
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

__snake_case								=  collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= numpy.dtype(numpy.uintaa		).newbyteorder('>'		)
					return numpy.frombuffer(bytestream.read(4		)	,	dtype=UpperCamelCase_		)[0]






@deprecated(UpperCamelCase_	,	'Please use tf.data to implement this functionality.'		)
def 							_lowercase							(     UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


					print('Extracting'	,	f.name		)
					with gzip.GzipFile(fileobj=UpperCamelCase_		) as bytestream:
										SCREAMING_SNAKE_CASE__													= _readaa(UpperCamelCase_		)
										if magic != 2051:
															raise ValueError(
															    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)		)
										SCREAMING_SNAKE_CASE__													= _readaa(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= _readaa(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= _readaa(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= bytestream.read(rows * cols * num_images		)
										SCREAMING_SNAKE_CASE__													= numpy.frombuffer(UpperCamelCase_	,	dtype=numpy.uinta		)
										SCREAMING_SNAKE_CASE__													= data.reshape(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	1		)
										return data






@deprecated(UpperCamelCase_	,	'Please use tf.one_hot on tensors.'		)
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= labels_dense.shape[0]
					SCREAMING_SNAKE_CASE__													= numpy.arange(UpperCamelCase_		) * num_classes
					SCREAMING_SNAKE_CASE__													= numpy.zeros((num_labels, num_classes)		)
					SCREAMING_SNAKE_CASE__													= 1
					return labels_one_hot






@deprecated(UpperCamelCase_	,	'Please use tf.data to implement this functionality.'		)
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_=False	,	UpperCamelCase_=10		)		->		Union[str, Any]:
					'''simple docstring'''


					print('Extracting'	,	f.name		)
					with gzip.GzipFile(fileobj=UpperCamelCase_		) as bytestream:
										SCREAMING_SNAKE_CASE__													= _readaa(UpperCamelCase_		)
										if magic != 2049:
															raise ValueError(
															    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)		)
										SCREAMING_SNAKE_CASE__													= _readaa(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= bytestream.read(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= numpy.frombuffer(UpperCamelCase_	,	dtype=numpy.uinta		)
										if one_hot:
															return _dense_to_one_hot(UpperCamelCase_	,	UpperCamelCase_		)
										return labels




class 						lowercase__  :
	@deprecated(
	    UpperCAmelCase_       ,   'Please use alternatives such as official/mnist/_DataSet.py'
	    ' from tensorflow/models.'       ,   )
	def __init__(			self							:   Tuple       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   str=False       ,   UpperCAmelCase_							:   List[Any]=False       ,   UpperCAmelCase_							:   int=dtypes.floataa       ,   UpperCAmelCase_							:   Tuple=True       ,   UpperCAmelCase_							:   Optional[Any]=None       ,   ):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= random_seed.get_seed(UpperCAmelCase_							)
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda							)
						SCREAMING_SNAKE_CASE__													= dtypes.as_dtype(UpperCAmelCase_							).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
											raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype							)
						if fake_data:
											SCREAMING_SNAKE_CASE__													= 10000
											SCREAMING_SNAKE_CASE__													= one_hot
						else:
											assert (
											    images.shape[0] == labels.shape[0]
											), F'images.shape: {images.shape} labels.shape: {labels.shape}'
											SCREAMING_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
																SCREAMING_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].
																SCREAMING_SNAKE_CASE__													= images.astype(numpy.floataa							)
																SCREAMING_SNAKE_CASE__													= numpy.multiply(UpperCAmelCase_       ,   1.0 / 255.0							)
						SCREAMING_SNAKE_CASE__													= images
						SCREAMING_SNAKE_CASE__													= labels
						SCREAMING_SNAKE_CASE__													= 0
						SCREAMING_SNAKE_CASE__													= 0
	@property
	def      A_				(			self							:   int							):
						return self._images
	@property
	def      A_				(			self							:   Optional[Any]							):
						return self._labels
	@property
	def      A_				(			self							:   Any							):
						return self._num_examples
	@property
	def      A_				(			self							:   int							):
						return self._epochs_completed





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






@deprecated(UpperCamelCase_	,	'Please write your own downloading logic.'		)
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					if not gfile.Exists(UpperCamelCase_		):
										gfile.MakeDirs(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= os.path.join(UpperCamelCase_	,	UpperCamelCase_		)
					if not gfile.Exists(UpperCamelCase_		):
										urllib.request.urlretrieve(UpperCamelCase_	,	UpperCamelCase_		)  # noqa: S310
										with gfile.GFile(UpperCamelCase_		) as f:
															SCREAMING_SNAKE_CASE__													= f.size()
										print('Successfully downloaded'	,	UpperCamelCase_	,	UpperCamelCase_	,	'bytes.'		)
					return filepath






