






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	(							snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :int 							=    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 	UpperCamelCase	(							snake_case__  : str	):




     '''simple docstring'''






     print("""Extracting"""						,f.name	)
     with gzip.GzipFile(fileobj=snake_case__	) as bytestream:
          __snake_case      :str 							=    _readaa(snake_case__	)
          if magic != 2051:
               raise ValueError(
                   """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)	)
          __snake_case      :List[str] 							=    _readaa(snake_case__	)
          __snake_case      :Any 							=    _readaa(snake_case__	)
          __snake_case      :str 							=    _readaa(snake_case__	)
          __snake_case      :str 							=    bytestream.read(rows * cols * num_images	)
          __snake_case      :List[Any] 							=    numpy.frombuffer(snake_case__						,dtype=numpy.uinta	)
          __snake_case      :Optional[int] 							=    data.reshape(snake_case__						,snake_case__						,snake_case__						,1	)
          return data






@deprecated(snake_case__						,"""Please use tf.one_hot on tensors."""	)
def 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : Optional[Any]	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    labels_dense.shape[0]
     __snake_case      :Dict 							=    numpy.arange(snake_case__	) * num_classes
     __snake_case      :Union[str, Any] 							=    numpy.zeros((num_labels, num_classes)	)
     __snake_case      :List[Any] 							=    1
     return labels_one_hot






@deprecated(snake_case__						,"""Please use tf.data to implement this functionality."""	)
def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : Union[str, Any]=False						,snake_case__  : str=10	):




     '''simple docstring'''






     print("""Extracting"""						,f.name	)
     with gzip.GzipFile(fileobj=snake_case__	) as bytestream:
          __snake_case      :List[Any] 							=    _readaa(snake_case__	)
          if magic != 2049:
               raise ValueError(
                   """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)	)
          __snake_case      :int 							=    _readaa(snake_case__	)
          __snake_case      :Dict 							=    bytestream.read(snake_case__	)
          __snake_case      :Any 							=    numpy.frombuffer(snake_case__						,dtype=numpy.uinta	)
          if one_hot:
               return _dense_to_one_hot(snake_case__						,snake_case__	)
          return labels




class       snake_case__ :

       '''simple docstring'''



       @deprecated(
           lowerCamelCase_      ,    """Please use alternatives such as official/mnist/_DataSet.py"""
           """ from tensorflow/models."""      ,    )
       def __init__(				self      ,    a__      ,    a__      ,    a__=False      ,    a__=False      ,    a__=dtypes.floataa      ,    a__=True      ,    a__=None      ,    )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case							,			__snake_case      :Tuple 							=    random_seed.get_seed(lowerCamelCase_     )
            # 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      :List[Any] 							=    dtypes.as_dtype(lowerCamelCase_     ).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      :Any 							=    1_00_00
                 __snake_case      :Tuple 							=    one_hot
            else:
                 assert (
                     images.shape[0] == labels.shape[0]
                 ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
                 __snake_case      :Optional[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
                      __snake_case      :Optional[Any] 							=    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      :Optional[Any] 							=    images.astype(numpy.floataa     )
                      __snake_case      :Optional[int] 							=    numpy.multiply(lowerCamelCase_      ,    1.0 / 2_55.0     )
            __snake_case      :Optional[int] 							=    images
            __snake_case      :Tuple 							=    labels
            __snake_case      :Tuple 							=    0
            __snake_case      :Tuple 							=    0



       @property
       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            return self._images



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return self._labels



       @property
       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            return self._num_examples



       @property
       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            return self._epochs_completed



       def        __lowercase    (				self      ,    a__      ,    a__=False      ,    a__=True     )			->   Optional[Any]:






            '''simple docstring'''





            if fake_data:
                 __snake_case      :str 							=    [1] * 7_84
                 __snake_case      :int 							=    [1] + [0] * 9 if self.one_hot else 0
                 return (
                     [fake_image for _ in range(lowerCamelCase_     )],
                     [fake_label for _ in range(lowerCamelCase_     )],
                 )
            __snake_case      :Tuple 							=    self._index_in_epoch
            # Shuffle for the first epoch
            if self._epochs_completed == 0 and start == 0 and shuffle:
                 __snake_case      :Tuple 							=    numpy.arange(self._num_examples     )
                 numpy.random.shuffle(lowerCamelCase_     )
                 __snake_case      :List[Any] 							=    self.images[perma]
                 __snake_case      :Union[str, Any] 							=    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      :Optional[int] 							=    self._num_examples - start
                 __snake_case      :Tuple 							=    self._images[start : self._num_examples]
                 __snake_case      :int 							=    self._labels[start : self._num_examples]
                 # Shuffle the data
                 if shuffle:
                      __snake_case      :Union[str, Any] 							=    numpy.arange(self._num_examples     )
                      numpy.random.shuffle(lowerCamelCase_     )
                      __snake_case      :Any 							=    self.images[perm]
                      __snake_case      :int 							=    self.labels[perm]
                 # Start next epoch
                 __snake_case      :Union[str, Any] 							=    0
                 __snake_case      :Optional[int] 							=    batch_size - rest_num_examples
                 __snake_case      :Tuple 							=    self._index_in_epoch
                 __snake_case      :Union[str, Any] 							=    self._images[start:end]
                 __snake_case      :List[Any] 							=    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      :Union[str, Any] 							=    self._index_in_epoch
                 return self._images[start:end], self._labels[start:end]






@deprecated(snake_case__						,"""Please write your own downloading logic."""	)
def 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : int						,snake_case__  : List[Any]	):




