

import functools
import operator

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


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class     snake_case_			(				_lowercase							):


							"""simple docstring"""





							__UpperCAmelCase       ='''sew-d'''




							def __init__( self					,						_A=3_2					,						_A=7_6_8					,						_A=1_2					,						_A=1_2					,						_A=3_0_7_2					,						_A=2					,						_A=5_1_2					,						_A=2_5_6					,						_A=True					,						_A=True					,						_A=("p2c", "c2p")					,						_A="layer_norm"					,						_A="gelu_python"					,						_A=0.1					,						_A=0.1					,						_A=0.1					,						_A=0.0					,						_A=0.1					,						_A=0.02					,						_A=1e-7					,						_A=1e-5					,						_A="group"					,						_A="gelu"					,						_A=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)					,						_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,						_A=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,						_A=False					,						_A=1_2_8					,						_A=1_6					,						_A=True					,						_A=0.05					,						_A=1_0					,						_A=2					,						_A=0.0					,						_A=1_0					,						_A=0					,						_A="mean"					,						_A=False					,						_A=False					,						_A=2_5_6					,						_A=0					,						_A=1					,						_A=2					,						**_A					,						):
												super().__init__(**A_					,						pad_token_id=A_					,						bos_token_id=A_					,						eos_token_id=A_							)
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							feat_extract_norm
												__lowerCAmelCase					=							feat_extract_activation
												__lowerCAmelCase					=							list(A_							)
												__lowerCAmelCase					=							list(A_							)
												__lowerCAmelCase					=							list(A_							)
												__lowerCAmelCase					=							conv_bias
												__lowerCAmelCase					=							num_conv_pos_embeddings
												__lowerCAmelCase					=							num_conv_pos_embedding_groups
												__lowerCAmelCase					=							len(self.conv_dim							)
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							intermediate_size
												__lowerCAmelCase					=							squeeze_factor
												__lowerCAmelCase					=							max_position_embeddings
												__lowerCAmelCase					=							position_buckets
												__lowerCAmelCase					=							share_att_key
												__lowerCAmelCase					=							relative_attention
												__lowerCAmelCase					=							norm_rel_ebd
												__lowerCAmelCase					=							list(A_							)
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							hidden_dropout
												__lowerCAmelCase					=							attention_dropout
												__lowerCAmelCase					=							activation_dropout
												__lowerCAmelCase					=							feat_proj_dropout
												__lowerCAmelCase					=							final_dropout
												__lowerCAmelCase					=							layer_norm_eps
												__lowerCAmelCase					=							feature_layer_norm_eps
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							vocab_size

												if (
												    (len(self.conv_stride							) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
												    or (len(self.conv_dim							) != self.num_feat_extract_layers)
												):
																	raise ValueError(
																	    'Configuration for convolutional layers is incorrect.'
																	    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
																	    F"""but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)"""
																	    F"""= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__lowerCAmelCase					=							apply_spec_augment
												__lowerCAmelCase					=							mask_time_prob
												__lowerCAmelCase					=							mask_time_length
												__lowerCAmelCase					=							mask_time_min_masks
												__lowerCAmelCase					=							mask_feature_prob
												__lowerCAmelCase					=							mask_feature_length
												__lowerCAmelCase					=							mask_feature_min_masks

												# ctc loss
												__lowerCAmelCase					=							ctc_loss_reduction
												__lowerCAmelCase					=							ctc_zero_infinity

												# sequence classification
												__lowerCAmelCase					=							use_weighted_layer_sum
												__lowerCAmelCase					=							classifier_proj_size




							@property
							def        A__  ( self							):
												return functools.reduce(operator.mul					,						self.conv_stride					,						1							)









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    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = None		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							{}

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

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

					if len(UpperCAmelCase__      ) == 1:
										for k in files.keys():
															__lowerCAmelCase					=							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():
															__lowerCAmelCase					=							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]:
															__lowerCAmelCase					=							{k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase					=							labelaid[ex[label_name]]
															yield (d, label)

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

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

					__lowerCAmelCase					=							(
					    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:
										__lowerCAmelCase					=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

					__lowerCAmelCase					=							(
					    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:
										__lowerCAmelCase					=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

					__lowerCAmelCase					=							(
					    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:
										__lowerCAmelCase					=							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     snake_case_			:


							"""simple docstring"""





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





@dataclass
class     snake_case_			:


							"""simple docstring"""





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


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
					__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							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					=							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					=							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		,					)

					__lowerCAmelCase					=							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():
										__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							np.argmax(p.predictions		,					axis=1      )

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

					# Initialize our Trainer
					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )
										__lowerCAmelCase					=							trainer.evaluate()
										__lowerCAmelCase					=							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()






