




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


UpperCamelCase__ =						logging.getLogger(__name__)
@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  str
			lowercase__      :  List[str]
			lowercase__      :  Optional[List[str]]





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  List[int]
			lowercase__      :  List[int]
			lowercase__      :  Optional[List[int]]      =			None
			lowercase__      :  Optional[List[int]]      =			None





class 			_lowerCAmelCase   ( _lowercase			):






			"""simple docstring"""





			lowercase__      :  Optional[int]      =			'''train'''
			lowercase__      :  Tuple      =			'''dev'''
			lowercase__      :  Optional[Any]      =			'''test'''





class 			_lowerCAmelCase   :






			"""simple docstring"""





			@staticmethod
			def 		snake_case__			(				lowercase	:       List[str]			,     lowercase	:       Union[Split, str]   )						->					List[InputExample]:
									"""simple docstring"""







									raise NotImplementedError





			@staticmethod
			def 		snake_case__			(				lowercase	:       str   )						->					List[str]:
									"""simple docstring"""







									raise NotImplementedError







			@staticmethod
			def 		snake_case__			(				lowercase	:       List[InputExample]			,     lowercase	:       List[str]			,     lowercase	:       int			,     lowercase	:       PreTrainedTokenizer			,     lowercase	:       int=False			,     lowercase	:       Optional[int]="[CLS]"			,     lowercase	:       Union[str, Any]=1			,     lowercase	:       Union[str, Any]="[SEP]"			,     lowercase	:       int=False			,     lowercase	:       List[Any]=False			,     lowercase	:       Any=0			,     lowercase	:       List[str]=0			,     lowercase	:       int=-100			,     lowercase	:       List[str]=0			,     lowercase	:       Tuple=True			,     )						->					List[InputFeatures]:
									"""simple docstring"""







									__lowercase					=				{label: i for i, label in enumerate(A_   )}

									__lowercase					=				[]
									for ex_index, example in enumerate(A_   ):
															if ex_index % 10_000 == 0:
																					logger.info("""Writing example %d of %d"""			,     A_			,     len(A_   )   )

															__lowercase					=				[]
															__lowercase					=				[]
															for word, label in zip(example.words			,     example.labels   ):
																					__lowercase					=				tokenizer.tokenize(A_   )

																					# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																					if len(A_   ) > 0:
																											tokens.extend(A_   )
																											# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																											label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A_   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
															__lowercase					=				tokenizer.num_special_tokens_to_add()
															if len(A_   ) > max_seq_length - special_tokens_count:
																					__lowercase					=				tokens[: (max_seq_length - special_tokens_count)]
																					__lowercase					=				label_ids[: (max_seq_length - special_tokens_count)]

															# The convention in BERT is:
															# (a) For sequence pairs:
															#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
															#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
															# (b) For single sequences:
															#  tokens:   [CLS] the dog is hairy . [SEP]
															#  type_ids:   0   0   0   0  0     0   0
															#
															# Where "type_ids" are used to indicate whether this is the first
															# sequence or the second sequence. The embedding vectors for `type=0` and
															# `type=1` were learned during pre-training and are added to the wordpiece
															# embedding vector (and position vector). This is not *strictly* necessary
															# since the [SEP] token unambiguously separates the sequences, but it makes
															# it easier for the model to learn the concept of sequences.
															#
															# For classification tasks, the first vector (corresponding to [CLS]) is
															# used as the "sentence vector". Note that this only makes sense because
															# the entire model is fine-tuned.
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
															if sep_token_extra:
																					# roberta uses an extra separator b/w pairs of sentences
																					tokens += [sep_token]
																					label_ids += [pad_token_label_id]
															__lowercase					=				[sequence_a_segment_id] * len(A_   )

															if cls_token_at_end:
																					tokens += [cls_token]
																					label_ids += [pad_token_label_id]
																					segment_ids += [cls_token_segment_id]
															else:
																					__lowercase					=				[cls_token] + tokens
																					__lowercase					=				[pad_token_label_id] + label_ids
																					__lowercase					=				[cls_token_segment_id] + segment_ids

															__lowercase					=				tokenizer.convert_tokens_to_ids(A_   )

															# The mask has 1 for real tokens and 0 for padding tokens. Only real
															# tokens are attended to.
															__lowercase					=				[1 if mask_padding_with_zero else 0] * len(A_   )

