from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class 					lowercase__						(   UpperCamelCase_):


						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				float						):





												'''simple docstring'''





												return 0.0






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
						return lowest, highest
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[Any]							=							512
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[1] + [0] * (size - 1)
						SCREAMING_SNAKE_CASE							:    Tuple							=							[filter_type.process(_lowercase						) for item in inputs]

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[0] * (samplerate - size)  # zero-padding
						outputs += filler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							np.abs(np.fft.fft(_lowercase						)						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							20 * np.logaa(_lowercase						)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24		,  samplerate / 2 - 1						)
						plt.xlabel('''Frequency (Hz)'''						)
						plt.xscale('''log'''						)

						# Display within reasonable bounds
						SCREAMING_SNAKE_CASE							:    str							=							get_bounds(_lowercase		,  _lowercase						)
						plt.ylim(max([-80, bounds[0]]						)		,  min([80, bounds[1]]						)						)
						plt.ylabel('''Gain (dB)'''						)

						plt.plot(_lowercase						)
						plt.show()



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							512
						SCREAMING_SNAKE_CASE							:    int							=							[1] + [0] * (size - 1)
						SCREAMING_SNAKE_CASE							:    List[str]							=							[filter_type.process(_lowercase						) for item in inputs]

						SCREAMING_SNAKE_CASE							:    Any							=							[0] * (samplerate - size)  # zero-padding
						outputs += filler
						SCREAMING_SNAKE_CASE							:    Dict							=							np.angle(np.fft.fft(_lowercase						)						)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24		,  samplerate / 2 - 1						)
						plt.xlabel('''Frequency (Hz)'''						)
						plt.xscale('''log'''						)

						plt.ylim(-2 * pi		,  2 * pi						)
						plt.ylabel('''Phase shift (Radians)'''						)
						plt.plot(np.unwrap(_lowercase		,  -2 * pi						)						)
						plt.show()






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												for model_result in results.values():
																		for batch_size, sequence_length in zip(model_result['''bs''']    ,		model_result['''ss''']						):
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model_result['''result'''][batch_size][sequence_length]
																								self.assertIsNotNone(UpperCamelCase__						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sgugger/tiny-distilbert-classification'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		only_pretrain_model=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    str							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''patrickvonplaten/t5-tiny-random'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		configs=[config]						)
												SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''						)						) == 0    ,		'''Cannot do xla on CPU.'''						)
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		use_xla=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		save_to_csv=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		inference_time_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)    ,		inference_memory_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)    ,		env_info_csv_file=os.path.join(UpperCamelCase__    ,		'''env.csv'''						)    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		benchmark.run()
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''env.csv'''						)						).exists()						)



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							'''sshleifer/tiny-gpt2'''

												def _check_summary_is_not_empty(UpperCamelCase__	:				Dict						):
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''sequential'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''cumulative'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''current'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''total'''						)						)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		log_filename=os.path.join(UpperCamelCase__    ,		'''log.txt'''						)    ,		log_print=UpperCamelCase__    ,		trace_memory_line_by_line=UpperCamelCase__    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
																		_check_summary_is_not_empty(result.inference_summary						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''log.txt'''						)						).exists()						)





