






"""simple docstring"""







import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_lowerCAmelCase            =	collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCAmelCase            =	'https://storage.googleapis.com/cvdf-datasets/mnist/'


def      UpperCamelCase       ( _A )     ->       Dict:
				lowercase    :      Tuple       = numpy.dtype(numpy.uintaa ).newbyteorder(""">""" )
				return numpy.frombuffer(bytestream.read(4 )  ,				dtype=_A )[0]


@deprecated(_A  ,				"""Please use tf.data to implement this functionality.""" )
def      UpperCamelCase       ( _A )     ->       List[Any]:
				print("""Extracting"""  ,				f.name )
				with gzip.GzipFile(fileobj=_A ) as bytestream:
								lowercase    :      Dict       = _readaa(_A )
								if magic != 2_051:
												raise ValueError(
												    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name) )
								lowercase    :      Tuple       = _readaa(_A )
								lowercase    :      List[Any]       = _readaa(_A )
								lowercase    :      Dict       = _readaa(_A )
								lowercase    :      List[str]       = bytestream.read(rows * cols * num_images )
								lowercase    :      List[str]       = numpy.frombuffer(_A  ,				dtype=numpy.uinta )
								lowercase    :      Optional[int]       = data.reshape(_A  ,				_A  ,				_A  ,				1 )
								return data


@deprecated(_A  ,				"""Please use tf.one_hot on tensors.""" )
def      UpperCamelCase       ( _A  ,				_A )     ->       Union[str, Any]:
				lowercase    :      List[str]       = labels_dense.shape[0]
				lowercase    :      int       = numpy.arange(_A ) * num_classes
				lowercase    :      int       = numpy.zeros((num_labels, num_classes) )
				lowercase    :      List[str]       = 1
				return labels_one_hot


@deprecated(_A  ,				"""Please use tf.data to implement this functionality.""" )
def      UpperCamelCase       ( _A  ,				_A=False  ,				_A=10 )     ->       str:
				print("""Extracting"""  ,				f.name )
				with gzip.GzipFile(fileobj=_A ) as bytestream:
								lowercase    :      List[Any]       = _readaa(_A )
								if magic != 2_049:
												raise ValueError(
												    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name) )
								lowercase    :      int       = _readaa(_A )
								lowercase    :      List[Any]       = bytestream.read(_A )
								lowercase    :      Optional[int]       = numpy.frombuffer(_A  ,				dtype=numpy.uinta )
								if one_hot:
												return _dense_to_one_hot(_A  ,				_A )
								return labels


class      UpperCamelCase		:






						@deprecated(
						    __magic_name__			, """Please use alternatives such as official/mnist/_DataSet.py"""
						    """ from tensorflow/models."""			, )
						def __init__(		self  :List[str]			, __magic_name__  :Any			, __magic_name__  :List[str]			, __magic_name__  :List[Any]=False			, __magic_name__  :Dict=False			, __magic_name__  :List[Any]=dtypes.floataa			, __magic_name__  :Dict=True			, __magic_name__  :Tuple=None			, )      ->List[Any]:
										lowercase      ,      lowercase    :      str       = random_seed.get_seed(__magic_name__				)
										# If op level seed is not set, use whatever graph level seed is returned
										numpy.random.seed(seeda if seed is None else seeda				)
										lowercase    :      int       = dtypes.as_dtype(__magic_name__				).base_dtype
										if dtype not in (dtypes.uinta, dtypes.floataa):
														raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype				)
										if fake_data:
														lowercase    :      int       = 10_000
														lowercase    :      Any       = one_hot
										else:
														assert (
														    images.shape[0] == labels.shape[0]
														), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
														lowercase    :      Union[str, Any]       = images.shape[0]