															# Zero-pad up to the sequence length.
															__lowercase					=				max_seq_length - len(A_   )
															if pad_on_left:
																					__lowercase					=				([pad_token] * padding_length) + input_ids
																					__lowercase					=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																					__lowercase					=				([pad_token_segment_id] * padding_length) + segment_ids
																					__lowercase					=				([pad_token_label_id] * padding_length) + label_ids
															else:
																					input_ids += [pad_token] * padding_length
																					input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																					segment_ids += [pad_token_segment_id] * padding_length
																					label_ids += [pad_token_label_id] * padding_length

															assert len(A_   ) == max_seq_length
															assert len(A_   ) == max_seq_length
															assert len(A_   ) == max_seq_length
															assert len(A_   ) == max_seq_length

															if ex_index < 5:
																					logger.info("""*** Example ***"""   )
																					logger.info("""guid: %s"""			,     example.guid   )
																					logger.info("""tokens: %s"""			,     """ """.join([str(A_   ) for x in tokens]   )   )
																					logger.info("""input_ids: %s"""			,     """ """.join([str(A_   ) for x in input_ids]   )   )
																					logger.info("""input_mask: %s"""			,     """ """.join([str(A_   ) for x in input_mask]   )   )
																					logger.info("""segment_ids: %s"""			,     """ """.join([str(A_   ) for x in segment_ids]   )   )
																					logger.info("""label_ids: %s"""			,     """ """.join([str(A_   ) for x in label_ids]   )   )

															if "token_type_ids" not in tokenizer.model_input_names:
																					__lowercase					=				None

															features.append(
															    InputFeatures(
															        input_ids=A_			,     attention_mask=A_			,     token_type_ids=A_			,     label_ids=A_   )   )
									return features


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset
			class 			_lowerCAmelCase   ( _lowercase			):






						"""simple docstring"""





						lowercase__      :  List[InputFeatures]
						lowercase__      :  int      =			nn.CrossEntropyLoss().ignore_index





						def __init__(				self	:       Tuple			,     lowercase	:       TokenClassificationTask			,     lowercase	:       str			,     lowercase	:       PreTrainedTokenizer			,     lowercase	:       List[str]			,     lowercase	:       str			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[int]=False			,     lowercase	:       Split = Split.train			,     )						->					Dict:
												"""simple docstring"""







												__lowercase					=				os.path.join(
												    A_			,     """cached_{}_{}_{}""".format(mode.value			,     tokenizer.__class__.__name__			,     str(A_   )   )			,     )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												__lowercase					=				cached_features_file + """.lock"""
												with FileLock(A_   ):
																		if os.path.exists(A_   ) and not overwrite_cache:
																								logger.info(F"Loading features from cached file {cached_features_file}"   )
																								__lowercase					=				torch.load(A_   )
																		else:
																								logger.info(F"Creating features from dataset file at {data_dir}"   )
																								__lowercase					=				token_classification_task.read_examples_from_file(A_			,     A_   )
																								# TODO clean up all this to leverage built-in features of tokenizers
																								__lowercase					=				token_classification_task.convert_examples_to_features(
																								    A_			,     A_			,     A_			,     A_			,     cls_token_at_end=bool(model_type in ["""xlnet"""]   )			,     cls_token=tokenizer.cls_token			,     cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0			,     sep_token=tokenizer.sep_token			,     sep_token_extra=A_			,     pad_on_left=bool(tokenizer.padding_side == """left"""   )			,     pad_token=tokenizer.pad_token_id			,     pad_token_segment_id=tokenizer.pad_token_type_id			,     pad_token_label_id=self.pad_token_label_id			,     )
																								logger.info(F"Saving features into cached file {cached_features_file}"   )
																								torch.save(self.features			,     A_   )





						def __len__(				self	:       Optional[int]   )						->					List[str]:
												"""simple docstring"""







												return len(self.features   )







						def __getitem__(				self	:       List[Any]			,     lowercase	:       Optional[int]   )						->					InputFeatures:
												"""simple docstring"""







												return self.features[i]


if is_tf_available():
			import tensorflow as tf
			class 			_lowerCAmelCase   :






						"""simple docstring"""





						lowercase__      :  List[InputFeatures]
						lowercase__      :  int      =			-100





						def __init__(				self	:       Any			,     lowercase	:       TokenClassificationTask			,     lowercase	:       str			,     lowercase	:       PreTrainedTokenizer			,     lowercase	:       List[str]			,     lowercase	:       str			,     lowercase	:       Optional[int] = None			,     lowercase	:       str=False			,     lowercase	:       Split = Split.train			,     )						->					Tuple:
												"""simple docstring"""







												__lowercase					=				token_classification_task.read_examples_from_file(A_			,     A_   )
												# TODO clean up all this to leverage built-in features of tokenizers
												__lowercase					=				token_classification_task.convert_examples_to_features(
												    A_			,     A_			,     A_			,     A_			,     cls_token_at_end=bool(model_type in ["""xlnet"""]   )			,     cls_token=tokenizer.cls_token			,     cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0			,     sep_token=tokenizer.sep_token			,     sep_token_extra=A_			,     pad_on_left=bool(tokenizer.padding_side == """left"""   )			,     pad_token=tokenizer.pad_token_id			,     pad_token_segment_id=tokenizer.pad_token_type_id			,     pad_token_label_id=self.pad_token_label_id			,     )

												def gen():
																		for ex in self.features:
																								if ex.token_type_ids is None:
																														yield (
																														    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																														    ex.label_ids,
																														)
																								else:
																														yield (
																														    {
																														        "input_ids": ex.input_ids,
																														        "attention_mask": ex.attention_mask,
																														        "token_type_ids": ex.token_type_ids,
																														    },
																														    ex.label_ids,
																														)

