"""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

__lowercase		:		List[Any]														=							collections.namedtuple("_Datasets", ["train", "validation", "test"])

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





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							numpy.dtype(numpy.uintaa).newbyteorder('''>''')
     return numpy.frombuffer(bytestream.read(4),      dtype=snake_case)[0]





@deprecated(snake_case,      '''Please use tf.data to implement this functionality.''')
def    SCREAMING_SNAKE_CASE       (     snake_case):
     print('''Extracting''',      f.name)
     with gzip.GzipFile(fileobj=snake_case) as bytestream:
          __snake_case				=							_readaa(snake_case)
          if magic != 20_51:
               raise ValueError(
                   '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name))
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							bytestream.read(rows * cols * num_images)
          __snake_case				=							numpy.frombuffer(snake_case,      dtype=numpy.uinta)
          __snake_case				=							data.reshape(snake_case,      snake_case,      snake_case,      1)
          return data





@deprecated(snake_case,      '''Please use tf.one_hot on tensors.''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							labels_dense.shape[0]
     __snake_case				=							numpy.arange(snake_case) * num_classes
     __snake_case				=							numpy.zeros((num_labels, num_classes))
     __snake_case				=							1
     return labels_one_hot





@deprecated(snake_case,      '''Please use tf.data to implement this functionality.''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=False,      snake_case=10):
     print('''Extracting''',      f.name)
     with gzip.GzipFile(fileobj=snake_case) as bytestream:
          __snake_case				=							_readaa(snake_case)
          if magic != 20_49:
               raise ValueError(
                   '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name))
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							bytestream.read(snake_case)
          __snake_case				=							numpy.frombuffer(snake_case,      dtype=numpy.uinta)
          if one_hot:
               return _dense_to_one_hot(snake_case,      snake_case)
          return labels



class      _A	:




    """simple docstring"""




    @deprecated(
        A_	,      '''Please use alternatives such as official/mnist/_DataSet.py'''
        ''' from tensorflow/models.'''	,      )
    def __init__(		self							:     List[str]	,      A_							:     int	,      A_							:     Tuple	,      A_							:     Optional[int]=False	,      A_							:     int=False	,      A_							:     Dict=dtypes.floataa	,      A_							:     Tuple=True	,      A_							:     Any=None	,      )    ->       Any:
         __snake_case		,	__snake_case				=							random_seed.get_seed(A_    )
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seeda if seed is None else seeda    )
         __snake_case				=							dtypes.as_dtype(A_    ).base_dtype
         if dtype not in (dtypes.uinta, dtypes.floataa):
              raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype    )
         if fake_data:
              __snake_case				=							10_000
              __snake_case				=							one_hot
         else:
              assert (
                  images.shape[0] == labels.shape[0]
              ), f"images.shape: {images.shape} labels.shape: {labels.shape}"
              __snake_case				=							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
                   __snake_case				=							images.reshape(
                       images.shape[0]	,      images.shape[1] * images.shape[2]    )
              if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   __snake_case				=							images.astype(numpy.floataa    )
                   __snake_case				=							numpy.multiply(A_	,      1.0 / 2_55.0    )
         __snake_case				=							images
         __snake_case				=							labels
         __snake_case				=							0
         __snake_case				=							0
    @property
    def  lowercase					(		self							:     int    )    ->       Dict:
         return self._images
    @property
    def  lowercase					(		self							:     Dict    )    ->       Optional[int]:
         return self._labels
    @property
    def  lowercase					(		self							:     int    )    ->       Tuple:
         return self._num_examples
    @property
    def  lowercase					(		self							:     int    )    ->       Tuple:
         return self._epochs_completed







    def  lowercase					(		self							:     int	,      A_							:     Optional[int]	,      A_							:     Tuple=False	,      A_							:     Tuple=True    )    ->       List[Any]:
         if fake_data:
              __snake_case				=							[1] * 784
              __snake_case				=							[1] + [0] * 9 if self.one_hot else 0
              return (
                  [fake_image for _ in range(A_    )],
                  [fake_label for _ in range(A_    )],
              )
         __snake_case				=							self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
              __snake_case				=							numpy.arange(self._num_examples    )
              numpy.random.shuffle(A_    )
              __snake_case				=							self.images[perma]
              __snake_case				=							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
              __snake_case				=							self._num_examples - start
              __snake_case				=							self._images[start : self._num_examples]
              __snake_case				=							self._labels[start : self._num_examples]
              # Shuffle the data
              if shuffle:
                   __snake_case				=							numpy.arange(self._num_examples    )
                   numpy.random.shuffle(A_    )
                   __snake_case				=							self.images[perm]
                   __snake_case				=							self.labels[perm]
              # Start next epoch
              __snake_case				=							0
              __snake_case				=							batch_size - rest_num_examples
              __snake_case				=							self._index_in_epoch
              __snake_case				=							self._images[start:end]
              __snake_case				=							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
              __snake_case				=							self._index_in_epoch
              return self._images[start:end], self._labels[start:end]






@deprecated(snake_case,      '''Please write your own downloading logic.''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     if not gfile.Exists(snake_case):
          gfile.MakeDirs(snake_case)
     __snake_case				=							os.path.join(snake_case,      snake_case)
     if not gfile.Exists(snake_case):
          urllib.request.urlretrieve(snake_case,      snake_case)  # noqa: S310
          with gfile.GFile(snake_case) as f:
               __snake_case				=							f.size()
          print('''Successfully downloaded''',      snake_case,      snake_case,      '''bytes.''')
     return filepath