     '''simple docstring'''






     if not gfile.Exists(snake_case__	):
          gfile.MakeDirs(snake_case__	)
     __snake_case      :Optional[Any] 							=    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      :Optional[Any] 							=    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 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : Tuple=False						,snake_case__  : Any=False						,snake_case__  : Dict=dtypes.floataa						,snake_case__  : Any=True						,snake_case__  : List[Any]=5000						,snake_case__  : str=None						,snake_case__  : Tuple=DEFAULT_SOURCE_URL						,):




     '''simple docstring'''






     if fake_data:

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

          __snake_case      :int 							=    fake()
          __snake_case      :Tuple 							=    fake()
          __snake_case      :str 							=    fake()
          return _Datasets(train=snake_case__						,validation=snake_case__						,test=snake_case__	)

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

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

     __snake_case      :Union[str, Any] 							=    _maybe_download(
         snake_case__						,snake_case__						,source_url + train_images_file	)
     with gfile.Open(snake_case__						,"""rb"""	) as f:
          __snake_case      :str 							=    _extract_images(snake_case__	)

     __snake_case      :List[Any] 							=    _maybe_download(
         snake_case__						,snake_case__						,source_url + train_labels_file	)
     with gfile.Open(snake_case__						,"""rb"""	) as f:
          __snake_case      :Union[str, Any] 							=    _extract_labels(snake_case__						,one_hot=snake_case__	)

     __snake_case      :List[Any] 							=    _maybe_download(
         snake_case__						,snake_case__						,source_url + test_images_file	)
     with gfile.Open(snake_case__						,"""rb"""	) as f:
          __snake_case      :Tuple 							=    _extract_images(snake_case__	)

     __snake_case      :Optional[int] 							=    _maybe_download(
         snake_case__						,snake_case__						,source_url + test_labels_file	)
     with gfile.Open(snake_case__						,"""rb"""	) as f:
          __snake_case      :Union[str, Any] 							=    _extract_labels(snake_case__						,one_hot=snake_case__	)

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

     __snake_case      :Optional[int] 							=    train_images[:validation_size]
     __snake_case      :Optional[int] 							=    train_labels[:validation_size]
     __snake_case      :List[str] 							=    train_images[validation_size:]
     __snake_case      :Optional[Any] 							=    train_labels[validation_size:]

     __snake_case      :Optional[Any] 							=    {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

     __snake_case      :Optional[int] 							=    _DataSet(snake_case__						,snake_case__						,**snake_case__	)
     __snake_case      :int 							=    _DataSet(snake_case__						,snake_case__						,**snake_case__	)
     __snake_case      :Tuple 							=    _DataSet(snake_case__						,snake_case__						,**snake_case__	)

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






'''simple docstring'''






import os
from typing import List, Optional, Union

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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{'''vocab_file''': '''vocab.txt'''}

__UpperCAmelCase       							=				{
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

__UpperCAmelCase       							=				{
    '''facebook/esm2_t6_8M_UR50D''': 1_024,
    '''facebook/esm2_t12_35M_UR50D''': 1_024,
}


def    _snake_case  (	A			)		->       Optional[Any]:
							with open(A					,       '''r'''			) as f:
														lowerCAmelCase__			  =      f.read().splitlines()
														return [l.strip() for l in lines]






class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= VOCAB_FILES_NAMES
							lowercase__     :  List[Any]      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Union[str, Any]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__     :  Union[str, Any]      		= ["input_ids", "attention_mask"]







							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_="<unk>"   ,       lowerCamelCase_="<cls>"   ,       lowerCamelCase_="<pad>"   ,       lowerCamelCase_="<mask>"   ,       lowerCamelCase_="<eos>"   ,       **lowerCamelCase_   ,       )			->				Tuple:
														super().__init__(**lowerCamelCase_  )
														lowerCAmelCase__			  =      load_vocab_file(lowerCamelCase_  )
														lowerCAmelCase__			  =      dict(enumerate(self.all_tokens  )  )
														lowerCAmelCase__			  =      {tok: ind for ind, tok in enumerate(self.all_tokens  )}
														lowerCAmelCase__			  =      unk_token
														lowerCAmelCase__			  =      cls_token
														lowerCAmelCase__			  =      pad_token
														lowerCAmelCase__			  =      mask_token
														lowerCAmelCase__			  =      eos_token
														lowerCAmelCase__			  =      self.all_tokens
														self._create_trie(self.unique_no_split_tokens  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				Union[str, Any]:
														return text.split()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=False  )			->				Dict:
														return len(self._id_to_token  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return {token: i for i, token in enumerate(self.all_tokens  )}







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.cls_token_id]
														lowerCAmelCase__			  =      [self.eos_token_id]  # No sep token in ESM vocabulary
														if token_ids_a is None:
																					if self.eos_token_id is None:
																												return cls + token_ids_a
																					else:
																												return cls + token_ids_a + sep
														elif self.eos_token_id is None:
																					raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''  )
														return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = False  )			->				List[int]:
														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''  )

																					return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
														lowerCAmelCase__			  =      [1] + ([0] * len(lowerCamelCase_  )) + [1]
														if token_ids_a is not None:
																					mask += [0] * len(lowerCamelCase_  ) + [1]
														return mask







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      os.path.join(lowerCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''  )
														with open(lowerCamelCase_   ,       '''w'''  ) as f:
																					f.write('''\n'''.join(self.all_tokens  )  )
														return (vocab_file,)







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return self.get_vocab_size(with_added_tokens=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False  )			->				int:
														return super()._add_tokens(lowerCamelCase_   ,       special_tokens=lowerCamelCase_  )