.get(_lowercase	,   float('inf'							)							)							)
													if bigram not in self.bpe_ranks:
																			break
													lowercase_	,   lowercase_   : Any   	=      bigram
													lowercase_   : Dict   	=      []
													lowercase_   : Tuple   	=      0
													while i < len(_lowercase							):
																			try:
																									lowercase_   : str   	=      word.index(_lowercase	,   _lowercase							)
																			except ValueError:
																									new_word.extend(word[i:]							)
																									break
																			else:
																									new_word.extend(word[i:j]							)
																									lowercase_   : str   	=      j

																			if word[i] == first and i < len(_lowercase							) - 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(_lowercase							)
													lowercase_   : Tuple   	=      new_word
													if len(_lowercase							) == 1:
																			break
													else:
																			lowercase_   : Optional[Any]   	=      get_pairs(_lowercase							)
							lowercase_   : Tuple   	=      ' '.join(_lowercase							)
							lowercase_   : int   	=      word
							return word
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_   : Union[str, Any]   	=      [self.cls_token_id]
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None	,   _lowercase = False							)    ->	List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowercase	,   token_ids_a=_lowercase	,   already_has_special_tokens=_lowercase							)

							if token_ids_a is None:
													return [1] + ([0] * len(_lowercase							)) + [1]
							return [1] + ([0] * len(_lowercase							)) + [1] + ([0] * len(_lowercase							)) + [1]
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							lowercase_   : Optional[Any]   	=      [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							) * [0] + len(token_ids_a + sep							) * [1]
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Optional[int]   	=      []
							for token in re.findall(self.pat	,   _lowercase							):
													lowercase_   : List[str]   	=      ''.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(_lowercase							).split(' '							)							)
							return bpe_tokens
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[str]:
							return self.encoder.get(_lowercase	,   self.encoder.get(self.unk_token							)							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							return self.decoder.get(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	int:
							lowercase_   : List[str]   	=      ''.join(_lowercase							)
							lowercase_   : List[str]   	=      bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'	,   errors=self.errors							)
							return text
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	Tuple[str]:
							if not os.path.isdir(_lowercase							):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
													return
							lowercase_   : Any   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
							lowercase_   : Optional[Any]   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

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

							lowercase_   : List[str]   	=      0
							with open(_lowercase	,   '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 _lowercase							: 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_   : Any   	=      token_index
																			writer.write(' '.join(_lowercase							) + '\n'							)
																			index += 1

							return vocab_file, merge_file
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase=False	,   **_lowercase							)    ->	Union[str, Any]:
							lowercase_   : List[Any]   	=      kwargs.pop('add_prefix_space'	,   self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(_lowercase							) > 0 and not text[0].isspace()):
													lowercase_   : Dict   	=      ' ' + text
							return (text, kwargs)







	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[int]:
							lowercase_   : Optional[int]   	=      []
							for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(_lowercase	,   add_special_tokens=_lowercase							) + [self.eos_token_id]							)
							if len(_lowercase							) > self.model_max_length:
													lowercase_   : Optional[int]   	=      input_ids[-self.model_max_length :]
							return input_ids








'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

# Datasets
A:   Optional[Any]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/datasets"
A:   Dict						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A:   List[str]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/metric"
A:   List[str]						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A:   str						=					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A:   List[Any]						=					HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A:   List[Any]						=					"main"

A:   Any						=					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A:   Optional[Any]						=					{"1", "ON", "YES", "TRUE"}
A:   Optional[int]						=					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("torch"))
																		logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling PyTorch because USE_TF is set")

A:   Union[str, Any]						=					"N/A"
A:   Dict						=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						A:   Optional[Any]						=					importlib.util.find_spec("tensorflow") is not None
						if TF_AVAILABLE:
												# For the metadata, we have to look for both tensorflow and tensorflow-cpu
												for package in [
												    "tensorflow",
												    "tensorflow-cpu",
												    "tensorflow-gpu",
												    "tf-nightly",
												    "tf-nightly-cpu",
												    "tf-nightly-gpu",
												    "intel-tensorflow",
												    "tensorflow-rocm",
												    "tensorflow-macos",
												]:
																		try:
																								A:   Any						=					version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		A:   Any						=					False
						if TF_AVAILABLE:
												if TF_VERSION.major < 2:
																		logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																		A:   Tuple						=					False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("Disabling Tensorflow because USE_TORCH is set")


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("jax"))
																		logger.info(f"""JAX version {JAX_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling JAX because USE_JAX is set to False")


A:   Optional[Any]						=					os.environ.get("USE_BEAM", "AUTO").upper()
A:   Any						=					"N/A"
A:   List[Any]						=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
						try:
												A:   Tuple						=					version.parse(importlib_metadata.version("apache_beam"))
												A:   Dict						=					True
												logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
												pass
else:
						logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
A:   Union[str, Any]						=					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A:   Optional[int]						=					importlib.util.find_spec("PIL") is not None
A:   Dict						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A:   Any						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
A:   Optional[Any]						=					importlib.util.find_spec("rarfile") is not None
A:   Any						=					importlib.util.find_spec("zstandard") is not None
A:   Union[str, Any]						=					importlib.util.find_spec("lz4") is not None
A:   str						=					importlib.util.find_spec("py7zr") is not None


# Cache location
A:   Any						=					"~/.cache"
A:   Tuple						=					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A:   Dict						=					os.path.join(XDG_CACHE_HOME, "huggingface")
A:   List[str]						=					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A:   int						=					os.path.join(HF_CACHE_HOME, "datasets")
A:   int						=					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A:   List[str]						=					"downloads"
A:   Tuple						=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A:   Union[str, Any]						=					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, Any]						=					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A:   List[str]						=					"500MB"

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

# Offline mode
A:   List[str]						=					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A:   List[str]						=					"dataset.arrow"
A:   Optional[int]						=					"indices.arrow"
A:   List[str]						=					"state.json"
A:   Tuple						=					"dataset_info.json"
A:   Tuple						=					"dataset_infos.json"
A:   Dict						=					"LICENSE"
A:   Optional[Any]						=					"metric_info.json"
A:   Any						=					"dataset_dict.json"

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec
