


'''simple docstring'''






import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
		import jax

		from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=1_00      ,					lowerCamelCase_=13      ,					lowerCamelCase_=30      ,					lowerCamelCase_=2      ,					lowerCamelCase_=3      ,					lowerCamelCase_=True      ,					lowerCamelCase_=True      ,					lowerCamelCase_=32      ,					lowerCamelCase_=5      ,					lowerCamelCase_=4      ,					lowerCamelCase_=37      ,					lowerCamelCase_="gelu"      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=10      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=3      ,					) ->							int:
														lowerCAmelCase__      =			parent
														lowerCAmelCase__      =			vocab_size
														lowerCAmelCase__      =			batch_size
														lowerCAmelCase__      =			image_size
														lowerCAmelCase__      =			patch_size
														lowerCAmelCase__      =			num_channels
														lowerCAmelCase__      =			is_training
														lowerCAmelCase__      =			use_labels
														lowerCAmelCase__      =			hidden_size
														lowerCAmelCase__      =			num_hidden_layers
														lowerCAmelCase__      =			num_attention_heads
														lowerCAmelCase__      =			intermediate_size
														lowerCAmelCase__      =			hidden_act
														lowerCAmelCase__      =			hidden_dropout_prob
														lowerCAmelCase__      =			attention_probs_dropout_prob
														lowerCAmelCase__      =			type_sequence_label_size
														lowerCAmelCase__      =			initializer_range

														# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														lowerCAmelCase__      =			(image_size // patch_size) ** 2
														lowerCAmelCase__      =			num_patches + 1







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCAmelCase__      =			None
														if self.use_labels:
																					lowerCAmelCase__      =			ids_tensor([self.batch_size]      ,					self.type_sequence_label_size     )

														lowerCAmelCase__      =			BeitConfig(
														    vocab_size=self.vocab_size      ,					image_size=self.image_size      ,					patch_size=self.patch_size      ,					num_channels=self.num_channels      ,					hidden_size=self.hidden_size      ,					num_hidden_layers=self.num_hidden_layers      ,					num_attention_heads=self.num_attention_heads      ,					intermediate_size=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					hidden_dropout_prob=self.hidden_dropout_prob      ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,					is_decoder=lowerCamelCase_      ,					initializer_range=self.initializer_range      ,					)

														return config, pixel_values, labels







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[Any]:
														lowerCAmelCase__      =			FlaxBeitModel(config=lowerCamelCase_     )
														lowerCAmelCase__      =			model(lowerCamelCase_     )
														self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[Any]:
														lowerCAmelCase__      =			FlaxBeitForMaskedImageModeling(config=lowerCamelCase_     )
														lowerCAmelCase__      =			model(lowerCamelCase_     )
														self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length - 1, self.vocab_size)     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self.type_sequence_label_size
														lowerCAmelCase__      =			FlaxBeitForImageClassification(config=lowerCamelCase_     )
														lowerCAmelCase__      =			model(lowerCamelCase_     )
														self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.type_sequence_label_size)     )

														# test greyscale images
														lowerCAmelCase__      =			1
														lowerCAmelCase__      =			FlaxBeitForImageClassification(lowerCamelCase_     )

														lowerCAmelCase__      =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCAmelCase__      =			model(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.prepare_config_and_inputs()
														(
														    (
														    lowerCAmelCase__
														)     ,      (
														    lowerCAmelCase__
														)     ,      (
														    lowerCAmelCase__
														)     ,      
														)      =			config_and_inputs
														lowerCAmelCase__      =			{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_flax
class 	a__       (							a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Tuple					=   (
							    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
							)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							None:
														lowerCAmelCase__      =			FlaxBeitModelTester(self     )
														lowerCAmelCase__      =			ConfigTester(self      ,					config_class=lowerCamelCase_      ,					has_text_modality=lowerCamelCase_      ,					hidden_size=37     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														self.config_tester.run_common_tests()







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__      =			model_class(lowerCamelCase_     )
																					lowerCAmelCase__      =			inspect.signature(model.__call__     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCAmelCase__      =			[*signature.parameters.keys()]

																					lowerCAmelCase__      =			['''pixel_values''']
																					self.assertListEqual(arg_names[:1]      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__     ):
																												lowerCAmelCase__      =			self._prepare_for_class(lowerCamelCase_      ,					lowerCamelCase_     )
																												lowerCAmelCase__      =			model_class(lowerCamelCase_     )

																												@jax.jit
																												def model_jitted(lowerCamelCase_      ,					**lowerCamelCase_     ):
																																			return model(pixel_values=lowerCamelCase_      ,					**lowerCamelCase_     )

