


"""simple docstring"""
import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
 from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor




class 	lowercase__				(					A_				):





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					Dict   =		self.config_class(**self.inputs_dict)
      self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """tf_padding"""))
      self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """depth_multiplier"""))







class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=13      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=0.25      ,   SCREAMING_SNAKE_CASE=8      ,   SCREAMING_SNAKE_CASE=8      ,   SCREAMING_SNAKE_CASE=6      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE="relu6"      ,   SCREAMING_SNAKE_CASE=1280      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=10      ,   SCREAMING_SNAKE_CASE=None      ,   )  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		parent
      _lowerCamelCase	:					Tuple   =		batch_size
      _lowerCamelCase	:					Optional[Any]   =		num_channels
      _lowerCamelCase	:					Tuple   =		image_size
      _lowerCamelCase	:					Tuple   =		depth_multiplier
      _lowerCamelCase	:					int   =		depth_divisible_by
      _lowerCamelCase	:					Tuple   =		min_depth
      _lowerCamelCase	:					Any   =		expand_ratio
      _lowerCamelCase	:					int   =		tf_padding
      _lowerCamelCase	:					str   =		output_stride
      _lowerCamelCase	:					str   =		first_layer_is_expansion
      _lowerCamelCase	:					Optional[int]   =		finegrained_output
      _lowerCamelCase	:					Union[str, Any]   =		hidden_act
      _lowerCamelCase	:					Optional[int]   =		last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
      _lowerCamelCase	:					Any   =		classifier_dropout_prob
      _lowerCamelCase	:					Any   =		use_labels
      _lowerCamelCase	:					Optional[int]   =		is_training
      _lowerCamelCase	:					Tuple   =		num_labels
      _lowerCamelCase	:					int   =		initializer_range
      _lowerCamelCase	:					List[str]   =		scope





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Any   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      _lowerCamelCase	:					List[Any]   =		None
      _lowerCamelCase	:					str   =		None
      if self.use_labels:
         _lowerCamelCase	:					List[Any]   =		ids_tensor([self.batch_size]      ,   self.num_labels)
         _lowerCamelCase	:					Union[str, Any]   =		ids_tensor([self.batch_size, self.image_size, self.image_size]      ,   self.num_labels)

      _lowerCamelCase	:					Dict   =		self.get_config()

      return config, pixel_values, labels, pixel_labels





   def 		UpperCamelCase_  (							self)  ->  str:
      return MobileNetVaConfig(
          num_channels=self.num_channels      ,   image_size=self.image_size      ,   depth_multiplier=self.depth_multiplier      ,   depth_divisible_by=self.depth_divisible_by      ,   min_depth=self.min_depth      ,   expand_ratio=self.expand_ratio      ,   output_stride=self.output_stride      ,   first_layer_is_expansion=self.first_layer_is_expansion      ,   finegrained_output=self.finegrained_output      ,   hidden_act=self.hidden_act      ,   tf_padding=self.tf_padding      ,   classifier_dropout_prob=self.classifier_dropout_prob      ,   initializer_range=self.initializer_range      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					Tuple   =		MobileNetVaModel(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					str   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,   (
              self.batch_size,
              self.last_hidden_size,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )      ,   )
      self.parent.assertEqual(
          result.pooler_output.shape      ,   (self.batch_size, self.last_hidden_size)      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Tuple:
      _lowerCamelCase	:					Tuple   =		self.num_labels
      _lowerCamelCase	:					Union[str, Any]   =		MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Optional[int]   =		model(SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Tuple   =		self.num_labels
      _lowerCamelCase	:					List[str]   =		MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					int   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.logits.shape      ,   (
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )      ,   )
      _lowerCamelCase	:					Tuple   =		model(SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.logits.shape      ,   (
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Tuple   =		self.prepare_config_and_inputs()
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		config_and_inputs
      _lowerCamelCase	:					Optional[Any]   =		{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(
       (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase							       =					(
       {
           '''feature-extraction''': MobileNetVaModel,
           '''image-classification''': MobileNetVaForImageClassification,
           '''image-segmentation''': MobileNetVaForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					int   =		MobileNetVaModelTester(self)
      _lowerCamelCase	:					Dict   =		MobileNetVaConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   has_text_modality=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      self.config_tester.run_common_tests()





