


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

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					print("""Extracting"""						,							f.name  )
					with gzip.GzipFile(fileobj=_UpperCAmelCase  ) as bytestream:
										lowercase_  							=			_readaa(_UpperCAmelCase  )
										if magic != 2_0_5_1:
															raise ValueError(
															    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)  )
										lowercase_  							=			_readaa(_UpperCAmelCase  )
										lowercase_  							=			_readaa(_UpperCAmelCase  )
										lowercase_  							=			_readaa(_UpperCAmelCase  )
										lowercase_  							=			bytestream.read(rows * cols * num_images  )
										lowercase_  							=			numpy.frombuffer(_UpperCAmelCase						,							dtype=numpy.uinta  )
										lowercase_  							=			data.reshape(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							1  )
										return data


@deprecated(_UpperCAmelCase						,							"""Please use tf.one_hot on tensors."""  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			labels_dense.shape[0]
					lowercase_  							=			numpy.arange(_UpperCAmelCase  ) * num_classes
					lowercase_  							=			numpy.zeros((num_labels, num_classes)  )
					lowercase_  							=			1
					return labels_one_hot


@deprecated(_UpperCAmelCase						,							"""Please use tf.data to implement this functionality."""  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=False						,							UpperCAmelCase__=1_0  ):
					print("""Extracting"""						,							f.name  )
					with gzip.GzipFile(fileobj=_UpperCAmelCase  ) as bytestream:
										lowercase_  							=			_readaa(_UpperCAmelCase  )
										if magic != 2_0_4_9:
															raise ValueError(
															    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)  )
										lowercase_  							=			_readaa(_UpperCAmelCase  )
										lowercase_  							=			bytestream.read(_UpperCAmelCase  )
										lowercase_  							=			numpy.frombuffer(_UpperCAmelCase						,							dtype=numpy.uinta  )
										if one_hot:
															return _dense_to_one_hot(_UpperCAmelCase						,							_UpperCAmelCase  )
										return labels





class        UpperCamelCase__   :

					@deprecated(
					    __SCREAMING_SNAKE_CASE							,					"""Please use alternatives such as official/mnist/_DataSet.py"""
					    """ from tensorflow/models."""							,					)
					def __init__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Any=False							,					UpperCamelCase__       :   Optional[int]=False							,					UpperCamelCase__       :   Any=dtypes.floataa							,					UpperCamelCase__       :   List[str]=True							,					UpperCamelCase__       :   Any=None							,					):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			random_seed.get_seed(__SCREAMING_SNAKE_CASE       )
										# 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_  							=			dtypes.as_dtype(__SCREAMING_SNAKE_CASE       ).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_  							=			10_000
															lowercase_  							=			one_hot
										else:
															assert (
															    images.shape[0] == labels.shape[0]
															), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
															lowercase_  							=			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_  							=			images.reshape(
																				    images.shape[0]							,					images.shape[1] * images.shape[2]       )
															if dtype == dtypes.floataa:
																				# Convert from [0, 255] -> [0.0, 1.0].
																				lowercase_  							=			images.astype(numpy.floataa       )
																				lowercase_  							=			numpy.multiply(__SCREAMING_SNAKE_CASE							,					1.0 / 255.0       )
										lowercase_  							=			images
										lowercase_  							=			labels
										lowercase_  							=			0
										lowercase_  							=			0

					@property
					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										return self._images

					@property
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										return self._labels

					@property
					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return self._num_examples

					@property
					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										return self._epochs_completed







					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Tuple=False							,					UpperCamelCase__       :   Optional[int]=True       ):






										'''simple docstring'''






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



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



@deprecated(
    _UpperCAmelCase						,							"""Please use alternatives such as:""" """ tensorflow_datasets.load(\'mnist\')"""  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=False						,							UpperCAmelCase__=False						,							UpperCAmelCase__=dtypes.floataa						,							UpperCAmelCase__=True						,							UpperCAmelCase__=5_0_0_0						,							UpperCAmelCase__=None						,							UpperCAmelCase__=DEFAULT_SOURCE_URL						,							):
					if fake_data:

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

										lowercase_  							=			fake()
										lowercase_  							=			fake()
										lowercase_  							=			fake()
										return _Datasets(train=_UpperCAmelCase						,							validation=_UpperCAmelCase						,							test=_UpperCAmelCase  )

					if not source_url:  # empty string check
										lowercase_  							=			DEFAULT_SOURCE_URL

					lowercase_  							=			"""train-images-idx3-ubyte.gz"""
					lowercase_  							=			"""train-labels-idx1-ubyte.gz"""
					lowercase_  							=			"""t10k-images-idx3-ubyte.gz"""
					lowercase_  							=			"""t10k-labels-idx1-ubyte.gz"""

					lowercase_  							=			_maybe_download(
					    _UpperCAmelCase						,							_UpperCAmelCase						,							source_url + train_images_file  )
					with gfile.Open(_UpperCAmelCase						,							"""rb"""  ) as f:
										lowercase_  							=			_extract_images(_UpperCAmelCase  )

					lowercase_  							=			_maybe_download(
					    _UpperCAmelCase						,							_UpperCAmelCase						,							source_url + train_labels_file  )
					with gfile.Open(_UpperCAmelCase						,							"""rb"""  ) as f:
										lowercase_  							=			_extract_labels(_UpperCAmelCase						,							one_hot=_UpperCAmelCase  )

					lowercase_  							=			_maybe_download(
					    _UpperCAmelCase						,							_UpperCAmelCase						,							source_url + test_images_file  )
					with gfile.Open(_UpperCAmelCase						,							"""rb"""  ) as f:
										lowercase_  							=			_extract_images(_UpperCAmelCase  )

					lowercase_  							=			_maybe_download(
					    _UpperCAmelCase						,							_UpperCAmelCase						,							source_url + test_labels_file  )
					with gfile.Open(_UpperCAmelCase						,							"""rb"""  ) as f:
										lowercase_  							=			_extract_labels(_UpperCAmelCase						,							one_hot=_UpperCAmelCase  )

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

					lowercase_  							=			train_images[:validation_size]
					lowercase_  							=			train_labels[:validation_size]
					lowercase_  							=			train_images[validation_size:]
					lowercase_  							=			train_labels[validation_size:]

					lowercase_  							=			{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

					lowercase_  							=			_DataSet(_UpperCAmelCase						,							_UpperCAmelCase						,							**_UpperCAmelCase  )
					lowercase_  							=			_DataSet(_UpperCAmelCase						,							_UpperCAmelCase						,							**_UpperCAmelCase  )
					lowercase_  							=			_DataSet(_UpperCAmelCase						,							_UpperCAmelCase						,							**_UpperCAmelCase  )

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






from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					  =     logging.get_logger(__name__)

# TODO Update this
a					  =     {
    'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Tuple						=   'esm'

					def __init__(     self       :   Optional[Any]							,					UpperCamelCase__       :   Dict=None							,					UpperCamelCase__       :   Tuple=None							,					UpperCamelCase__       :   List[Any]=None							,					UpperCamelCase__       :   List[Any]=768							,					UpperCamelCase__       :   Tuple=12							,					UpperCamelCase__       :   Optional[int]=12							,					UpperCamelCase__       :   Dict=3_072							,					UpperCamelCase__       :   Union[str, Any]=0.1							,					UpperCamelCase__       :   Dict=0.1							,					UpperCamelCase__       :   Optional[int]=1_026							,					UpperCamelCase__       :   Any=0.02							,					UpperCamelCase__       :   Dict=1e-12							,					UpperCamelCase__       :   List[str]="absolute"							,					UpperCamelCase__       :   Union[str, Any]=True							,					UpperCamelCase__       :   Tuple=None							,					UpperCamelCase__       :   Union[str, Any]=False							,					UpperCamelCase__       :   Optional[int]=False							,					UpperCamelCase__       :   Dict=None							,					UpperCamelCase__       :   str=None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										super().__init__(pad_token_id=UpperCamelCase__							,					mask_token_id=UpperCamelCase__							,					**UpperCamelCase__       )

										lowercase_  							=			vocab_size
										lowercase_  							=			hidden_size
										lowercase_  							=			num_hidden_layers
										lowercase_  							=			num_attention_heads
										lowercase_  							=			intermediate_size
										lowercase_  							=			hidden_dropout_prob
										lowercase_  							=			attention_probs_dropout_prob
										lowercase_  							=			max_position_embeddings
										lowercase_  							=			initializer_range
										lowercase_  							=			layer_norm_eps
										lowercase_  							=			position_embedding_type
										lowercase_  							=			use_cache
										lowercase_  							=			emb_layer_norm_before
										lowercase_  							=			token_dropout
										lowercase_  							=			is_folding_model
										if is_folding_model:
															if esmfold_config is None:
																				logger.info("""No esmfold_config supplied for folding model, using default values."""       )
																				lowercase_  							=			EsmFoldConfig()
															elif isinstance(UpperCamelCase__							,					UpperCamelCase__       ):
																				lowercase_  							=			EsmFoldConfig(**UpperCamelCase__       )
															lowercase_  							=			esmfold_config
															if vocab_list is None:
																				logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""       )
																				lowercase_  							=			get_default_vocab_list()
															else:
																				lowercase_  							=			vocab_list
										else:
															lowercase_  							=			None
															lowercase_  							=			None
										if self.esmfold_config is not None and getattr(self.esmfold_config							,					"""use_esm_attn_map"""							,					UpperCamelCase__       ):
															raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""       )







