"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    ):






							"""simple docstring"""





							A__	     =							{}

							if train_file is not None:
														A__	     =							[train_file]
							if eval_file is not None:
														A__	     =							[eval_file]
							if test_file is not None:
														A__	     =							[test_file]

							A__	     =							datasets.load_dataset('csv'       ,    data_files=UpperCAmelCase__				)
							A__	     =							list(ds[list(files.keys()				)[0]].features.keys()				)
							A__	     =							features_name.pop(UpperCAmelCase__				)
							A__	     =							list(set(ds[list(files.keys()				)[0]][label_name]				)				)
							A__	     =							{label: i for i, label in enumerate(UpperCAmelCase__				)}
							A__	     =							tokenizer.model_input_names
							A__	     =							{}

							if len(UpperCAmelCase__				) == 1:
														for k in files.keys():
																					A__	     =							ds[k].map(
																					    lambda UpperCamelCase__				: tokenizer.batch_encode_plus(
																					        example[features_name[0]]       ,    truncation=UpperCAmelCase__       ,    max_length=UpperCAmelCase__       ,    padding='max_length'				)       ,    batched=UpperCAmelCase__       ,    )
							elif len(UpperCAmelCase__				) == 2:
														for k in files.keys():
																					A__	     =							ds[k].map(
																					    lambda UpperCamelCase__				: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])       ,    truncation=UpperCAmelCase__       ,    max_length=UpperCAmelCase__       ,    padding='max_length'       ,    )       ,    batched=UpperCAmelCase__       ,    )

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					A__	     =							{k: v for k, v in ex.items() if k in input_names}
																					A__	     =							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					A__	     =							{k: v for k, v in ex.items() if k in input_names}
																					A__	     =							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					A__	     =							{k: v for k, v in ex.items() if k in input_names}
																					A__	     =							labelaid[ex[label_name]]
																					yield (d, label)

							A__	     =							(
							    tf.data.Dataset.from_generator(
							        UpperCAmelCase__       ,    ({k: tf.intaa for k in input_names}, tf.intaa)       ,    ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))       ,    )
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														A__	     =							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

							A__	     =							(
							    tf.data.Dataset.from_generator(
							        UpperCAmelCase__       ,    ({k: tf.intaa for k in input_names}, tf.intaa)       ,    ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))       ,    )
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														A__	     =							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

							A__	     =							(
							    tf.data.Dataset.from_generator(
							        UpperCAmelCase__       ,    ({k: tf.intaa for k in input_names}, tf.intaa)       ,    ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))       ,    )
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														A__	     =							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

							return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase											=  logging.getLogger(__name__)
@dataclass
class     UpperCamelCase__:
						lowerCAmelCase__  :						Optional[int]													=		field(metadata={'help': 'Which column contains the label'} )
						lowerCAmelCase__  :						int													=		field(default=_a		,						metadata={'help': 'The path of the training file'} )
						lowerCAmelCase__  :						Dict													=		field(default=_a		,						metadata={'help': 'The path of the development file'} )
						lowerCAmelCase__  :						Tuple													=		field(default=_a		,						metadata={'help': 'The path of the test file'} )
						lowerCAmelCase__  :						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.'
						        )
						    }		,						)
						lowerCAmelCase__  :						int													=		field(
						    default=_a		,						metadata={'help': 'Overwrite the cached training and evaluation sets'} )






@dataclass
class     UpperCamelCase__:
						lowerCAmelCase__  :						Dict													=		field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
						lowerCAmelCase__  :						Union[str, Any]													=		field(
						    default=_a		,						metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
						lowerCAmelCase__  :						Optional[int]													=		field(
						    default=_a		,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
						lowerCAmelCase__  :						Optional[int]													=		field(default=_a		,						metadata={'help': 'Set this flag to use fast tokenization.'} )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						lowerCAmelCase__  :						List[Any]													=		field(
						    default=_a		,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,						)