														# Convert shape from [num examples, rows, columns, depth]
														# to [num examples, rows*columns] (assuming depth == 1)
														if reshape:
																		assert images.shape[3] == 1
																		lowercase    :      Tuple       = images.reshape(
																		    images.shape[0]			, images.shape[1] * images.shape[2]				)
														if dtype == dtypes.floataa:
																		# Convert from [0, 255] -> [0.0, 1.0].
																		lowercase    :      Tuple       = images.astype(numpy.floataa				)
																		lowercase    :      Dict       = numpy.multiply(__magic_name__			, 1.0 / 255.0				)
										lowercase    :      List[Any]       = images
										lowercase    :      Dict       = labels
										lowercase    :      Union[str, Any]       = 0
										lowercase    :      str       = 0






						@property
						def      __snake_case      (		self  :Tuple				)      ->List[Any]:
										return self._images






						@property
						def      __snake_case      (		self  :Dict				)      ->Optional[int]:
										return self._labels






						@property
						def      __snake_case      (		self  :Any				)      ->Tuple:
										return self._num_examples






						@property
						def      __snake_case      (		self  :Tuple				)      ->Optional[Any]:
										return self._epochs_completed






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Union[str, Any]			, __magic_name__  :Union[str, Any]=False			, __magic_name__  :Optional[int]=True				)      ->Dict:
										if fake_data:
														lowercase    :      List[str]       = [1] * 784
														lowercase    :      Union[str, Any]       = [1] + [0] * 9 if self.one_hot else 0
														return (
														    [fake_image for _ in range(__magic_name__				)],
														    [fake_label for _ in range(__magic_name__				)],
														)
										lowercase    :      Any       = self._index_in_epoch
										# Shuffle for the first epoch
										if self._epochs_completed == 0 and start == 0 and shuffle:
														lowercase    :      Union[str, Any]       = numpy.arange(self._num_examples				)
														numpy.random.shuffle(__magic_name__				)
														lowercase    :      Optional[int]       = self.images[perma]
														lowercase    :      Tuple       = self.labels[perma]
										# Go to the next epoch
										if start + batch_size > self._num_examples:
														# Finished epoch
														self._epochs_completed += 1
														# Get the rest examples in this epoch
														lowercase    :      List[Any]       = self._num_examples - start
														lowercase    :      List[Any]       = self._images[start : self._num_examples]
														lowercase    :      Any       = self._labels[start : self._num_examples]
														# Shuffle the data
														if shuffle:
																		lowercase    :      int       = numpy.arange(self._num_examples				)
																		numpy.random.shuffle(__magic_name__				)
																		lowercase    :      Tuple       = self.images[perm]
																		lowercase    :      Optional[int]       = self.labels[perm]
														# Start next epoch
														lowercase    :      Optional[int]       = 0
														lowercase    :      Any       = batch_size - rest_num_examples
														lowercase    :      int       = self._index_in_epoch
														lowercase    :      Dict       = self._images[start:end]
														lowercase    :      Dict       = self._labels[start:end]
														return (
														    numpy.concatenate((images_rest_part, images_new_part)			, axis=0				),
														    numpy.concatenate((labels_rest_part, labels_new_part)			, axis=0				),
														)
										else:
														self._index_in_epoch += batch_size
														lowercase    :      str       = self._index_in_epoch
														return self._images[start:end], self._labels[start:end]


@deprecated(_A  ,				"""Please write your own downloading logic.""" )
def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				if not gfile.Exists(_A ):
								gfile.MakeDirs(_A )
				lowercase    :      int       = os.path.join(_A  ,				_A )
				if not gfile.Exists(_A ):
								urllib.request.urlretrieve(_A  ,				_A )  # noqa: S310
								with gfile.GFile(_A ) as f:
												lowercase    :      Union[str, Any]       = f.size()
								print("""Successfully downloaded"""  ,				_A  ,				_A  ,				"""bytes.""" )
				return filepath


