

'''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				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					str		,lowerCAmelCase__:					str		,lowerCAmelCase__:					PreTrainedTokenizer		,lowerCAmelCase__:					int		,lowerCAmelCase__:					Optional[int] = None		,)      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				{}

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

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

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

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

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

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

							SCREAMING_SNAKE_CASE_				=				(
							    tf.data.Dataset.from_generator(
							        lowerCAmelCase__		,({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:
														SCREAMING_SNAKE_CASE_				=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

							SCREAMING_SNAKE_CASE_				=				(
							    tf.data.Dataset.from_generator(
							        lowerCAmelCase__		,({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:
														SCREAMING_SNAKE_CASE_				=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

							SCREAMING_SNAKE_CASE_				=				(
							    tf.data.Dataset.from_generator(
							        lowerCAmelCase__		,({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:
														SCREAMING_SNAKE_CASE_				=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

							return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			logging.getLogger(__name__)






@dataclass
class    snake_case     :






				"""simple docstring"""
				_a    	=     field(metadata={"""help""": """Which column contains the label"""}		)
				_a    	=     field(default=lowercase_,						metadata={"""help""": """The path of the training file"""}		)
				_a    	=     field(default=lowercase_,						metadata={"""help""": """The path of the development file"""}		)
				_a    	=     field(default=lowercase_,						metadata={"""help""": """The path of the test file"""}		)
				_a    	=     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."""
				        )
				    },						)
				_a    	=     field(
				    default=lowercase_,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)






@dataclass
class    snake_case     :






				"""simple docstring"""
				_a    	=     field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
				_a    	=     field(
				    default=lowercase_,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
				_a    	=     field(
				    default=lowercase_,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
				_a    	=     field(default=lowercase_,						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.
				_a    	=     field(
				    default=lowercase_,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},						)




def 							_UpperCamelCase				(   )      -> Any:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.
							SCREAMING_SNAKE_CASE_				=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				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.

							SCREAMING_SNAKE_CASE_				=				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		,)

							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				get_tfds(
							    train_file=data_args.train_file		,eval_file=data_args.dev_file		,test_file=data_args.test_file		,tokenizer=lowerCAmelCase__		,label_column_id=data_args.label_column_id		,max_seq_length=data_args.max_seq_length		,)

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

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

							def compute_metrics(lowerCAmelCase__:					EvalPrediction   ) -> Dict:
														SCREAMING_SNAKE_CASE_				=				np.argmax(p.predictions		,axis=1   )

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

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE_				=				TFTrainer(
							    model=lowerCAmelCase__		,args=lowerCAmelCase__		,train_dataset=lowerCAmelCase__		,eval_dataset=lowerCAmelCase__		,compute_metrics=lowerCAmelCase__		,)

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

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

														with open(lowerCAmelCase__		,'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(lowerCAmelCase__   )

							return results


if __name__ == "__main__":
							main()








'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE							:				int     	=			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE							:				Dict     	=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE							:				str     	=			{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			{
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     VOCAB_FILES_NAMES
				_a    	=     PRETRAINED_VOCAB_FILES_MAP
				_a    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_a    	=     ["""input_ids""", """attention_mask"""]
				_a    	=     BartTokenizer



				def __init__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     _lowercase="replace",     _lowercase="<s>",     _lowercase="</s>",     _lowercase="</s>",     _lowercase="<s>",     _lowercase="<unk>",     _lowercase="<pad>",     _lowercase="<mask>",     _lowercase=False,     _lowercase=True,     **_lowercase,     )     ->							Dict:
											super().__init__(
											    _lowercase,     _lowercase,     tokenizer_file=_lowercase,     errors=_lowercase,     bos_token=_lowercase,     eos_token=_lowercase,     sep_token=_lowercase,     cls_token=_lowercase,     unk_token=_lowercase,     pad_token=_lowercase,     mask_token=_lowercase,     add_prefix_space=_lowercase,     trim_offsets=_lowercase,     **_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
											if pre_tok_state.get('add_prefix_space',     _lowercase       ) != add_prefix_space:
																		SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     pre_tok_state.pop('type'       )       )
																		SCREAMING_SNAKE_CASE_				=				add_prefix_space
																		SCREAMING_SNAKE_CASE_				=				pre_tok_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				add_prefix_space

											# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
											SCREAMING_SNAKE_CASE_				=				'post_processor'
											SCREAMING_SNAKE_CASE_				=				getattr(self.backend_tokenizer,     _lowercase,     _lowercase       )
											if tokenizer_component_instance:
																		SCREAMING_SNAKE_CASE_				=				json.loads(tokenizer_component_instance.__getstate__()       )

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																									SCREAMING_SNAKE_CASE_				=				tuple(state['sep']       )
																		if "cls" in state:
																									SCREAMING_SNAKE_CASE_				=				tuple(state['cls']       )

																		SCREAMING_SNAKE_CASE_				=				False

																		if state.get('add_prefix_space',     _lowercase       ) != add_prefix_space:
																									SCREAMING_SNAKE_CASE_				=				add_prefix_space
																									SCREAMING_SNAKE_CASE_				=				True

																		if state.get('trim_offsets',     _lowercase       ) != trim_offsets:
																									SCREAMING_SNAKE_CASE_				=				trim_offsets
																									SCREAMING_SNAKE_CASE_				=				True

																		if changes_to_apply:
																									SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     state.pop('type'       )       )
																									SCREAMING_SNAKE_CASE_				=				component_class(**_lowercase       )
																									setattr(self.backend_tokenizer,     _lowercase,     _lowercase       )



				@property
				def   a__					(    self       )     ->							str:
											if self._mask_token is None:
																		if self.verbose:
																									logger.error('Using mask_token, but it is not set yet.'       )
																		return None
											return str(self._mask_token       )



				@mask_token.setter
				def   a__					(    self,     _lowercase       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				AddedToken(_lowercase,     lstrip=_lowercase,     rstrip=_lowercase       ) if isinstance(_lowercase,     _lowercase       ) else value
											SCREAMING_SNAKE_CASE_				=				value



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							BatchEncoding:
											SCREAMING_SNAKE_CASE_				=				kwargs.get('is_split_into_words',     _lowercase       )

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.'       )

											return super()._batch_encode_plus(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							BatchEncoding:
											SCREAMING_SNAKE_CASE_				=				kwargs.get('is_split_into_words',     _lowercase       )

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.'       )

											return super()._encode_plus(*_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							Tuple[str]:
											SCREAMING_SNAKE_CASE_				=				self._tokenizer.model.save(_lowercase,     name=_lowercase       )
											return tuple(_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase=None       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							List[int]:
											SCREAMING_SNAKE_CASE_				=				[self.sep_token_id]
											SCREAMING_SNAKE_CASE_				=				[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep       ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]