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
							A__     ,  A__     ,  A__	     =							parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir				)
							    and os.listdir(training_args.output_dir				)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
														    ' --overwrite_output_dir to overcome.'				)

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,    datefmt='%m/%d/%Y %H:%M:%S'       ,    level=logging.INFO       ,    )
							logger.info(
							    F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, '''
							    F'''16-bits training: {training_args.fpaa}'''				)
							logger.info(F'''Training/evaluation parameters {training_args}'''				)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							A__	     =							AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,    cache_dir=model_args.cache_dir       ,    )

							A__     ,  A__     ,  A__     ,  A__	     =							get_tfds(
							    train_file=data_args.train_file       ,    eval_file=data_args.dev_file       ,    test_file=data_args.test_file       ,    tokenizer=UpperCAmelCase__       ,    label_column_id=data_args.label_column_id       ,    max_seq_length=data_args.max_seq_length       ,    )

							A__	     =							AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,    num_labels=len(UpperCAmelCase__				)       ,    labelaid=UpperCAmelCase__       ,    idalabel={id: label for label, id in labelaid.items()}       ,    finetuning_task='text-classification'       ,    cache_dir=model_args.cache_dir       ,    )

							with training_args.strategy.scope():
														A__	     =							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path       ,    from_pt=bool('.bin' in model_args.model_name_or_path				)       ,    config=UpperCAmelCase__       ,    cache_dir=model_args.cache_dir       ,    )

							def compute_metrics(UpperCamelCase__				) -> Dict:
														A__	     =							np.argmax(p.predictions       ,    axis=1				)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							A__	     =							TFTrainer(
							    model=UpperCAmelCase__       ,    args=UpperCAmelCase__       ,    train_dataset=UpperCAmelCase__       ,    eval_dataset=UpperCAmelCase__       ,    compute_metrics=UpperCAmelCase__       ,    )

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir				)

							# Evaluation
							A__	     =							{}
							if training_args.do_eval:
														logger.info('*** Evaluate ***'				)
														A__	     =							trainer.evaluate()
														A__	     =							os.path.join(training_args.output_dir       ,    'eval_results.txt'				)

														with open(UpperCAmelCase__       ,    'w'				) as writer:
																					logger.info('***** Eval results *****'				)

																					for key, value in result.items():
																												logger.info(F'''  {key} = {value}'''				)
																												writer.write(F'''{key} = {value}\n'''				)

																					results.update(UpperCAmelCase__				)

							return results


if __name__ == "__main__":
	main()






"""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


__lowerCamelCase											=  logging.get_logger(__name__)

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

__lowerCamelCase											=  {
    "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",
    },
}

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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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


class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Any													=		VOCAB_FILES_NAMES
						lowerCAmelCase__  :						List[str]													=		PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__  :						List[Any]													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__  :						Dict													=		['input_ids', 'attention_mask']





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





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self._id_to_token.get(__UpperCAmelCase		,self.unk_token     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self._token_to_id.get(__UpperCAmelCase		,self._token_to_id.get(self.unk_token     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													return text.split()





						def 				snake_case__	(       self		,__UpperCAmelCase=False     )					->				int:
													return len(self._id_to_token     )





						def 				snake_case__	(       self     )					->				List[str]:
													return {token: i for i, token in enumerate(self.all_tokens     )}





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													return self._token_to_id.get(__UpperCAmelCase		,self._token_to_id.get(self.unk_token     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													return self._id_to_token.get(__UpperCAmelCase		,self.unk_token     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				List[int]:
													A__	     =							[self.cls_token_id]
													A__	     =							[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 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = 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]
													A__	     =							[1] + ([0] * len(__UpperCAmelCase     )) + [1]
													if token_ids_a is not None:
																				mask += [0] * len(__UpperCAmelCase     ) + [1]
													return mask





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[str]:
													A__	     =							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 				snake_case__	(       self     )					->				int:
													return self.get_vocab_size(with_added_tokens=__UpperCAmelCase     )





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





