
"""simple docstring"""




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   SMALL_MODEL_IDENTIFIER
											SCREAMING_SNAKE_CASE__						:       str           =   """pt"""
											SCREAMING_SNAKE_CASE__						:       Dict           =   """tf"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   AutoModel.from_pretrained(self.test_model    )
											model_pt.save_pretrained(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   TFAutoModel.from_pretrained(self.test_model   ,       from_pt=SCREAMING_SNAKE_CASE__    )
											model_tf.save_pretrained(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   """mock_framework"""

											# Framework provided - return whatever the user provides
											SCREAMING_SNAKE_CASE__						:       Tuple           =   FeaturesManager.determine_framework(self.test_model   ,       SCREAMING_SNAKE_CASE__    )
											self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Local checkpoint and framework provided - return provided framework
											# PyTorch checkpoint
											with TemporaryDirectory() as local_pt_ckpt:
																		self._setup_pt_ckpt(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# TensorFlow checkpoint
											with TemporaryDirectory() as local_tf_ckpt:
																		self._setup_tf_ckpt(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											with TemporaryDirectory() as local_pt_ckpt:
																		self._setup_pt_ckpt(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       self.framework_pt    )

											# TensorFlow checkpoint
											with TemporaryDirectory() as local_tf_ckpt:
																		self._setup_tf_ckpt(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       self.framework_tf    )

											# Invalid local checkpoint
											with TemporaryDirectory() as local_invalid_ckpt:
																		with self.assertRaises(SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Dict           =   FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   MagicMock(return_value=SCREAMING_SNAKE_CASE__    )
											with patch("""transformers.onnx.features.is_tf_available"""   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Dict           =   FeaturesManager.determine_framework(self.test_model    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       self.framework_pt    )

											# PyTorch not in environment -> use TensorFlow
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   MagicMock(return_value=SCREAMING_SNAKE_CASE__    )
											with patch("""transformers.onnx.features.is_torch_available"""   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Dict           =   FeaturesManager.determine_framework(self.test_model    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       self.framework_tf    )

											# Both in environment -> use PyTorch
											SCREAMING_SNAKE_CASE__						:       List[str]           =   MagicMock(return_value=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   MagicMock(return_value=SCREAMING_SNAKE_CASE__    )
											with patch("""transformers.onnx.features.is_tf_available"""   ,       SCREAMING_SNAKE_CASE__    ), patch(
											    """transformers.onnx.features.is_torch_available"""   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FeaturesManager.determine_framework(self.test_model    )
																		self.assertEqual(SCREAMING_SNAKE_CASE__   ,       self.framework_pt    )

											# Both not in environment -> raise error
											SCREAMING_SNAKE_CASE__						:       Any           =   MagicMock(return_value=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   MagicMock(return_value=SCREAMING_SNAKE_CASE__    )
											with patch("""transformers.onnx.features.is_tf_available"""   ,       SCREAMING_SNAKE_CASE__    ), patch(
											    """transformers.onnx.features.is_torch_available"""   ,       SCREAMING_SNAKE_CASE__    ):
																		with self.assertRaises(SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Any           =   FeaturesManager.determine_framework(self.test_model    )









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return 0.0






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]  )]  )
							_lowerCAmelCase     :							Optional[int]    =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]  )]  )
							return lowest, highest






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    512
							_lowerCAmelCase     :							Union[str, Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							Optional[Any]    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							int    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							str    =    np.abs(np.fft.fft(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Union[str, Any]    =    20 * np.logaa(_lowerCamelCase  )

							# 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
							_lowerCAmelCase     :							List[Any]    =    get_bounds(_lowerCamelCase				, _lowerCamelCase  )
							plt.ylim(max([-80, bounds[0]]  )				, min([80, bounds[1]]  )  )
							plt.ylabel("Gain (dB)"  )

							plt.plot(_lowerCamelCase  )
							plt.show()






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    512
							_lowerCAmelCase     :							Optional[Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							str    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							Optional[Any]    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							Optional[Any]    =    np.angle(np.fft.fft(_lowerCamelCase  )  )

							# 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(_lowerCamelCase				, -2 * pi  )  )
							plt.show()
