





"""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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							PreTrainedTokenizer    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Optional[int] = None    ,						):


							'''simple docstring'''







							__lowerCamelCase      :      Any         ={}

							if train_file is not None:
														__lowerCamelCase      :      List[Any]         =[train_file]
							if eval_file is not None:
														__lowerCamelCase      :      Any         =[eval_file]
							if test_file is not None:
														__lowerCamelCase      :      Any         =[test_file]

							__lowerCamelCase      :      Any         =datasets.load_dataset('''csv'''    ,						data_files=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Dict         =list(ds[list(files.keys()   )[0]].features.keys()   )
							__lowerCamelCase      :      List[Any]         =features_name.pop(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      List[Any]         =list(set(ds[list(files.keys()   )[0]][label_name]   )   )
							__lowerCamelCase      :      Tuple         ={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE   )}
							__lowerCamelCase      :      Optional[Any]         =tokenizer.model_input_names
							__lowerCamelCase      :      Optional[Any]         ={}

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

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

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

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

							__lowerCamelCase      :      Any         =(
							    tf.data.Dataset.from_generator(
							        SCREAMING_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:
														__lowerCamelCase      :      Tuple         =train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

							__lowerCamelCase      :      Dict         =(
							    tf.data.Dataset.from_generator(
							        SCREAMING_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:
														__lowerCamelCase      :      str         =val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

							__lowerCamelCase      :      Optional[Any]         =(
							    tf.data.Dataset.from_generator(
							        SCREAMING_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:
														__lowerCamelCase      :      Optional[Any]         =test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

							return train_ds, val_ds, test_ds, labelaid


_UpperCamelCase							=      logging.getLogger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       int	 =		field(metadata={"""help""": """Which column contains the label"""}   )
				__snake_case   :       str	 =		field(default=snake_case__       ,  metadata={"""help""": """The path of the training file"""}   )
				__snake_case   :       Optional[str]	 =		field(default=snake_case__       ,  metadata={"""help""": """The path of the development file"""}   )
				__snake_case   :       Optional[str]	 =		field(default=snake_case__       ,  metadata={"""help""": """The path of the test file"""}   )
				__snake_case   :       int	 =		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."""
				        )
				    }       ,  )
				__snake_case   :       bool	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )

@dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
				__snake_case   :       bool	 =		field(default=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.
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,  )



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      int         =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
							__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      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.

							__lowerCamelCase      :      Optional[int]         =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    ,						)

							__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Optional[Any]         =get_tfds(
							    train_file=data_args.train_file    ,						eval_file=data_args.dev_file    ,						test_file=data_args.test_file    ,						tokenizer=SCREAMING_SNAKE_CASE    ,						label_column_id=data_args.label_column_id    ,						max_seq_length=data_args.max_seq_length    ,						)

							__lowerCamelCase      :      int         =AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,						num_labels=len(SCREAMING_SNAKE_CASE   )    ,						labelaid=SCREAMING_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():
														__lowerCamelCase      :      List[Any]         =TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path    ,						from_pt=bool('''.bin''' in model_args.model_name_or_path   )    ,						config=SCREAMING_SNAKE_CASE    ,						cache_dir=model_args.cache_dir    ,						)

							def compute_metrics(SCREAMING_SNAKE_CASE       :							EvalPrediction   ) -> Dict:
														__lowerCamelCase      :      List[str]         =np.argmax(p.predictions    ,						axis=1   )

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

							# Initialize our Trainer
							__lowerCamelCase      :      Optional[Any]         =TFTrainer(
							    model=SCREAMING_SNAKE_CASE    ,						args=SCREAMING_SNAKE_CASE    ,						train_dataset=SCREAMING_SNAKE_CASE    ,						eval_dataset=SCREAMING_SNAKE_CASE    ,						compute_metrics=SCREAMING_SNAKE_CASE    ,						)

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

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

														with open(SCREAMING_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(SCREAMING_SNAKE_CASE   )

							return results


if __name__ == "__main__":
							main()











"""simple docstring"""





from typing import List, Optional, Union

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""time_series_transformer"""
				__snake_case   :       Any	 =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(     self  :List[Any]						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :str = "student_t"						,    __lowercase  :str = "nll"						,    __lowercase  :int = 1						,    __lowercase  :List[int] = [1, 2, 3, 4, 5, 6, 7]						,    __lowercase  :Optional[Union[str, bool]] = "mean"						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :int = 32						,    __lowercase  :int = 32						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :bool = True						,    __lowercase  :str = "gelu"						,    __lowercase  :int = 64						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :int = 100						,    __lowercase  :float = 0.02						,    __lowercase  :int=True						,    **__lowercase  :Optional[Any]						,    ):
											# time series specific configuration
											__lowerCamelCase      :      Tuple         =prediction_length
											__lowerCamelCase      :      List[Any]         =context_length or prediction_length
											__lowerCamelCase      :      Dict         =distribution_output
											__lowerCamelCase      :      str         =loss
											__lowerCamelCase      :      Tuple         =input_size
											__lowerCamelCase      :      int         =num_time_features
											__lowerCamelCase      :      int         =lags_sequence
											__lowerCamelCase      :      Optional[int]         =scaling
											__lowerCamelCase      :      str         =num_dynamic_real_features
											__lowerCamelCase      :      Optional[Any]         =num_static_real_features
											__lowerCamelCase      :      List[Any]         =num_static_categorical_features
											if cardinality and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      Optional[int]         =cardinality
											else:
																		__lowerCamelCase      :      str         =[0]
											if embedding_dimension and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      int         =embedding_dimension
											else:
																		__lowerCamelCase      :      Optional[int]         =[min(50						,    (cat + 1) // 2			) for cat in self.cardinality]
											__lowerCamelCase      :      List[str]         =num_parallel_samples

											# Transformer architecture configuration
											__lowerCamelCase      :      str         =input_size * len(__lowercase			) + self._number_of_features
											__lowerCamelCase      :      Union[str, Any]         =d_model
											__lowerCamelCase      :      int         =encoder_attention_heads
											__lowerCamelCase      :      int         =decoder_attention_heads
											__lowerCamelCase      :      Dict         =encoder_ffn_dim
											__lowerCamelCase      :      int         =decoder_ffn_dim
											__lowerCamelCase      :      List[Any]         =encoder_layers
											__lowerCamelCase      :      int         =decoder_layers

											__lowerCamelCase      :      Union[str, Any]         =dropout
											__lowerCamelCase      :      Optional[Any]         =attention_dropout
											__lowerCamelCase      :      List[str]         =activation_dropout
											__lowerCamelCase      :      List[str]         =encoder_layerdrop
											__lowerCamelCase      :      int         =decoder_layerdrop

											__lowerCamelCase      :      Tuple         =activation_function
											__lowerCamelCase      :      str         =init_std

											__lowerCamelCase      :      Dict         =use_cache

											super().__init__(is_encoder_decoder=__lowercase						,    **__lowercase			)




				@property
				def 					__lowercase  (     self  :int			):
											return (
											    sum(self.embedding_dimension			)
											    + self.num_dynamic_real_features
											    + self.num_time_features
											    + self.num_static_real_features
											    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
											)




