
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser




def 	A_ (							snake_case							:   Any=None ,      snake_case							:   Union[str, Any]=None )      ->    Union[str, Any]:



						'''simple docstring'''

						return field(default_factory=lambda: default ,      metadata=snake_case )






@dataclass
class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							_snake_case     		=  field(
							    metadata={'help': 'The csv file to plot.'}   ,		)
							_snake_case     		=  field(
							    default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}   ,		)
							_snake_case     		=  field(
							    default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}   ,		)
							_snake_case     		=  field(
							    default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Disable logarithmic scale when plotting'}   ,		)
							_snake_case     		=  field(
							    default=SCREAMING_SNAKE_CASE_   ,		metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    }   ,		)
							_snake_case     		=  field(
							    default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}   ,		)
							_snake_case     		=  list_field(
							    default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}    )





def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						try:
												int(snake_case )
												return True
						except ValueError:
												return False




def 	A_ (							snake_case							:   int )      ->    Any:



						'''simple docstring'''

						try:
												float(snake_case )
												return True
						except ValueError:
												return False






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			args
													__UpperCamelCase       =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

													with open(self.args.csv_file     ,	newline=''''''			) as csv_file:
																			__UpperCamelCase       =			csv.DictReader(SCREAMING_SNAKE_CASE_			)
																			for row in reader:
																									__UpperCamelCase       =			row['''model''']
																									self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']			)			)
																									self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']			)			)
																									if can_convert_to_int(row['''result''']			):
																															# value is not None
																															__UpperCamelCase       =			int(row['''result''']			)
																									elif can_convert_to_float(row['''result''']			):
																															# value is not None
																															__UpperCamelCase       =			float(row['''result''']			)



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





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			plt.subplots()
													__UpperCamelCase       =			'''Time usage''' if self.args.is_time else '''Memory usage'''
													__UpperCamelCase       =			title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

													if not self.args.no_log_scale:
																			# set logarithm scales
																			ax.set_xscale('''log'''			)
																			ax.set_yscale('''log'''			)

													for axis in [ax.xaxis, ax.yaxis]:
																			axis.set_major_formatter(ScalarFormatter()			)

													for model_name_idx, model_name in enumerate(self.result_dict.keys()			):
																			__UpperCamelCase       =			sorted(set(self.result_dict[model_name]['''bsz''']			)			)
																			__UpperCamelCase       =			sorted(set(self.result_dict[model_name]['''seq_len''']			)			)
																			__UpperCamelCase       =			self.result_dict[model_name]['''result''']

																			((__UpperCamelCase)  ,							(__UpperCamelCase))       =			(
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			__UpperCamelCase       =			(
																			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																			)

																			for inner_loop_value in inner_loop_array:
																									if self.args.plot_along_batch:
																															__UpperCamelCase       =			np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,	dtype=SCREAMING_SNAKE_CASE_     ,	)
																									else:
																															__UpperCamelCase       =			np.asarray(
																															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,	dtype=np.floataa     ,	)

																									((__UpperCamelCase)  ,							(__UpperCamelCase))       =			(
																									    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																									)

																									__UpperCamelCase       =			np.asarray(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)[: len(SCREAMING_SNAKE_CASE_			)]
																									plt.scatter(
																									    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"			)
																									plt.plot(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	'''--'''			)

																			title_str += F" {label_model_name} vs."

													__UpperCamelCase       =			title_str[:-4]
													__UpperCamelCase       =			'''Time in s''' if self.args.is_time else '''Memory in MB'''

													# plot
													plt.title(SCREAMING_SNAKE_CASE_			)
													plt.xlabel(SCREAMING_SNAKE_CASE_			)
													plt.ylabel(SCREAMING_SNAKE_CASE_			)
													plt.legend()

													if self.args.figure_png_file is not None:
																			plt.savefig(self.args.figure_png_file			)
													else:
																			plt.show()





def 	A_ (							)      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			HfArgumentParser(snake_case )
						__UpperCamelCase       =			parser.parse_args_into_dataclasses()[0]
						__UpperCamelCase       =			Plot(args=snake_case )
						plot.plot()


if __name__ == "__main__":
				main()






import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_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 torch import nn

				from transformers import ASTForAudioClassification, ASTModel
				from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)


if is_torchaudio_available():
				import torchaudio

				from transformers import ASTFeatureExtractor






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=24     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=2     ,	)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			patch_size
													__UpperCamelCase       =			max_length
													__UpperCamelCase       =			num_mel_bins
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_labels
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			type_sequence_label_size
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			scope
													__UpperCamelCase       =			frequency_stride
													__UpperCamelCase       =			time_stride

													# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
													__UpperCamelCase       =			(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
													__UpperCamelCase       =			(self.max_length - self.patch_size) // self.time_stride + 1
													__UpperCamelCase       =			frequency_out_dimension * time_out_dimension
													__UpperCamelCase       =			num_patches + 2



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]			)

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

													__UpperCamelCase       =			self.get_config()

													return config, input_values, labels



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													return ASTConfig(
													    patch_size=self.patch_size     ,	max_length=self.max_length     ,	num_mel_bins=self.num_mel_bins     ,	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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	frequency_stride=self.frequency_stride     ,	time_stride=self.time_stride     ,	)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			ASTModel(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							
													)       =			config_and_inputs
													__UpperCamelCase       =			{'''input_values''': input_values}
													return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (
							    (
							        ASTModel,
							        ASTForAudioClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case     		=  (
							    {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
							    if is_torch_available()
							    else {}
							)
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													if pipeline_test_casse_name == "AudioClassificationPipelineTests":
																			return True

													return False



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			ASTModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							@unittest.skip(reason='''AST does not use inputs_embeds'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)			)
																			__UpperCamelCase       =			model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_     ,	nn.Linear			)			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCamelCase       =			['''input_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       =			ASTModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





def 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			hf_hub_download(
						    repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' ,      filename='''sample_audio.flac''' ,      repo_type='''dataset''' )

						__UpperCamelCase  ,							__UpperCamelCase       =			torchaudio.load(snake_case )

						return audio, sampling_rate






@require_torch
@require_torchaudio
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													return (
													    ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			)
													    if is_torchaudio_available()
													    else None
													)



							@slow
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.default_feature_extractor
													__UpperCamelCase       =			ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			).to(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			self.default_feature_extractor
													__UpperCamelCase  ,							__UpperCamelCase       =			prepare_audio()
													__UpperCamelCase       =			audio.squeeze().numpy()
													__UpperCamelCase       =			feature_extractor(SCREAMING_SNAKE_CASE_     ,	sampling_rate=SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 527)			)
													self.assertEqual(outputs.logits.shape     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]			).to(SCREAMING_SNAKE_CASE_			)

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