																												with self.subTest('''JIT Enabled'''     ):
																																			lowerCAmelCase__      =			model_jitted(**lowerCamelCase_     ).to_tuple()

																												with self.subTest('''JIT Disabled'''     ):
																																			with jax.disable_jit():
																																										lowerCAmelCase__      =			model_jitted(**lowerCamelCase_     ).to_tuple()

																												self.assertEqual(len(lowerCamelCase_     )      ,					len(lowerCamelCase_     )     )
																												for jitted_output, output in zip(lowerCamelCase_      ,					lowerCamelCase_     ):
																																			self.assertEqual(jitted_output.shape      ,					output.shape     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														for model_class_name in self.all_model_classes:
																					lowerCAmelCase__      =			model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''     )
																					lowerCAmelCase__      =			model(np.ones((1, 3, 2_24, 2_24)     )     )
																					self.assertIsNotNone(lowerCamelCase_     )


def       _snake_case     (  )		->					int:
							lowerCAmelCase__      =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
							return image






@require_vision
@require_flax
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							@cached_property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''     ) if is_vision_available() else None







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__      =			FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''     )

														lowerCAmelCase__      =			self.default_image_processor
														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			image_processor(images=lowerCamelCase_      ,					return_tensors='''np'''     ).pixel_values

														# prepare bool_masked_pos
														lowerCAmelCase__      =			np.ones((1, 1_96)      ,					dtype=lowerCamelCase_     )

														# forward pass
														lowerCAmelCase__      =			model(pixel_values=lowerCamelCase_      ,					bool_masked_pos=lowerCamelCase_     )
														lowerCAmelCase__      =			outputs.logits

														# verify the logits
														lowerCAmelCase__      =			(1, 1_96, 81_92)
														self.assertEqual(logits.shape      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			np.array(
														    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]     )

														self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]      ,					lowerCamelCase_      ,					atol=1e-2     )     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''     )

														lowerCAmelCase__      =			self.default_image_processor
														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			image_processor(images=lowerCamelCase_      ,					return_tensors='''np'''     )

														# forward pass
														lowerCAmelCase__      =			model(**lowerCamelCase_     )
														lowerCAmelCase__      =			outputs.logits

														# verify the logits
														lowerCAmelCase__      =			(1, 10_00)
														self.assertEqual(logits.shape      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			np.array([-1.2_385, -1.0_987, -1.0_108]     )

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

														lowerCAmelCase__      =			2_81
														self.assertEqual(logits.argmax(-1     ).item()      ,					lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''     )

														lowerCAmelCase__      =			self.default_image_processor
														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			image_processor(images=lowerCamelCase_      ,					return_tensors='''np'''     )

														# forward pass
														lowerCAmelCase__      =			model(**lowerCamelCase_     )
														lowerCAmelCase__      =			outputs.logits

														# verify the logits
														lowerCAmelCase__      =			(1, 2_18_41)
														self.assertEqual(logits.shape      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			np.array([1.6_881, -0.2_787, 0.5_901]     )

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

														lowerCAmelCase__      =			23_96
														self.assertEqual(logits.argmax(-1     ).item()      ,					lowerCamelCase_     )



'''simple docstring'''






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


__UpperCAmelCase					       =							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       _snake_case     (  A				,					A				,					A = 16000 )		->					Any:
							lowerCAmelCase__      =			int(round(sample_rate * max_length ) )
							if len(A ) <= sample_length:
														return wav
							lowerCAmelCase__      =			randint(0				,					len(A ) - sample_length - 1 )
							return wav[random_offset : random_offset + sample_length]




@dataclass
class 	a__       :





							'''simple docstring'''






							lowercase__    :     Optional[str]					=   field(default=a__       ,       metadata={"help": "Name of a dataset from the datasets package"}						)
							lowercase__    :     Optional[str]					=   field(
							    default=a__       ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
							lowercase__    :     Optional[str]					=   field(
							    default=a__       ,       metadata={"help": "A file containing the training audio paths and labels."}						)
							lowercase__    :     Optional[str]					=   field(
							    default=a__       ,       metadata={"help": "A file containing the validation audio paths and labels."}						)
							lowercase__    :     str					=   field(
							    default="train"       ,       metadata={
							        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
							    }       ,       )
							lowercase__    :     str					=   field(
							    default="validation"       ,       metadata={
							        "help": (
							            "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
							        )
							    }       ,       )
							lowercase__    :     str					=   field(
							    default="audio"       ,       metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}       ,       )
							lowercase__    :     str					=   field(
							    default="label"       ,       metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}						)
							lowercase__    :     Optional[int]					=   field(
							    default=a__       ,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }       ,       )
							lowercase__    :     Optional[int]					=   field(
							    default=a__       ,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }       ,       )
							lowercase__    :     float					=   field(
							    default=2_0       ,       metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}       ,       )