@deprecated(
    _A  ,				"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')""" )
def      UpperCamelCase       ( _A  ,				_A=False  ,				_A=False  ,				_A=dtypes.floataa  ,				_A=True  ,				_A=5_000  ,				_A=None  ,				_A=DEFAULT_SOURCE_URL  ,				)     ->       Tuple:
				if fake_data:

								def fake():
												return _DataSet(
												    []  ,				[]  ,				fake_data=_A  ,				one_hot=_A  ,				dtype=_A  ,				seed=_A )

								lowercase    :      Tuple       = fake()
								lowercase    :      int       = fake()
								lowercase    :      str       = fake()
								return _Datasets(train=_A  ,				validation=_A  ,				test=_A )

				if not source_url:  # empty string check
								lowercase    :      str       = DEFAULT_SOURCE_URL

				lowercase    :      Optional[int]       = """train-images-idx3-ubyte.gz"""
				lowercase    :      Any       = """train-labels-idx1-ubyte.gz"""
				lowercase    :      List[str]       = """t10k-images-idx3-ubyte.gz"""
				lowercase    :      int       = """t10k-labels-idx1-ubyte.gz"""

				lowercase    :      Dict       = _maybe_download(
				    _A  ,				_A  ,				source_url + train_images_file )
				with gfile.Open(_A  ,				"""rb""" ) as f:
								lowercase    :      List[Any]       = _extract_images(_A )

				lowercase    :      Dict       = _maybe_download(
				    _A  ,				_A  ,				source_url + train_labels_file )
				with gfile.Open(_A  ,				"""rb""" ) as f:
								lowercase    :      int       = _extract_labels(_A  ,				one_hot=_A )

				lowercase    :      Tuple       = _maybe_download(
				    _A  ,				_A  ,				source_url + test_images_file )
				with gfile.Open(_A  ,				"""rb""" ) as f:
								lowercase    :      int       = _extract_images(_A )

				lowercase    :      Dict       = _maybe_download(
				    _A  ,				_A  ,				source_url + test_labels_file )
				with gfile.Open(_A  ,				"""rb""" ) as f:
								lowercase    :      List[str]       = _extract_labels(_A  ,				one_hot=_A )

				if not 0 <= validation_size <= len(_A ):
								lowercase    :      Union[str, Any]       = (
								    """Validation size should be between 0 and """
								    F"""{len(_A )}. Received: {validation_size}."""
								)
								raise ValueError(_A )

				lowercase    :      int       = train_images[:validation_size]
				lowercase    :      Dict       = train_labels[:validation_size]
				lowercase    :      List[str]       = train_images[validation_size:]
				lowercase    :      int       = train_labels[validation_size:]

				lowercase    :      str       = {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

				lowercase    :      List[str]       = _DataSet(_A  ,				_A  ,				**_A )
				lowercase    :      Optional[int]       = _DataSet(_A  ,				_A  ,				**_A )
				lowercase    :      Dict       = _DataSet(_A  ,				_A  ,				**_A )

				return _Datasets(train=_A  ,				validation=_A  ,				test=_A )








"""simple docstring"""







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase            =	logging.get_logger(__name__)


_lowerCAmelCase            =	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_lowerCAmelCase            =	{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_lowerCAmelCase            =	{'facebook/blenderbot_small-90M': 5_12}


def      UpperCamelCase       ( _A )     ->       Dict:
				lowercase    :      Union[str, Any]       = set()
				lowercase    :      Union[str, Any]       = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								lowercase    :      List[str]       = char

				lowercase    :      Optional[Any]       = set(_A )
				return pairs


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : str												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Any												=		["""input_ids""", """attention_mask"""]






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :str			, __magic_name__  :Union[str, Any]="__start__"			, __magic_name__  :Optional[int]="__end__"			, __magic_name__  :Tuple="__unk__"			, __magic_name__  :Optional[int]="__null__"			, **__magic_name__  :Tuple			, )      ->Tuple:
										super().__init__(unk_token=__magic_name__			, bos_token=__magic_name__			, eos_token=__magic_name__			, pad_token=__magic_name__			, **__magic_name__				)

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      int       = json.load(__magic_name__				)
										lowercase    :      int       = {v: k for k, v in self.encoder.items()}
										with open(__magic_name__			, encoding="""utf-8"""				) as merges_handle:
														lowercase    :      Any       = merges_handle.read().split("""\n"""				)[1:-1]
										lowercase    :      Union[str, Any]       = [tuple(merge.split()				) for merge in merges]
										lowercase    :      Any       = dict(zip(__magic_name__			, range(len(__magic_name__				)				)				)				)
										lowercase    :      Union[str, Any]       = {}






