

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a__				=		logging.get_logger(__name__)

@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
							UpperCAmelCase__       :						str	    =    field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
							UpperCAmelCase__       :						int	    =    field(
							    default=128				,       metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }				,       )
							UpperCAmelCase__       :						bool	    =    field(
							    default=__lowercase				,       metadata={"help": "Overwrite the cached training and evaluation sets"}   )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		self.task_name.lower()



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    "train"
							UpperCAmelCase__       :						List[str]	    =    "dev"
							UpperCAmelCase__       :						Union[str, Any]	    =    "test"



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						GlueDataTrainingArguments
							UpperCAmelCase__       :						str
							UpperCAmelCase__       :						List[InputFeatures]


							def __init__(			self       ,							_a       ,							_a       ,							_a = None       ,							_a = Split.train       ,							_a = None       ,							)    ->    str:
												warnings.warn(
												    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
												    '''library. You can have a look at this example script for pointers: '''
												    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''       ,							_a       ,							)
												_a	:    List[str]         =		args
												_a	:    List[str]         =		glue_processors[args.task_name]()
												_a	:    List[Any]         =		glue_output_modes[args.task_name]
												if isinstance(_a       ,							_a       ):
																	try:
																						_a	:    Any         =		Split[mode]
																	except KeyError:
																						raise KeyError('''mode is not a valid split name'''       )
        # Load data features from cache or dataset file
												_a	:    Union[str, Any]         =		os.path.join(
												    cache_dir if cache_dir is not None else args.data_dir       ,							F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""       ,							)
												_a	:    str         =		self.processor.get_labels()
												if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
												    "RobertaTokenizer",
												    "RobertaTokenizerFast",
												    "XLMRobertaTokenizer",
												    "BartTokenizer",
												    "BartTokenizerFast",
												):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	_a      ,					_a	:    Optional[int]         =		label_list[2], label_list[1]
												_a	:    Dict         =		label_list

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_a	:    Optional[int]         =		cached_features_file + '''.lock'''
												with FileLock(_a       ):
																	if os.path.exists(_a       ) and not args.overwrite_cache:
																						_a	:    List[Any]         =		time.time()
																						_a	:    str         =		torch.load(_a       )
																						logger.info(
																						    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""       ,							time.time() - start       )
																	else:
																						logger.info(F"""Creating features from dataset file at {args.data_dir}"""       )

																						if mode == Split.dev:
																											_a	:    str         =		self.processor.get_dev_examples(args.data_dir       )
																						elif mode == Split.test:
																											_a	:    Any         =		self.processor.get_test_examples(args.data_dir       )
																						else:
																											_a	:    List[str]         =		self.processor.get_train_examples(args.data_dir       )
																						if limit_length is not None:
																											_a	:    Dict         =		examples[:limit_length]
																						_a	:    Union[str, Any]         =		glue_convert_examples_to_features(
																						    _a       ,							_a       ,							max_length=args.max_seq_length       ,							label_list=_a       ,							output_mode=self.output_mode       ,							)
																						_a	:    Optional[int]         =		time.time()
																						torch.save(self.features       ,							_a       )
																						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																						logger.info(
																						    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""       )


							def __len__(			self       )    ->    Optional[int]:
												return len(self.features       )


							def __getitem__(			self       ,							_a       )    ->    InputFeatures:
												return self.features[i]




							def 				__lowercase				(			self       )    ->    Tuple:
												return self.label_list






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers.models.esm.modeling_esmfold import EsmForProteinFolding

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=1_3       ,							_a=7       ,							_a=False       ,							_a=True       ,							_a=False       ,							_a=False       ,							_a=1_9       ,							_a=3_2       ,							_a=5       ,							_a=4       ,							_a=3_7       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=5_1_2       ,							_a=1_6       ,							_a=2       ,							_a=0.02       ,							_a=3       ,							_a=4       ,							_a=None       ,							)    ->    Union[str, Any]:
												_a	:    Optional[Any]         =		parent
												_a	:    Union[str, Any]         =		batch_size
												_a	:    List[Any]         =		seq_length
												_a	:    Dict         =		is_training
												_a	:    int         =		use_input_mask
												_a	:    str         =		use_token_type_ids
												_a	:    Any         =		use_labels
												_a	:    List[Any]         =		vocab_size
												_a	:    Any         =		hidden_size
												_a	:    int         =		num_hidden_layers
												_a	:    str         =		num_attention_heads
												_a	:    Dict         =		intermediate_size
												_a	:    List[str]         =		hidden_act
												_a	:    Optional[Any]         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    int         =		max_position_embeddings
												_a	:    Tuple         =		type_vocab_size
												_a	:    str         =		type_sequence_label_size
												_a	:    Any         =		initializer_range
												_a	:    Union[str, Any]         =		num_labels
												_a	:    Dict         =		num_choices
												_a	:    Union[str, Any]         =		scope


