





import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf

@require_vision
@require_torchvision
class  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								lowerCamelCase			=tempfile.mkdtemp()
								lowerCamelCase			=SamImageProcessor()
								lowerCamelCase			=SamProcessor(UpperCAmelCase_							)
								processor.save_pretrained(self.tmpdirname							)


				def _snake_case    (						self				,	**UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname				,	**UpperCAmelCase_							).image_processor


				def _snake_case    (						self							):
								shutil.rmtree(self.tmpdirname							)


				def _snake_case    (						self							):
								lowerCamelCase			=[np.random.randint(255				,	size=(3, 30, 400)				,	dtype=np.uinta							)]

								lowerCamelCase			=[Image.fromarray(np.moveaxis(UpperCAmelCase_				,	0				,	-1							)							) for x in image_inputs]

								return image_inputs


				def _snake_case    (						self							):
								lowerCamelCase			=SamProcessor(image_processor=self.get_image_processor()							)
								processor.save_pretrained(self.tmpdirname							)

								lowerCamelCase			=self.get_image_processor(do_normalize=UpperCAmelCase_				,	padding_value=1.0							)

								lowerCamelCase			=SamProcessor.from_pretrained(self.tmpdirname				,	do_normalize=UpperCAmelCase_				,	padding_value=1.0							)

								self.assertEqual(processor.image_processor.to_json_string()				,	image_processor_add_kwargs.to_json_string()							)
								self.assertIsInstance(processor.image_processor				,	UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.get_image_processor()

								lowerCamelCase			=SamProcessor(image_processor=UpperCAmelCase_							)

								lowerCamelCase			=self.prepare_image_inputs()

								lowerCamelCase			=image_processor(UpperCAmelCase_				,	return_tensors="""np"""							)
								lowerCamelCase			=processor(images=UpperCAmelCase_				,	return_tensors="""np"""							)

								input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
								input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop original_sizes as it is popped in the processor

								for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()				,	input_processor[key].sum()				,	delta=1E-2							)


				@require_torch
				def _snake_case    (						self							):
								lowerCamelCase			=self.get_image_processor()

								lowerCamelCase			=SamProcessor(image_processor=UpperCAmelCase_							)
								lowerCamelCase			=[torch.ones((1, 3, 5, 5)							)]

								lowerCamelCase			=[[1764, 2646]]

								lowerCamelCase			=[[683, 1024]]
								lowerCamelCase			=processor.post_process_masks(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)
								self.assertEqual(masks[0].shape				,	(1, 3, 1764, 2646)							)

								lowerCamelCase			=processor.post_process_masks(
								    UpperCAmelCase_				,	torch.tensor(UpperCAmelCase_							)				,	torch.tensor(UpperCAmelCase_							)							)
								self.assertEqual(masks[0].shape				,	(1, 3, 1764, 2646)							)

								# should also work with np
								lowerCamelCase			=[np.ones((1, 3, 5, 5)							)]
								lowerCamelCase			=processor.post_process_masks(UpperCAmelCase_				,	np.array(UpperCAmelCase_							)				,	np.array(UpperCAmelCase_							)							)

								self.assertEqual(masks[0].shape				,	(1, 3, 1764, 2646)							)

								lowerCamelCase			=[[1, 0], [0, 1]]
								with self.assertRaises(UpperCAmelCase_							):
												lowerCamelCase			=processor.post_process_masks(UpperCAmelCase_				,	np.array(UpperCAmelCase_							)				,	np.array(UpperCAmelCase_							)							)




@require_vision
@require_tf
class  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								lowerCamelCase			=tempfile.mkdtemp()
								lowerCamelCase			=SamImageProcessor()
								lowerCamelCase			=SamProcessor(UpperCAmelCase_							)
								processor.save_pretrained(self.tmpdirname							)


				def _snake_case    (						self				,	**UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname				,	**UpperCAmelCase_							).image_processor


				def _snake_case    (						self							):
								shutil.rmtree(self.tmpdirname							)


				def _snake_case    (						self							):
								lowerCamelCase			=[np.random.randint(255				,	size=(3, 30, 400)				,	dtype=np.uinta							)]

