





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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase = None							,   )						-> int:
 UpperCamelCase	:					List[str]       	=   {}

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

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

 if len(_lowerCAmelCase     ) == 1:
  for k in files.keys():
   UpperCamelCase	:					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():
   UpperCamelCase	:					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]:
   UpperCamelCase	:					Union[str, Any]       	=   {k: v for k, v in ex.items() if k in input_names}
   UpperCamelCase	:					Union[str, Any]       	=   labelaid[ex[label_name]]
   yield (d, label)

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

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

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

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

 UpperCamelCase	:					Tuple       	=   (
     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:
  UpperCamelCase	:					List[str]       	=   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

 return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase       :     Tuple   =     logging.getLogger(__name__)



@dataclass
class 					A__   :
      _UpperCAmelCase	:int						    =   field(metadata={'help': 'Which column contains the label'}			)
      _UpperCAmelCase	:str						    =   field(default=__snake_case       ,   metadata={'help': 'The path of the training file'}			)
      _UpperCAmelCase	:Optional[str]						    =   field(default=__snake_case       ,   metadata={'help': 'The path of the development file'}			)
      _UpperCAmelCase	:Optional[str]						    =   field(default=__snake_case       ,   metadata={'help': 'The path of the test file'}			)
      _UpperCAmelCase	:int						    =   field(
          default=1_2_8       ,   metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }       ,   )
      _UpperCAmelCase	:bool						    =   field(
          default=__snake_case       ,   metadata={'help': 'Overwrite the cached training and evaluation sets'}			)



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



def 		A_				(     )						-> Tuple:
 # 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.
 UpperCamelCase	:					Tuple       	=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
 UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					int       	=   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.

 UpperCamelCase	:					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							,   )

 UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					Dict       	=   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							,   )

 UpperCamelCase	:					List[str]       	=   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():
  UpperCamelCase	:					Union[str, 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:
  UpperCamelCase	:					List[Any]       	=   np.argmax(p.predictions							,   axis=1     )

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

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







import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowerCamelCase       :     str   =     random.Random()

if is_torch_available():
    import torch



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase=1.0							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None     )						-> Optional[Any]:
 if rng is None:
  UpperCamelCase	:					Optional[int]       	=   global_rng

 UpperCamelCase	:					Optional[Any]       	=   []
 for batch_idx in range(shape[0]     ):
  values.append([]     )
  for _ in range(shape[1]     ):
   values[-1].append(rng.random() * scale     )

 return values



class 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=7       ,							A_=400       ,							A_=2000       ,							A_=1       ,							A_=0.0       ,							A_=1_6000       ,							A_=True       ,							A_=True       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   parent
       UpperCamelCase	:					List[Any]       	=   batch_size
       UpperCamelCase	:					List[Any]       	=   min_seq_length
       UpperCamelCase	:					List[str]       	=   max_seq_length
       UpperCamelCase	:					int       	=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCamelCase	:					Union[str, Any]       	=   feature_size
       UpperCamelCase	:					List[str]       	=   padding_value
       UpperCamelCase	:					Optional[Any]       	=   sampling_rate
       UpperCamelCase	:					List[str]       	=   return_attention_mask
       UpperCamelCase	:					List[Any]       	=   do_normalize





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }





      def        __UpperCamelCase( self       ,							A_=False       ,							A_=False   ):
       '''simple docstring'''




       def _flatten(A_   ):
        return list(itertools.chain(*A_   )   )

       if equal_length:
        UpperCamelCase	:					List[str]       	=   floats_list((self.batch_size, self.max_seq_length)   )
       else:
        # make sure that inputs increase in size
        UpperCamelCase	:					Dict       	=   [
            _flatten(floats_list((x, self.feature_size)   )   )
            for x in range(self.min_seq_length       ,							self.max_seq_length       ,							self.seq_length_diff   )
        ]

       if numpify:
        UpperCamelCase	:					Union[str, Any]       	=   [np.asarray(A_   ) for x in speech_inputs]

       return speech_inputs



@require_torch
@require_torchaudio
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   ASTFeatureExtractor





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractionTester(self   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       # create three inputs of length 800, 1000, and 1200
       UpperCamelCase	:					Tuple       	=   [floats_list((1, x)   )[0] for x in range(800       ,							1400       ,							200   )]
       UpperCamelCase	:					Dict       	=   [np.asarray(A_   ) for speech_input in speech_inputs]

       # Test not batched input
       UpperCamelCase	:					Dict       	=   feat_extract(speech_inputs[0]       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract(np_speech_inputs[0]       ,							return_tensors="np"   ).input_values
       self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test batched
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       for enc_seq_a, enc_seq_a in zip(A_       ,							A_   ):
        self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test 2-D numpy arrays are batched.
       UpperCamelCase	:					Dict       	=   [floats_list((1, x)   )[0] for x in (800, 800, 800)]
       UpperCamelCase	:					int       	=   np.asarray(A_   )
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					List[str]       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       for enc_seq_a, enc_seq_a in zip(A_       ,							A_   ):
        self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import torch

       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       UpperCamelCase	:					int       	=   np.random.rand(100   ).astype(np.floataa   )
       UpperCamelCase	:					str       	=   np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
        UpperCamelCase	:					List[Any]       	=   feature_extractor.pad([{"input_values": inputs}]       ,							return_tensors="np"   )
        self.assertTrue(np_processed.input_values.dtype == np.floataa   )
        UpperCamelCase	:					List[str]       	=   feature_extractor.pad([{"input_values": inputs}]       ,							return_tensors="pt"   )
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       from datasets import load_dataset

       UpperCamelCase	:					Dict       	=   load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,							"clean"       ,							split="validation"   )
       # automatic decoding with librispeech
       UpperCamelCase	:					Any       	=   ds.sort("id"   ).select(range(A_   )   )[:num_samples]["audio"]

       return [x["array"] for x in speech_samples]





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   torch.tensor(
           [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
            -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
            -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
            -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]   )
       # fmt: on

       UpperCamelCase	:					List[Any]       	=   self._load_datasamples(1   )
       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractor()
       UpperCamelCase	:					str       	=   feature_extractor(A_       ,							return_tensors="pt"   ).input_values
       self.assertEquals(input_values.shape       ,							(1, 1024, 128)   )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,							A_       ,							atol=1e-4   )   )