   @unittest.skip(reason="""MobileNetV2 does not use inputs_embeds""")
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      pass





   @unittest.skip(reason="""MobileNetV2 does not support input and output embeddings""")
   def 		UpperCamelCase_  (							self)  ->  Dict:
      pass





   @unittest.skip(reason="""MobileNetV2 does not output attentions""")
   def 		UpperCamelCase_  (							self)  ->  Any:
      pass





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					Union[str, Any]   =		model_class(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Tuple   =		inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCamelCase	:					Optional[Any]   =		[*signature.parameters.keys()]

         _lowerCamelCase	:					Optional[Any]   =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      def check_hidden_states_output(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					List[str]   =		model_class(SCREAMING_SNAKE_CASE)
         model.to(SCREAMING_SNAKE_CASE)
         model.eval()

         with torch.no_grad():
            _lowerCamelCase	:					List[Any]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE))

         _lowerCamelCase	:					Optional[Any]   =		outputs.hidden_states

         _lowerCamelCase	:					Tuple   =		16
         self.assertEqual(len(SCREAMING_SNAKE_CASE)      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					List[str]   =		True
         check_hidden_states_output(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCamelCase	:					Optional[Any]   =		True

         check_hidden_states_output(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					str   =		MobileNetVaModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)







def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image





@require_torch
@require_vision
class 	lowercase__				(					unittest.TestCase				):





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Any:
      return (
          MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224""") if is_vision_available() else None
      )





   @slow
   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					List[str]   =		MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224""").to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		self.default_image_processor
      _lowerCamelCase	:					int   =		prepare_img()
      _lowerCamelCase	:					Optional[Any]   =		image_processor(images=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)

      # forward pass
      with torch.no_grad():
         _lowerCamelCase	:					str   =		model(**SCREAMING_SNAKE_CASE)

      # verify the logits
      _lowerCamelCase	:					Optional[int]   =		torch.Size((1, 1001))
      self.assertEqual(outputs.logits.shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		torch.tensor([0.24_45, -1.19_93, 0.19_05]).to(SCREAMING_SNAKE_CASE)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[int]   =		MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513""")
      _lowerCamelCase	:					str   =		model.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513""")

      _lowerCamelCase	:					Union[str, Any]   =		prepare_img()
      _lowerCamelCase	:					Any   =		image_processor(images=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)

      # forward pass
      with torch.no_grad():
         _lowerCamelCase	:					List[Any]   =		model(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		outputs.logits

      # verify the logits
      _lowerCamelCase	:					Union[str, Any]   =		torch.Size((1, 21, 65, 65))
      self.assertEqual(logits.shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		torch.tensor(
          [
              [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
              [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
              [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
          ]      ,   device=SCREAMING_SNAKE_CASE      ,   )

      self.assertTrue(torch.allclose(logits[0, :3, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))



import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


__magic_name__  							=	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = 16_000				):
    snake_case__    =					int(round(sample_rate * max_length				)				)
    if len(__lowerCAmelCase				) <= sample_length:
        return wav
    snake_case__    =					randint(0     ,		len(__lowerCAmelCase				) - sample_length - 1				)
    return wav[random_offset : random_offset + sample_length]




@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		Optional[str]     						=  field(default=__UpperCamelCase							,       metadata={'help': 'Name of a dataset from the datasets package'}			)
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'A file containing the training audio paths and labels.'}			)
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'A file containing the validation audio paths and labels.'}			)
      _A    :		str     						=  field(
          default='train'							,       metadata={
              'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
          }							,       )
      _A    :		str     						=  field(
          default='validation'							,       metadata={
              'help': (
                  'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
              )
          }							,       )
      _A    :		str     						=  field(
          default='audio'							,       metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''}							,       )
      _A    :		str     						=  field(
          default='label'							,       metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''}			)
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          }							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                  'value if set.'
              )
          }							,       )
      _A    :		float     						=  field(
          default=20							,       metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'}							,       )