@deprecated(
    snake_case,      '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=False,      snake_case=False,      snake_case=dtypes.floataa,      snake_case=True,      snake_case=50_00,      snake_case=None,      snake_case=DEFAULT_SOURCE_URL,      ):
     if fake_data:

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

          __snake_case				=							fake()
          __snake_case				=							fake()
          __snake_case				=							fake()
          return _Datasets(train=snake_case,      validation=snake_case,      test=snake_case)

     if not source_url:  # empty string check
          __snake_case				=							DEFAULT_SOURCE_URL

     __snake_case				=							'''train-images-idx3-ubyte.gz'''
     __snake_case				=							'''train-labels-idx1-ubyte.gz'''
     __snake_case				=							'''t10k-images-idx3-ubyte.gz'''
     __snake_case				=							'''t10k-labels-idx1-ubyte.gz'''

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + train_images_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_images(snake_case)

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + train_labels_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_labels(snake_case,      one_hot=snake_case)

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + test_images_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_images(snake_case)

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + test_labels_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_labels(snake_case,      one_hot=snake_case)

     if not 0 <= validation_size <= len(snake_case):
          __snake_case				=							(
              '''Validation size should be between 0 and '''
              f"{len(snake_case)}. Received: {validation_size}."
          )
          raise ValueError(snake_case)

     __snake_case				=							train_images[:validation_size]
     __snake_case				=							train_labels[:validation_size]
     __snake_case				=							train_images[validation_size:]
     __snake_case				=							train_labels[validation_size:]

     __snake_case				=							{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

     __snake_case				=							_DataSet(snake_case,      snake_case,      **snake_case)
     __snake_case				=							_DataSet(snake_case,      snake_case,      **snake_case)
     __snake_case				=							_DataSet(snake_case,      snake_case,      **snake_case)

     return _Datasets(train=snake_case,      validation=snake_case,      test=snake_case)
"""simple docstring"""



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__lowercase		:		int														=							logging.get_logger(__name__)


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

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

__lowercase		:		List[str]														=							{"facebook/blenderbot-3B": 128}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					int							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Tuple							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					int							=					['''input_ids''', '''attention_mask''']
    UpperCamelCase_	:					str							=					BlenderbotTokenizer
    def __init__(		self							:     Union[str, Any]	,      A_							:     Any=None	,      A_							:     Optional[int]=None	,      A_							:     Optional[Any]=None	,      A_							:     Union[str, Any]="replace"	,      A_							:     Union[str, Any]="<s>"	,      A_							:     Union[str, Any]="</s>"	,      A_							:     Optional[int]="</s>"	,      A_							:     List[Any]="<s>"	,      A_							:     Union[str, Any]="<unk>"	,      A_							:     Any="<pad>"	,      A_							:     List[str]="<mask>"	,      A_							:     int=False	,      A_							:     Tuple=True	,      **A_							:     Optional[Any]	,      )    ->       int:
         super().__init__(
             A_	,      A_	,      tokenizer_file=A_	,      errors=A_	,      bos_token=A_	,      eos_token=A_	,      sep_token=A_	,      cls_token=A_	,      unk_token=A_	,      pad_token=A_	,      mask_token=A_	,      add_prefix_space=A_	,      trim_offsets=A_	,      **A_	,      )

         __snake_case				=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
              __snake_case				=							getattr(A_	,      pre_tok_state.pop('''type'''    )    )
              __snake_case				=							add_prefix_space
              __snake_case				=							pre_tok_class(**A_    )

         __snake_case				=							add_prefix_space

         __snake_case				=							'''post_processor'''
         __snake_case				=							getattr(self.backend_tokenizer	,      A_	,      A_    )
         if tokenizer_component_instance:
              __snake_case				=							json.loads(tokenizer_component_instance.__getstate__()    )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   __snake_case				=							tuple(state['''sep''']    )
              if "cls" in state:
                   __snake_case				=							tuple(state['''cls''']    )

              __snake_case				=							False

              if state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
                   __snake_case				=							add_prefix_space
                   __snake_case				=							True

              if state.get('''trim_offsets'''	,      A_    ) != trim_offsets:
                   __snake_case				=							trim_offsets
                   __snake_case				=							True

              if changes_to_apply:
                   __snake_case				=							getattr(A_	,      state.pop('''type'''    )    )
                   __snake_case				=							component_class(**A_    )
                   setattr(self.backend_tokenizer	,      A_	,      A_    )
    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''    )
              return None
         return str(self._mask_token    )
    @mask_token.setter
    def  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else value
         __snake_case				=							value
    def  lowercase					(		self							:     Any	,      *A_							:     List[str]	,      **A_							:     Optional[int]    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*A_	,      **A_    )
    def  lowercase					(		self							:     str	,      *A_							:     Tuple	,      **A_							:     str    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*A_	,      **A_    )
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         __snake_case				=							self._tokenizer.model.save(A_	,      name=A_    )
         return tuple(A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __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  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       Any:
         return token_ids_a + [self.eos_token_id]







    def  lowercase					(		self							:     Optional[Any]	,      A_							:     "Conversation"    )    ->       List[int]:
         __snake_case				=							[]
         for is_user, text in conversation.iter_texts():
              if is_user:
                   # We need to space prefix as it's being done within blenderbot
                   inputs.append(''' ''' + text    )
              else:
                   # Generated responses should contain them already.
                   inputs.append(A_    )

         __snake_case				=							'''  '''.join(A_    )
         __snake_case				=							self.encode(A_    )
         if len(A_    ) > self.model_max_length:
              __snake_case				=							input_ids[-self.model_max_length :]
              logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."    )
         return input_ids