								lowerCamelCase			=[Image.fromarray(np.moveaxis(UpperCAmelCase_				,	0				,	-1							)							) for x in image_inputs]

								return image_inputs


				def _snake_case    (						self							):
								lowerCamelCase			=SamProcessor(image_processor=self.get_image_processor()							)
								processor.save_pretrained(self.tmpdirname							)

								lowerCamelCase			=self.get_image_processor(do_normalize=UpperCAmelCase_				,	padding_value=1.0							)

								lowerCamelCase			=SamProcessor.from_pretrained(self.tmpdirname				,	do_normalize=UpperCAmelCase_				,	padding_value=1.0							)

								self.assertEqual(processor.image_processor.to_json_string()				,	image_processor_add_kwargs.to_json_string()							)
								self.assertIsInstance(processor.image_processor				,	UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.get_image_processor()

								lowerCamelCase			=SamProcessor(image_processor=UpperCAmelCase_							)

								lowerCamelCase			=self.prepare_image_inputs()

								lowerCamelCase			=image_processor(UpperCAmelCase_				,	return_tensors="""np"""							)
								lowerCamelCase			=processor(images=UpperCAmelCase_				,	return_tensors="""np"""							)

								input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
								input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop reshaped_input_sizes as it is popped in the processor

								for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()				,	input_processor[key].sum()				,	delta=1E-2							)


				@require_tf
				def _snake_case    (						self							):
								lowerCamelCase			=self.get_image_processor()

								lowerCamelCase			=SamProcessor(image_processor=UpperCAmelCase_							)
								lowerCamelCase			=[tf.ones((1, 3, 5, 5)							)]

								lowerCamelCase			=[[1764, 2646]]

								lowerCamelCase			=[[683, 1024]]
								lowerCamelCase			=processor.post_process_masks(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	return_tensors="""tf"""							)
								self.assertEqual(masks[0].shape				,	(1, 3, 1764, 2646)							)

								lowerCamelCase			=processor.post_process_masks(
								    UpperCAmelCase_				,	tf.convert_to_tensor(UpperCAmelCase_							)				,	tf.convert_to_tensor(UpperCAmelCase_							)				,	return_tensors="""tf"""				,	)
								self.assertEqual(masks[0].shape				,	(1, 3, 1764, 2646)							)

								# should also work with np
								lowerCamelCase			=[np.ones((1, 3, 5, 5)							)]
								lowerCamelCase			=processor.post_process_masks(
								    UpperCAmelCase_				,	np.array(UpperCAmelCase_							)				,	np.array(UpperCAmelCase_							)				,	return_tensors="""tf"""							)

								self.assertEqual(masks[0].shape				,	(1, 3, 1764, 2646)							)

								lowerCamelCase			=[[1, 0], [0, 1]]
								with self.assertRaises(tf.errors.InvalidArgumentError							):
												lowerCamelCase			=processor.post_process_masks(
												    UpperCAmelCase_				,	np.array(UpperCAmelCase_							)				,	np.array(UpperCAmelCase_							)				,	return_tensors="""tf"""							)




@require_vision
@require_torchvision
class  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								lowerCamelCase			=tempfile.mkdtemp()
								lowerCamelCase			=SamImageProcessor()
								lowerCamelCase			=SamProcessor(UpperCAmelCase_							)
								processor.save_pretrained(self.tmpdirname							)


				def _snake_case    (						self				,	**UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname				,	**UpperCAmelCase_							).image_processor


				def _snake_case    (						self							):
								shutil.rmtree(self.tmpdirname							)


				def _snake_case    (						self							):
								lowerCamelCase			=[np.random.randint(255				,	size=(3, 30, 400)				,	dtype=np.uinta							)]

								lowerCamelCase			=[Image.fromarray(np.moveaxis(UpperCAmelCase_				,	0				,	-1							)							) for x in image_inputs]

								return image_inputs


				@is_pt_tf_cross_test
				def _snake_case    (						self							):
								lowerCamelCase			=self.get_image_processor()

								lowerCamelCase			=SamProcessor(image_processor=UpperCAmelCase_							)
								lowerCamelCase			=np.random.randint(0				,	2				,	size=(1, 3, 5, 5)							).astype(np.floataa							)
								lowerCamelCase			=[tf.convert_to_tensor(UpperCAmelCase_							)]
								lowerCamelCase			=[torch.tensor(UpperCAmelCase_							)]

								lowerCamelCase			=[[1764, 2646]]

								lowerCamelCase			=[[683, 1024]]
								lowerCamelCase			=processor.post_process_masks(
								    UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	return_tensors="""tf"""							)
								lowerCamelCase			=processor.post_process_masks(
								    UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	return_tensors="""pt"""							)

								self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()							)							)


