


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__	(      a	:   int	,  a	:   Optional[Any]	,  a	:   Any	,  a	:   Optional[int]	,  a	:   Any	,  a	:   List[Any] = None	,  )    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    {}

			if train_file is not None:
						a__ :Tuple						  =    [train_file]
			if eval_file is not None:
						a__ :List[str]						  =    [eval_file]
			if test_file is not None:
						a__ :Optional[Any]						  =    [test_file]

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

			if len(_SCREAMING_SNAKE_CASE				) == 1:
						for k in files.keys():
									a__ :Optional[Any]						  =    ds[k].map(
									    lambda a				: 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():
									a__ :Union[str, Any]						  =    ds[k].map(
									    lambda a				: 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]:
									a__ :Optional[int]						  =    {k: v for k, v in ex.items() if k in input_names}
									a__ :Tuple						  =    labelaid[ex[label_name]]
									yield (d, label)

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

			def gen_test():
						for ex in transformed_ds[datasets.Split.TEST]:
									a__ :List[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__ :List[str]						  =    (
			    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:
						a__ :str						  =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

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

			a__ :List[str]						  =    (
			    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:
						a__ :Union[str, Any]						  =    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

			return train_ds, val_ds, test_ds, labelaid


snake_case__			 =						logging.getLogger(__name__)


@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     field(metadata={'help': 'Which column contains the label'})
						lowerCamelCase_         =     field(default=snake_case__			,metadata={'help': 'The path of the training file'})
						lowerCamelCase_         =     field(default=snake_case__			,metadata={'help': 'The path of the development file'})
						lowerCamelCase_         =     field(default=snake_case__			,metadata={'help': 'The path of the test file'})
						lowerCamelCase_         =     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_         =     field(
						    default=snake_case__			,metadata={'help': 'Overwrite the cached training and evaluation sets'})






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






def 				lowerCamelCase__	(      )    ->	Union[str, Any]:





			"""simple docstring"""
			# 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.
			a__ :Dict						  =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
			a__  ,  a__  ,  a__ :List[Any]						  =    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__ :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__  ,  a__  ,  a__  ,  a__ :List[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	,  )

			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(_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():
						a__ :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(a	:   Any				) -> Dict:
						a__ :Tuple						  =    np.argmax(p.predictions	,  axis=1				)

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

			# Initialize our Trainer
			a__ :Dict						  =    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
			a__ :Optional[Any]						  =    {}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"				)
						a__ :Optional[int]						  =    trainer.evaluate()
						a__ :Union[str, 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()






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

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


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    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
									a__ :int						  =    apply_spec_augment
									a__ :Optional[Any]						  =    mask_time_prob
									a__ :List[Any]						  =    mask_time_length
									a__ :Any						  =    mask_time_min_masks
									a__ :Any						  =    mask_feature_prob
									a__ :Dict						  =    mask_feature_length
									a__ :Union[str, Any]						  =    mask_feature_min_masks

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									return functools.reduce(operator.mul   ,     self.conv_stride   ,     1						)