@deprecated(
    UpperCamelCase_	,	'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'		)
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_=False	,	UpperCamelCase_=False	,	UpperCamelCase_=dtypes.floataa	,	UpperCamelCase_=True	,	UpperCamelCase_=5000	,	UpperCamelCase_=None	,	UpperCamelCase_=DEFAULT_SOURCE_URL	,	)		->		Any:
					'''simple docstring'''


					if fake_data:

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

										SCREAMING_SNAKE_CASE__													= fake()
										SCREAMING_SNAKE_CASE__													= fake()
										SCREAMING_SNAKE_CASE__													= fake()
										return _Datasets(train=UpperCamelCase_	,	validation=UpperCamelCase_	,	test=UpperCamelCase_		)

					if not source_url:  # empty string check
										SCREAMING_SNAKE_CASE__													= DEFAULT_SOURCE_URL

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

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

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

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

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

					if not 0 <= validation_size <= len(UpperCamelCase_		):
										SCREAMING_SNAKE_CASE__													= (
										    'Validation size should be between 0 and '
										    F'{len(UpperCamelCase_		)}. Received: {validation_size}.'
										)
										raise ValueError(UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= train_images[:validation_size]
					SCREAMING_SNAKE_CASE__													= train_labels[:validation_size]
					SCREAMING_SNAKE_CASE__													= train_images[validation_size:]
					SCREAMING_SNAKE_CASE__													= train_labels[validation_size:]

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

					SCREAMING_SNAKE_CASE__													= _DataSet(UpperCamelCase_	,	UpperCamelCase_	,	**UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= _DataSet(UpperCamelCase_	,	UpperCamelCase_	,	**UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= _DataSet(UpperCamelCase_	,	UpperCamelCase_	,	**UpperCamelCase_		)

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







import os
from typing import List, Optional, Union

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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """vocab.txt"""}

__snake_case								=  {
    """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""",
    },
}

__snake_case								=  {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					with open(UpperCamelCase_	,	'r'		) as f:
										SCREAMING_SNAKE_CASE__													= f.read().splitlines()
										return [l.strip() for l in lines]




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Tuple				       =VOCAB_FILES_NAMES
	A__    :   Union[str, Any]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Any				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Any				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple="<unk>"       ,   UpperCAmelCase_							:   Optional[Any]="<cls>"       ,   UpperCAmelCase_							:   List[Any]="<pad>"       ,   UpperCAmelCase_							:   Optional[int]="<mask>"       ,   UpperCAmelCase_							:   Optional[int]="<eos>"       ,   **UpperCAmelCase_							:   Optional[int]       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= load_vocab_file(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= dict(enumerate(self.all_tokens							)							)
						SCREAMING_SNAKE_CASE__													= {tok: ind for ind, tok in enumerate(self.all_tokens							)}
						SCREAMING_SNAKE_CASE__													= unk_token
						SCREAMING_SNAKE_CASE__													= cls_token
						SCREAMING_SNAKE_CASE__													= pad_token
						SCREAMING_SNAKE_CASE__													= mask_token
						SCREAMING_SNAKE_CASE__													= eos_token
						SCREAMING_SNAKE_CASE__													= self.all_tokens
						self._create_trie(self.unique_no_split_tokens							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   **UpperCAmelCase_							:   List[Any]							):
						return text.split()
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Optional[Any]=False							):
						return len(self._id_to_token							)
	def      A_				(			self							:   Union[str, Any]							):
						return {token: i for i, token in enumerate(self.all_tokens							)}
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [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      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   List       ,   UpperCAmelCase_							:   Optional[List] = None       ,   UpperCAmelCase_							:   bool = False							):
						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]
						SCREAMING_SNAKE_CASE__													= [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						if token_ids_a is not None:
											mask += [0] * len(UpperCAmelCase_							) + [1]
						return mask
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= os.path.join(UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'							)
						with open(UpperCAmelCase_       ,   'w'							) as f:
											f.write('\n'.join(self.all_tokens							)							)
						return (vocab_file,)
	@property
	def      A_				(			self							:   int							):
						return self.get_vocab_size(with_added_tokens=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[List[str], List[AddedToken]]       ,   UpperCAmelCase_							:   bool = False							):
						return super()._add_tokens(UpperCAmelCase_       ,   special_tokens=UpperCAmelCase_							)