@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		str     						=  field(
          default='facebook/wav2vec2-base'							,       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							,       )
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'}			)
      _A    :		str     						=  field(
          default='main'							,       metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,       )
      _A    :		Optional[str]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Name or path of preprocessor config.'}			)
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Whether to freeze the feature encoder layers of the model.'}			)
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Whether to generate an attention mask in the feature extractor.'}			)
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }							,       )
      _A    :		Optional[bool]     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}			)
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}							,       )
      def A_      (      self			):
          if not self.freeze_feature_extractor and self.freeze_feature_encoder:
              warnings.warn(
                  "The argument `--freeze_feature_extractor` is deprecated and "
                  "will be removed in a future version. Use `--freeze_feature_encoder`"
                  "instead. Setting `freeze_feature_encoder==True`."		,						lowerCamelCase		,						)
          if self.freeze_feature_extractor and not self.freeze_feature_encoder:
              raise ValueError(
                  "The argument `--freeze_feature_extractor` is deprecated and "
                  "should not be used in combination with `--freeze_feature_encoder`."
                  "Only make use of `--freeze_feature_encoder`."			)





def 						SCREAMING_SNAKE_CASE__     (   ):
    # 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.

    snake_case__    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
    if len(sys.argv				) == 2 and sys.argv[1].endswith(".json"				):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case__    ,       snake_case__    ,       snake_case__    =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        snake_case__    ,       snake_case__    ,       snake_case__    =					parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_audio_classification"     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,		datefmt="%m/%d/%Y %H:%M:%S"     ,		handlers=[logging.StreamHandler(sys.stdout				)]     ,		)

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    snake_case__    =					training_args.get_process_log_level()
    logger.setLevel(__lowerCAmelCase				)
    transformers.utils.logging.set_verbosity(__lowerCAmelCase				)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
        + F"""distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"""				)
    logger.info(F"""Training/evaluation parameters {training_args}"""				)

    # Set seed before initializing model.
    set_seed(training_args.seed				)

    # Detecting last checkpoint.
    snake_case__    =					None
    if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
        snake_case__    =					get_last_checkpoint(training_args.output_dir				)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                "Use --overwrite_output_dir to train from scratch."				)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."				)

    # Initialize our dataset and prepare it for the audio classification task.
    snake_case__    =					DatasetDict()
    snake_case__    =					load_dataset(
        data_args.dataset_name     ,		data_args.dataset_config_name     ,		split=data_args.train_split_name     ,		use_auth_token=True if model_args.use_auth_token else None     ,		)
    snake_case__    =					load_dataset(
        data_args.dataset_name     ,		data_args.dataset_config_name     ,		split=data_args.eval_split_name     ,		use_auth_token=True if model_args.use_auth_token else None     ,		)

    if data_args.audio_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
            "Make sure to set `--audio_column_name` to the correct audio column - one of "
            F"""{", ".join(raw_datasets["train"].column_names				)}."""				)

    if data_args.label_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
            "Make sure to set `--label_column_name` to the correct text column - one of "
            F"""{", ".join(raw_datasets["train"].column_names				)}."""				)

    # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
    # transformer outputs in the classifier, but it doesn't always lead to better accuracy
    snake_case__    =					AutoFeatureExtractor.from_pretrained(
        model_args.feature_extractor_name or model_args.model_name_or_path     ,		return_attention_mask=model_args.attention_mask     ,		cache_dir=model_args.cache_dir     ,		revision=model_args.model_revision     ,		use_auth_token=True if model_args.use_auth_token else None     ,		)

    # `datasets` takes care of automatically loading and resampling the audio,
    # so we just need to set the correct target sampling rate.
    snake_case__    =					raw_datasets.cast_column(
        data_args.audio_column_name     ,		datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate				)				)

    snake_case__    =					feature_extractor.model_input_names[0]

    def train_transforms(__lowerCAmelCase				):
        snake_case__    =					[]
        for audio in batch[data_args.audio_column_name]:
            snake_case__    =					random_subsample(
                audio["array"]     ,		max_length=data_args.max_length_seconds     ,		sample_rate=feature_extractor.sampling_rate				)
            subsampled_wavs.append(__lowerCAmelCase				)
        snake_case__    =					feature_extractor(__lowerCAmelCase     ,		sampling_rate=feature_extractor.sampling_rate				)
        snake_case__    =					{model_input_name: inputs.get(__lowerCAmelCase				)}
        snake_case__    =					list(batch[data_args.label_column_name]				)

