





"""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 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Tuple    ,snake_case_  :  Optional[int]    ,snake_case_  :  List[str]    ,snake_case_  :  int    ,snake_case_  :  int = None    ,)       ->str:



				'''simple docstring'''







				__A   :						int									=					{}

				if train_file is not None:
								__A   :						Dict									=					[train_file]
				if eval_file is not None:
								__A   :						Union[str, Any]									=					[eval_file]
				if test_file is not None:
								__A   :						Optional[Any]									=					[test_file]

				__A   :						int									=					datasets.load_dataset('''csv'''    ,data_files=__snake_case						)
				__A   :						Tuple									=					list(ds[list(files.keys()						)[0]].features.keys()						)
				__A   :						str									=					features_name.pop(__snake_case						)
				__A   :						Union[str, Any]									=					list(set(ds[list(files.keys()						)[0]][label_name]						)						)
				__A   :						Dict									=					{label: i for i, label in enumerate(__snake_case						)}
				__A   :						Any									=					tokenizer.model_input_names
				__A   :						Union[str, Any]									=					{}

				if len(__snake_case						) == 1:
								for k in files.keys():
												__A   :						List[Any]									=					ds[k].map(
												    lambda snake_case_						: tokenizer.batch_encode_plus(
												        example[features_name[0]]    ,truncation=__snake_case    ,max_length=__snake_case    ,padding='''max_length'''						)    ,batched=__snake_case    ,)
				elif len(__snake_case						) == 2:
								for k in files.keys():
												__A   :						Dict									=					ds[k].map(
												    lambda snake_case_						: tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])    ,truncation=__snake_case    ,max_length=__snake_case    ,padding='''max_length'''    ,)    ,batched=__snake_case    ,)

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

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

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

				__A   :						str									=					(
				    tf.data.Dataset.from_generator(
				        __snake_case    ,({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   :						Optional[Any]									=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

				__A   :						int									=					(
				    tf.data.Dataset.from_generator(
				        __snake_case    ,({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   :						Optional[int]									=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

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

				return train_ds, val_ds, test_ds, labelaid


a_			=      logging.getLogger(__name__)






@dataclass
class 		__snake_case :





	"""simple docstring"""


	_lowerCamelCase				=					field(metadata={"""help""": """Which column contains the label"""}					)
	_lowerCamelCase				=					field(default=__SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The path of the training file"""}					)
	_lowerCamelCase				=					field(default=__SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The path of the development file"""}					)
	_lowerCamelCase				=					field(default=__SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The path of the test file"""}					)
	_lowerCamelCase				=					field(
	    default=1_28							,							metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }							,							)
	_lowerCamelCase				=					field(
	    default=__SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)






@dataclass
class 		__snake_case :





	"""simple docstring"""


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







def 		__lowercase					(       )       ->Dict:



				'''simple docstring'''







				__A   :						List[Any]									=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
				__A   :						Dict									=					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   :						Union[str, Any]									=					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   :						Union[str, Any]									=					get_tfds(
				    train_file=data_args.train_file    ,eval_file=data_args.dev_file    ,test_file=data_args.test_file    ,tokenizer=__snake_case    ,label_column_id=data_args.label_column_id    ,max_seq_length=data_args.max_seq_length    ,)

				__A   :						Optional[int]									=					AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,num_labels=len(__snake_case						)    ,labelaid=__snake_case    ,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   :						Optional[int]									=					TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path    ,from_pt=bool('''.bin''' in model_args.model_name_or_path						)    ,config=__snake_case    ,cache_dir=model_args.cache_dir    ,)

				def compute_metrics(snake_case_  :  List[Any]						) -> Dict:
								__A   :						str									=					np.argmax(p.predictions    ,axis=1						)

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

				# Initialize our Trainer
				__A   :						str									=					TFTrainer(
				    model=__snake_case    ,args=__snake_case    ,train_dataset=__snake_case    ,eval_dataset=__snake_case    ,compute_metrics=__snake_case    ,)

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

				# Evaluation
				__A   :						str									=					{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''						)
								__A   :						int									=					trainer.evaluate()
								__A   :						List[str]									=					os.path.join(training_args.output_dir    ,'''eval_results.txt'''						)

								with open(__snake_case    ,'''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(__snake_case						)

				return results


if __name__ == "__main__":
							main()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_			=      {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""BloomTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """BloomForCausalLM""",
           """BloomModel""",
           """BloomPreTrainedModel""",
           """BloomForSequenceClassification""",
           """BloomForTokenClassification""",
           """BloomForQuestionAnswering""",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




