




import functools
import operator

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


UpperCAmelCase_		:				Tuple									=     logging.get_logger(__name__)

UpperCAmelCase_		:				List[Any]									=     {
    "microsoft/unispeech-sat-base-100h-libri-ft": (
        "https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""unispeech-sat"""

						def __init__(		self   :Tuple    , __snake_case   :Union[str, Any]=32    , __snake_case   :Dict=7_68    , __snake_case   :List[Any]=12    , __snake_case   :Any=12    , __snake_case   :List[str]=30_72    , __snake_case   :Union[str, Any]="gelu"    , __snake_case   :List[str]=0.1    , __snake_case   :str=0.1    , __snake_case   :List[str]=0.1    , __snake_case   :Optional[Any]=0.0    , __snake_case   :List[Any]=0.0    , __snake_case   :str=0.1    , __snake_case   :Union[str, Any]=0.1    , __snake_case   :Tuple=0.02    , __snake_case   :List[Any]=1E-5    , __snake_case   :List[str]="group"    , __snake_case   :int="gelu"    , __snake_case   :Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)    , __snake_case   :str=(5, 2, 2, 2, 2, 2, 2)    , __snake_case   :Any=(10, 3, 3, 3, 3, 2, 2)    , __snake_case   :List[Any]=False    , __snake_case   :Optional[int]=1_28    , __snake_case   :Tuple=16    , __snake_case   :Union[str, Any]=False    , __snake_case   :List[str]=True    , __snake_case   :Optional[int]=0.05    , __snake_case   :Tuple=10    , __snake_case   :Tuple=2    , __snake_case   :Dict=0.0    , __snake_case   :Optional[Any]=10    , __snake_case   :Dict=0    , __snake_case   :Tuple=3_20    , __snake_case   :int=2    , __snake_case   :List[str]=0.1    , __snake_case   :Union[str, Any]=1_00    , __snake_case   :Tuple=2_56    , __snake_case   :Optional[Any]=2_56    , __snake_case   :str=0.1    , __snake_case   :Optional[Any]="mean"    , __snake_case   :List[Any]=False    , __snake_case   :Union[str, Any]=False    , __snake_case   :Optional[Any]=2_56    , __snake_case   :Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 15_00)    , __snake_case   :Dict=(5, 3, 3, 1, 1)    , __snake_case   :Tuple=(1, 2, 3, 1, 1)    , __snake_case   :Tuple=5_12    , __snake_case   :Dict=0    , __snake_case   :List[Any]=1    , __snake_case   :List[Any]=2    , __snake_case   :Union[str, Any]=5_04    , **__snake_case   :int    , ):
										'''simple docstring'''

										super().__init__(**__snake_case    , pad_token_id=__snake_case    , bos_token_id=__snake_case    , eos_token_id=__snake_case )
										__magic_name__	:	List[Any]								=hidden_size
										__magic_name__	:	Optional[int]								=feat_extract_norm
										__magic_name__	:	Dict								=feat_extract_activation
										__magic_name__	:	Tuple								=list(__snake_case )
										__magic_name__	:	Dict								=list(__snake_case )
										__magic_name__	:	List[str]								=list(__snake_case )
										__magic_name__	:	Any								=conv_bias
										__magic_name__	:	List[Any]								=num_conv_pos_embeddings
										__magic_name__	:	Any								=num_conv_pos_embedding_groups
										__magic_name__	:	Dict								=len(self.conv_dim )
										__magic_name__	:	Optional[Any]								=num_hidden_layers
										__magic_name__	:	int								=intermediate_size
										__magic_name__	:	List[Any]								=hidden_act
										__magic_name__	:	Tuple								=num_attention_heads
										__magic_name__	:	Union[str, Any]								=hidden_dropout
										__magic_name__	:	Optional[Any]								=attention_dropout
										__magic_name__	:	Optional[Any]								=activation_dropout
										__magic_name__	:	Optional[int]								=feat_proj_dropout
										__magic_name__	:	Dict								=final_dropout
										__magic_name__	:	Optional[int]								=layerdrop
										__magic_name__	:	Dict								=layer_norm_eps
										__magic_name__	:	Optional[Any]								=initializer_range
										__magic_name__	:	int								=vocab_size
										__magic_name__	:	Tuple								=num_clusters
										__magic_name__	:	Dict								=do_stable_layer_norm
										__magic_name__	:	Optional[Any]								=use_weighted_layer_sum

										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)`, but is `len(config.conv_dim) ="""
														    f" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"
														    f" `len(config.conv_kernel) = {len(self.conv_kernel )}`." )

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										__magic_name__	:	Union[str, Any]								=apply_spec_augment
										__magic_name__	:	Tuple								=mask_time_prob
										__magic_name__	:	str								=mask_time_length
										__magic_name__	:	Optional[Any]								=mask_time_min_masks
										__magic_name__	:	Optional[Any]								=mask_feature_prob
										__magic_name__	:	List[str]								=mask_feature_length
										__magic_name__	:	int								=mask_feature_min_masks

										# parameters for pretraining with codevector quantized representations
										__magic_name__	:	Optional[int]								=num_codevectors_per_group
										__magic_name__	:	List[str]								=num_codevector_groups
										__magic_name__	:	Optional[int]								=contrastive_logits_temperature
										__magic_name__	:	Tuple								=feat_quantizer_dropout
										__magic_name__	:	Any								=num_negatives
										__magic_name__	:	Optional[int]								=codevector_dim
										__magic_name__	:	List[Any]								=proj_codevector_dim
										__magic_name__	:	int								=diversity_loss_weight

										# ctc loss
										__magic_name__	:	List[Any]								=ctc_loss_reduction
										__magic_name__	:	str								=ctc_zero_infinity

										# SequenceClassification-specific parameter. Feel free to ignore for other classes.
										__magic_name__	:	str								=classifier_proj_size

										# XVector-specific parameters. Feel free to ignore for other classes.
										__magic_name__	:	Union[str, Any]								=list(__snake_case )
										__magic_name__	:	Union[str, Any]								=list(__snake_case )
										__magic_name__	:	Any								=list(__snake_case )
										__magic_name__	:	Dict								=xvector_output_dim


						@property
						def     A__						(		self   :Any ):
										'''simple docstring'''

										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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

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

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=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():
												__magic_name__	:	Optional[Any]								=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]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

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

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

				__magic_name__	:	Union[str, Any]								=(
				    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:
								__magic_name__	:	Optional[Any]								=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

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

				__magic_name__	:	Any								=(
				    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:
								__magic_name__	:	Optional[int]								=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						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=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




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




def 		lowerCAmelCase_   (	):
				# 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.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, 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.

				__magic_name__	:	Dict								=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	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=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	,     )

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

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

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