						@property
						def      __snake_case      (		self  :int				)      ->int:
										return len(self.encoder				)






						def      __snake_case      (		self  :Optional[Any]				)      ->Dict:
										return dict(self.encoder			, **self.added_tokens_encoder				)






						def      __snake_case      (		self  :List[str]			, __magic_name__  :str				)      ->str:
										if token in self.cache:
														return self.cache[token]
										lowercase    :      Dict       = re.sub("""([.,!?()])"""			, r""" \1"""			, __magic_name__				)
										lowercase    :      Tuple       = re.sub("""(')"""			, r""" \1 """			, __magic_name__				)
										lowercase    :      List[str]       = re.sub(r"""\s{2,}"""			, """ """			, __magic_name__				)
										if "\n" in token:
														lowercase    :      int       = token.replace("""\n"""			, """ __newln__"""				)

										lowercase    :      Tuple       = token.split(""" """				)
										lowercase    :      Dict       = []
										for token in tokens:
														if not len(__magic_name__				):
																		continue

														lowercase    :      List[Any]       = token.lower()
														lowercase    :      int       = tuple(__magic_name__				)
														lowercase    :      List[str]       = tuple(list(word[:-1]				) + [word[-1] + """</w>"""]				)
														lowercase    :      List[str]       = get_pairs(__magic_name__				)

														if not pairs:
																		words.append(__magic_name__				)
																		continue

														while True:
																		lowercase    :      Optional[Any]       = min(__magic_name__			, key=lambda __magic_name__				: self.bpe_ranks.get(__magic_name__			, float("""inf"""				)				)				)
																		if bigram not in self.bpe_ranks:
																						break
																		lowercase      ,      lowercase    :      List[str]       = bigram
																		lowercase    :      Dict       = []
																		lowercase    :      Optional[int]       = 0

																		while i < len(__magic_name__				):
																						try:
																										lowercase    :      Optional[Any]       = word.index(__magic_name__			, __magic_name__				)
																										new_word.extend(word[i:j]				)
																										lowercase    :      Union[str, Any]       = j
																						except ValueError:
																										new_word.extend(word[i:]				)
																										break

																						if word[i] == first and i < len(__magic_name__				) - 1 and word[i + 1] == second:
																										new_word.append(first + second				)
																										i += 2
																						else:
																										new_word.append(word[i]				)
																										i += 1
																		lowercase    :      str       = tuple(__magic_name__				)
																		lowercase    :      Optional[Any]       = new_word
																		if len(__magic_name__				) == 1:
																						break
																		else:
																						lowercase    :      List[Any]       = get_pairs(__magic_name__				)
														lowercase    :      int       = """@@ """.join(__magic_name__				)
														lowercase    :      int       = word[:-4]

														lowercase    :      List[str]       = word
														words.append(__magic_name__				)
										return " ".join(__magic_name__				)






						def      __snake_case      (		self  :List[str]			, __magic_name__  :str				)      ->List[str]:
										lowercase    :      str       = []

										lowercase    :      Dict       = re.findall(r"""\S+\n?"""			, __magic_name__				)

										for token in words:
														split_tokens.extend(list(self.bpe(__magic_name__				).split(""" """				)				)				)
										return split_tokens






						def      __snake_case      (		self  :int			, __magic_name__  :str				)      ->int:
										lowercase    :      Tuple       = token.lower()
										return self.encoder.get(__magic_name__			, self.encoder.get(self.unk_token				)				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :int				)      ->str:
										return self.decoder.get(__magic_name__			, self.unk_token				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :List[str]				)      ->str:
										lowercase    :      Tuple       = """ """.join(__magic_name__				).replace("""@@ """			, """"""				).strip()
										return out_string






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
										lowercase    :      List[str]       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

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

										lowercase    :      Any       = 0
										with open(__magic_name__			, """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 __magic_name__				: 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(__magic_name__				) + """\n"""				)
																		index += 1

										return vocab_file, merge_file