				@is_pt_tf_cross_test
				def _snake_case    (						self							):
								lowerCamelCase			=self.get_image_processor()

								lowerCamelCase			=SamProcessor(image_processor=UpperCAmelCase_							)

								lowerCamelCase			=self.prepare_image_inputs()

								lowerCamelCase			=image_processor(UpperCAmelCase_				,	return_tensors="""pt"""							)["""pixel_values"""].numpy()
								lowerCamelCase			=processor(images=UpperCAmelCase_				,	return_tensors="""pt"""							)["""pixel_values"""].numpy()

								lowerCamelCase			=image_processor(UpperCAmelCase_				,	return_tensors="""tf"""							)["""pixel_values"""].numpy()
								lowerCamelCase			=processor(images=UpperCAmelCase_				,	return_tensors="""tf"""							)["""pixel_values"""].numpy()

								self.assertTrue(np.allclose(UpperCAmelCase_				,	UpperCAmelCase_							)							)
								self.assertTrue(np.allclose(UpperCAmelCase_				,	UpperCAmelCase_							)							)
								self.assertTrue(np.allclose(UpperCAmelCase_				,	UpperCAmelCase_							)							)












from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=(1 - _cos) / 2
				lowerCamelCase			=1 - _cos

				lowerCamelCase			=1 + alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=(1 + _cos) / 2
				lowerCamelCase			=-1 - _cos

				lowerCamelCase			=1 + alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=_sin / 2
				lowerCamelCase			=0
				lowerCamelCase			=-ba

				lowerCamelCase			=1 + alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=1 - alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 + alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([ba, ba, ba]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)     , )  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)
				lowerCamelCase			=10 ** (gain_db / 40)

				lowerCamelCase			=1 + alpha * big_a
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha * big_a
				lowerCamelCase			=1 + alpha / big_a
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha / big_a

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)     , )  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)
				lowerCamelCase			=10 ** (gain_db / 40)
				lowerCamelCase			=(big_a + 1) - (big_a - 1) * _cos
				lowerCamelCase			=(big_a + 1) + (big_a - 1) * _cos
				lowerCamelCase			=(big_a - 1) - (big_a + 1) * _cos
				lowerCamelCase			=(big_a - 1) + (big_a + 1) * _cos
				lowerCamelCase			=2 * sqrt(_UpperCAmelCase			) * alpha

				lowerCamelCase			=big_a * (pmc + aaa)
				lowerCamelCase			=2 * big_a * mpc
				lowerCamelCase			=big_a * (pmc - aaa)
				lowerCamelCase			=ppmc + aaa
				lowerCamelCase			=-2 * pmpc
				lowerCamelCase			=ppmc - aaa

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)     , )  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)
				lowerCamelCase			=10 ** (gain_db / 40)
				lowerCamelCase			=(big_a + 1) - (big_a - 1) * _cos
				lowerCamelCase			=(big_a + 1) + (big_a - 1) * _cos
				lowerCamelCase			=(big_a - 1) - (big_a + 1) * _cos
				lowerCamelCase			=(big_a - 1) + (big_a + 1) * _cos
				lowerCamelCase			=2 * sqrt(_UpperCAmelCase			) * alpha

				lowerCamelCase			=big_a * (ppmc + aaa)
				lowerCamelCase			=-2 * big_a * pmpc
				lowerCamelCase			=big_a * (ppmc - aaa)
				lowerCamelCase			=pmc + aaa
				lowerCamelCase			=2 * mpc
				lowerCamelCase			=pmc - aaa

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