					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			super().to_dict()
										if isinstance(self.esmfold_config							,					UpperCamelCase__       ):
															lowercase_  							=			self.esmfold_config.to_dict()
										return output







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   None
					__SCREAMING_SNAKE_CASE       :  bool						=   True
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  float						=   0

					__SCREAMING_SNAKE_CASE       :  bool						=   True
					__SCREAMING_SNAKE_CASE       :  bool						=   False

					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  "TrunkConfig"						=   None

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										if self.trunk is None:
															lowercase_  							=			TrunkConfig()
										elif isinstance(self.trunk							,					UpperCamelCase__       ):
															lowercase_  							=			TrunkConfig(**self.trunk       )







					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			asdict(self       )
										lowercase_  							=			self.trunk.to_dict()
										return output







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  int						=   48
					__SCREAMING_SNAKE_CASE       :  int						=   1024
					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  int						=   32
					__SCREAMING_SNAKE_CASE       :  int						=   32
					__SCREAMING_SNAKE_CASE       :  int						=   32
					__SCREAMING_SNAKE_CASE       :  float						=   0
					__SCREAMING_SNAKE_CASE       :  float						=   0
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  int						=   4
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   128
					__SCREAMING_SNAKE_CASE       :  "StructureModuleConfig"						=   None

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										if self.structure_module is None:
															lowercase_  							=			StructureModuleConfig()
										elif isinstance(self.structure_module							,					UpperCamelCase__       ):
															lowercase_  							=			StructureModuleConfig(**self.structure_module       )

										if self.max_recycles <= 0:
															raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.'''       )
										if self.sequence_state_dim % self.sequence_state_dim != 0:
															raise ValueError(
															    """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
															    F''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''       )
										if self.pairwise_state_dim % self.pairwise_state_dim != 0:
															raise ValueError(
															    """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
															    F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''       )

										lowercase_  							=			self.sequence_state_dim // self.sequence_head_width
										lowercase_  							=			self.pairwise_state_dim // self.pairwise_head_width

										if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
															raise ValueError(
															    """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
															    F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''       )
										if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
															raise ValueError(
															    """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
															    F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''       )
										if self.pairwise_state_dim % 2 != 0:
															raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''       )

										if self.dropout >= 0.4:
															raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.'''       )







					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			asdict(self       )
										lowercase_  							=			self.structure_module.to_dict()
										return output







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  int						=   384
					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  int						=   16
					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  int						=   12
					__SCREAMING_SNAKE_CASE       :  int						=   4
					__SCREAMING_SNAKE_CASE       :  int						=   8
					__SCREAMING_SNAKE_CASE       :  float						=   0.1
					__SCREAMING_SNAKE_CASE       :  int						=   8
					__SCREAMING_SNAKE_CASE       :  int						=   1
					__SCREAMING_SNAKE_CASE       :  int						=   2
					__SCREAMING_SNAKE_CASE       :  int						=   7
					__SCREAMING_SNAKE_CASE       :  int						=   10
					__SCREAMING_SNAKE_CASE       :  float						=   1e-8
					__SCREAMING_SNAKE_CASE       :  float						=   1e5

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return asdict(self       )



def 			UpperCAmelCase_				(      ):
					return (
					    "<cls>",
					    "<pad>",
					    "<eos>",
					    "<unk>",
					    "L",
					    "A",
					    "G",
					    "V",
					    "S",
					    "E",
					    "R",
					    "T",
					    "I",
					    "D",
					    "P",
					    "K",
					    "Q",
					    "N",
					    "F",
					    "Y",
					    "M",
					    "H",
					    "W",
					    "C",
					    "X",
					    "B",
					    "U",
					    "Z",
					    "O",
					    ".",
					    "-",
					    "<null_1>",
					    "<mask>",
					)