        return output_batch

    def val_transforms(__lowerCAmelCase				):
        snake_case__    =					[audio["array"] for audio in batch[data_args.audio_column_name]]
        snake_case__    =					feature_extractor(__lowerCAmelCase     ,		sampling_rate=feature_extractor.sampling_rate				)
        snake_case__    =					{model_input_name: inputs.get(__lowerCAmelCase				)}
        snake_case__    =					list(batch[data_args.label_column_name]				)

        return output_batch

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    snake_case__    =					raw_datasets["train"].features[data_args.label_column_name].names
    snake_case__    ,       snake_case__    =					{}, {}
    for i, label in enumerate(__lowerCAmelCase				):
        snake_case__    =					str(__lowerCAmelCase				)
        snake_case__    =					label

    # Load the accuracy metric from the datasets package
    snake_case__    =					evaluate.load("accuracy"				)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
    # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
    def compute_metrics(__lowerCAmelCase				):
        snake_case__    =					np.argmax(eval_pred.predictions     ,		axis=1				)
        return metric.compute(predictions=__lowerCAmelCase     ,		references=eval_pred.label_ids				)

    snake_case__    =					AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path     ,		num_labels=len(__lowerCAmelCase				)     ,		labelaid=__lowerCAmelCase     ,		idalabel=__lowerCAmelCase     ,		finetuning_task="audio-classification"     ,		cache_dir=model_args.cache_dir     ,		revision=model_args.model_revision     ,		use_auth_token=True if model_args.use_auth_token else None     ,		)
    snake_case__    =					AutoModelForAudioClassification.from_pretrained(
        model_args.model_name_or_path     ,		from_tf=bool(".ckpt" in model_args.model_name_or_path				)     ,		config=__lowerCAmelCase     ,		cache_dir=model_args.cache_dir     ,		revision=model_args.model_revision     ,		use_auth_token=True if model_args.use_auth_token else None     ,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,		)

    # freeze the convolutional waveform encoder
    if model_args.freeze_feature_encoder:
        model.freeze_feature_encoder()

    if training_args.do_train:
        if data_args.max_train_samples is not None:
            snake_case__    =					(
                raw_datasets["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
            )
        # Set the training transforms
        raw_datasets["train"].set_transform(__lowerCAmelCase     ,		output_all_columns=__lowerCAmelCase				)

    if training_args.do_eval:
        if data_args.max_eval_samples is not None:
            snake_case__    =					(
                raw_datasets["eval"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
            )
        # Set the validation transforms
        raw_datasets["eval"].set_transform(__lowerCAmelCase     ,		output_all_columns=__lowerCAmelCase				)

    # Initialize our trainer
    snake_case__    =					Trainer(
        model=__lowerCAmelCase     ,		args=__lowerCAmelCase     ,		train_dataset=raw_datasets["train"] if training_args.do_train else None     ,		eval_dataset=raw_datasets["eval"] if training_args.do_eval else None     ,		compute_metrics=__lowerCAmelCase     ,		tokenizer=__lowerCAmelCase     ,		)

    # Training
    if training_args.do_train:
        snake_case__    =					None
        if training_args.resume_from_checkpoint is not None:
            snake_case__    =					training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            snake_case__    =					last_checkpoint
        snake_case__    =					trainer.train(resume_from_checkpoint=__lowerCAmelCase				)
        trainer.save_model()
        trainer.log_metrics("train"     ,		train_result.metrics				)
        trainer.save_metrics("train"     ,		train_result.metrics				)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        snake_case__    =					trainer.evaluate()
        trainer.log_metrics("eval"     ,		__lowerCAmelCase				)
        trainer.save_metrics("eval"     ,		__lowerCAmelCase				)

    # Write model card and (optionally) push to hub
    snake_case__    =					{
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "audio-classification",
        "dataset": data_args.dataset_name,
        "tags": ["audio-classification"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**__lowerCAmelCase				)
    else:
        trainer.create_model_card(**__lowerCAmelCase				)


if __name__ == "__main__":
     main()