@dataclass
class 	a__       :





							'''simple docstring'''






							lowercase__    :     str					=   field(
							    default="facebook/wav2vec2-base"       ,       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       ,       )
							lowercase__    :     Optional[str]					=   field(
							    default=a__       ,       metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
							lowercase__    :     Optional[str]					=   field(
							    default=a__       ,       metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}						)
							lowercase__    :     str					=   field(
							    default="main"       ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,       )
							lowercase__    :     Optional[str]					=   field(
							    default=a__       ,       metadata={"help": "Name or path of preprocessor config."}						)
							lowercase__    :     bool					=   field(
							    default=a__       ,       metadata={"help": "Whether to freeze the feature encoder layers of the model."}						)
							lowercase__    :     bool					=   field(
							    default=a__       ,       metadata={"help": "Whether to generate an attention mask in the feature extractor."}						)
							lowercase__    :     bool					=   field(
							    default=a__       ,       metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }       ,       )
							lowercase__    :     Optional[bool]					=   field(
							    default=a__       ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."}						)
							lowercase__    :     bool					=   field(
							    default=a__       ,       metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}       ,       )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														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       _snake_case     (  )		->					Any:
							# 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.

							lowerCAmelCase__      =			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.
														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
							else:
														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			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'''				,					A				,					A )

							# 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()

							lowerCAmelCase__      =			training_args.get_process_log_level()
							logger.setLevel(A )
							transformers.utils.logging.set_verbosity(A )
							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.
							lowerCAmelCase__      =			None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														lowerCAmelCase__      =			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.
							lowerCAmelCase__      =			DatasetDict()
							lowerCAmelCase__      =			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				,					)
							lowerCAmelCase__      =			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
							lowerCAmelCase__      =			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.
							lowerCAmelCase__      =			raw_datasets.cast_column(
							    data_args.audio_column_name				,					datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )

							lowerCAmelCase__      =			feature_extractor.model_input_names[0]

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

														return output_batch

							def val_transforms(A ):
														lowerCAmelCase__      =			[audio['''array'''] for audio in batch[data_args.audio_column_name]]
														lowerCAmelCase__      =			feature_extractor(A				,					sampling_rate=feature_extractor.sampling_rate )
														lowerCAmelCase__      =			{model_input_name: inputs.get(A )}
														lowerCAmelCase__      =			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.
							lowerCAmelCase__      =			raw_datasets['''train'''].features[data_args.label_column_name].names
							lowerCAmelCase__     ,      lowerCAmelCase__      =			{}, {}
							for i, label in enumerate(A ):
														lowerCAmelCase__      =			str(A )
														lowerCAmelCase__      =			label

							# Load the accuracy metric from the datasets package
							lowerCAmelCase__      =			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(A ):
														lowerCAmelCase__      =			np.argmax(eval_pred.predictions				,					axis=1 )
														return metric.compute(predictions=A				,					references=eval_pred.label_ids )

							lowerCAmelCase__      =			AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path				,					num_labels=len(A )				,					labelaid=A				,					idalabel=A				,					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				,					)
							lowerCAmelCase__      =			AutoModelForAudioClassification.from_pretrained(
							    model_args.model_name_or_path				,					from_tf=bool('''.ckpt''' in model_args.model_name_or_path )				,					config=A				,					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:
																					lowerCAmelCase__      =			(
																					    raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
																					)
														# Set the training transforms
														raw_datasets["train"].set_transform(A				,					output_all_columns=A )

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

							# Initialize our trainer
							lowerCAmelCase__      =			Trainer(
							    model=A				,					args=A				,					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=A				,					tokenizer=A				,					)

							# Training
							if training_args.do_train:
														lowerCAmelCase__      =			None
														if training_args.resume_from_checkpoint is not None:
																					lowerCAmelCase__      =			training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowerCAmelCase__      =			last_checkpoint
														lowerCAmelCase__      =			trainer.train(resume_from_checkpoint=A )
														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:
														lowerCAmelCase__      =			trainer.evaluate()
														trainer.log_metrics('''eval'''				,					A )
														trainer.save_metrics('''eval'''				,					A )

							# Write model card and (optionally) push to hub
							lowerCAmelCase__      =			{
							    '''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(**A )
							else:
														trainer.create_model_card(**A )


if __name__ == "__main__":
		main()