												if "token_type_ids" not in tokenizer.model_input_names:
																		__lowercase					=				tf.data.Dataset.from_generator(
																		    A_			,     ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)			,     (
																		        {"""input_ids""": tf.TensorShape([None]   ), """attention_mask""": tf.TensorShape([None]   )},
																		        tf.TensorShape([None]   ),
																		    )			,     )
												else:
																		__lowercase					=				tf.data.Dataset.from_generator(
																		    A_			,     ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)			,     (
																		        {
																		            """input_ids""": tf.TensorShape([None]   ),
																		            """attention_mask""": tf.TensorShape([None]   ),
																		            """token_type_ids""": tf.TensorShape([None]   ),
																		        },
																		        tf.TensorShape([None]   ),
																		    )			,     )





						def 		snake_case__			(				self	:       Union[str, Any]   )						->					Any:
												"""simple docstring"""







												__lowercase					=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

												return self.dataset





						def __len__(				self	:       Optional[Any]   )						->					str:
												"""simple docstring"""







												return len(self.features   )







						def __getitem__(				self	:       Union[str, Any]			,     lowercase	:       Optional[Any]   )						->					InputFeatures:
												"""simple docstring"""







												return self.features[i]










from dataclasses import asdict, dataclass
from typing import Optional

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


UpperCamelCase__ =						logging.get_logger(__name__)

# TODO Update this
UpperCamelCase__ =						{
    "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 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			"""esm"""





			def __init__(				self	:       Any			,     lowercase	:       Optional[Any]=None			,     lowercase	:       Optional[int]=None			,     lowercase	:       List[Any]=None			,     lowercase	:       Optional[int]=768			,     lowercase	:       str=12			,     lowercase	:       Union[str, Any]=12			,     lowercase	:       Dict=3_072			,     lowercase	:       Optional[int]=0.1			,     lowercase	:       str=0.1			,     lowercase	:       Dict=1_026			,     lowercase	:       Tuple=0.02			,     lowercase	:       str=1E-1_2			,     lowercase	:       Dict="absolute"			,     lowercase	:       Optional[Any]=True			,     lowercase	:       int=None			,     lowercase	:       int=False			,     lowercase	:       List[str]=False			,     lowercase	:       Tuple=None			,     lowercase	:       Tuple=None			,     **lowercase	:       Union[str, Any]			,     )						->					int:
									"""simple docstring"""







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

									__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(lowercase			,     lowercase   ):
																					__lowercase					=				EsmFoldConfig(**lowercase   )
															__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"""			,     lowercase   ):
															raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""   )







			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				super().to_dict()
									if isinstance(self.esmfold_config			,     lowercase   ):
															__lowercase					=				self.esmfold_config.to_dict()
									return output





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  str      =			None
			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			False
			lowercase__      :  bool      =			False
			lowercase__      :  bool      =			False
			lowercase__      :  float      =			0

			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			False

			lowercase__      :  int      =			128
			lowercase__      :  "TrunkConfig"      =			None





			def 		snake_case__			(				self	:       List[str]   )						->					Any:
									"""simple docstring"""







									if self.trunk is None:
															__lowercase					=				TrunkConfig()
									elif isinstance(self.trunk			,     lowercase   ):
															__lowercase					=				TrunkConfig(**self.trunk   )







			def 		snake_case__			(				self	:       Dict   )						->					Any:
									"""simple docstring"""







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





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  int      =			48
			lowercase__      :  int      =			1_024
			lowercase__      :  int      =			128
			lowercase__      :  int      =			32
			lowercase__      :  int      =			32
			lowercase__      :  int      =			32
			lowercase__      :  float      =			0
			lowercase__      :  float      =			0
			lowercase__      :  bool      =			False
			lowercase__      :  int      =			4
			lowercase__      :  Optional[int]      =			128
			lowercase__      :  "StructureModuleConfig"      =			None





			def 		snake_case__			(				self	:       Tuple   )						->					str:
									"""simple docstring"""







									if self.structure_module is None:
															__lowercase					=				StructureModuleConfig()
									elif isinstance(self.structure_module			,     lowercase   ):
															__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 		snake_case__			(				self	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







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





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  int      =			384
			lowercase__      :  int      =			128
			lowercase__      :  int      =			16
			lowercase__      :  int      =			128
			lowercase__      :  int      =			12
			lowercase__      :  int      =			4
			lowercase__      :  int      =			8
			lowercase__      :  float      =			0.1
			lowercase__      :  int      =			8
			lowercase__      :  int      =			1
			lowercase__      :  int      =			2
			lowercase__      :  int      =			7
			lowercase__      :  int      =			10
			lowercase__      :  float      =			1E-8
			lowercase__      :  float      =			1E5





			def 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									return asdict(self   )






def  UpperCAmelCase__    (    ) ->      List[Any]:
						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>",
						)