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    str         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

												_a	:    Dict         =		None
												if self.use_input_mask:
																	_a	:    int         =		random_attention_mask([self.batch_size, self.seq_length]       )

												_a	:    List[Any]         =		None
												_a	:    Tuple         =		None
												_a	:    Any         =		None
												if self.use_labels:
																	_a	:    Tuple         =		ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )
																	_a	:    Union[str, Any]         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.num_labels       )
																	_a	:    List[Any]         =		ids_tensor([self.batch_size]       ,							self.num_choices       )

												_a	:    str         =		self.get_config()

												return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Optional[int]         =		EsmConfig(
												    vocab_size=3_3       ,							hidden_size=self.hidden_size       ,							pad_token_id=1       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							max_position_embeddings=self.max_position_embeddings       ,							type_vocab_size=self.type_vocab_size       ,							initializer_range=self.initializer_range       ,							is_folding_model=_a       ,							esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}       ,							)
												return config


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    str:
												_a	:    Union[str, Any]         =		EsmForProteinFolding(config=_a       ).float()
												model.to(_a       )
												model.eval()
												_a	:    str         =		model(_a       ,							attention_mask=_a       )
												_a	:    Union[str, Any]         =		model(_a       )
												_a	:    Optional[int]         =		model(_a       )

												self.parent.assertEqual(result.positions.shape       ,							(8, self.batch_size, self.seq_length, 1_4, 3)       )
												self.parent.assertEqual(result.angles.shape       ,							(8, self.batch_size, self.seq_length, 7, 2)       )




							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    Optional[Any]         =		config_and_inputs
												_a	:    List[Any]         =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_torch
class 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    False

							UpperCAmelCase__       :						Any	    =    (EsmForProteinFolding,) if is_torch_available() else ()
							UpperCAmelCase__       :						Union[str, Any]	    =    ()
							UpperCAmelCase__       :						int	    =    {} if is_torch_available() else {}
							UpperCAmelCase__       :						Optional[int]	    =    False


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Optional[int]         =		EsmFoldModelTester(self       )
												_a	:    Dict         =		ConfigTester(self       ,							config_class=_a       ,							hidden_size=3_7       )


							def 				__lowercase				(			self       )    ->    List[str]:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    str:
												_a	:    str         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							@unittest.skip('''Does not support attention outputs'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip('''Esm does not support embedding resizing'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip('''Esm does not support embedding resizing'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold does not support passing input embeds!'''       )
							def 				__lowercase				(			self       )    ->    int:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    str:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Any:
												pass


							@unittest.skip('''ESMFold does not support head pruning.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMFold does not output hidden states in the normal way.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMfold does not output hidden states in the normal way.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMFold only has one output format.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''       )
							def 				__lowercase				(			self       )    ->    Dict:
												pass


							@unittest.skip('''ESMFold does not support input chunking.'''       )
							def 				__lowercase				(			self       )    ->    Tuple:
												pass


							@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''       )
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''       )
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							@unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''       )
							def 				__lowercase				(			self       )    ->    List[Any]:
												pass


							@unittest.skip('''ESMFold doesn\'t support data parallel.'''       )
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												pass




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass



@require_torch
class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@slow
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''       ).float()
												model.eval()
												_a	:    Tuple         =		torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]       )
												_a	:    Optional[Any]         =		model(_a       )['''positions''']
												_a	:    Union[str, Any]         =		torch.tensor([2.5828, 0.7993, -10.9334]       ,							dtype=torch.floataa       )
												self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]       ,							_a       ,							atol=1e-4       )       )



