




import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


__a  :		Any			=    '''./tests/fixtures/elise_format0.mid'''







@unittest.skip("""The note-seq package currently throws an error on import"""     )
class       UpperCAmelCase(     a_    ,	unittest.TestCase     ):



	"""simple docstring"""






	a	:					Optional[int]         =     SpectrogramDiffusionPipeline
	a	:					Tuple         =     PipelineTesterMixin.required_optional_params - {
	    'callback',
	    'latents',
	    'callback_steps',
	    'output_type',
	    'num_images_per_prompt',
	}
	a	:					Dict         =     False
	a	:					str         =     False
	a	:					Dict         =     TOKENS_TO_AUDIO_GENERATION_PARAMS
	a	:					Dict         =     TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

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





								"""simple docstring"""


								torch.manual_seed(0				)
								lowercase__  :       Dict   					=							SpectrogramNotesEncoder(
								    max_length=2048       ,      vocab_size=1536       ,      d_model=768       ,      dropout_rate=0.1       ,      num_layers=1       ,      num_heads=1       ,      d_kv=4       ,      d_ff=2048       ,      feed_forward_proj="gated-gelu"       ,      )

								lowercase__  :       Optional[Any]   					=							SpectrogramContEncoder(
								    input_dims=128       ,      targets_context_length=256       ,      d_model=768       ,      dropout_rate=0.1       ,      num_layers=1       ,      num_heads=1       ,      d_kv=4       ,      d_ff=2048       ,      feed_forward_proj="gated-gelu"       ,      )

								lowercase__  :       Any   					=							TaFilmDecoder(
								    input_dims=128       ,      targets_length=256       ,      max_decoder_noise_time=20000.0       ,      d_model=768       ,      num_layers=1       ,      num_heads=1       ,      d_kv=4       ,      d_ff=2048       ,      dropout_rate=0.1       ,      )

								lowercase__  :       Optional[Any]   					=							DDPMScheduler()

								lowercase__  :       int   					=							{
								    "notes_encoder": notes_encoder.eval(),
								    "continuous_encoder": continuous_encoder.eval(),
								    "decoder": decoder.eval(),
								    "scheduler": scheduler,
								    "melgan": None,
								}
								return components

	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase=0				)					->			Optional[Any]:





								"""simple docstring"""


								if str(snake_case__				).startswith("mps"				):
															lowercase__  :       Optional[int]   					=							torch.manual_seed(snake_case__				)
								else:
															lowercase__  :       Dict   					=							torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
								lowercase__  :       Union[str, Any]   					=							{
								    "input_tokens": [
								        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
								    ],
								    "generator": generator,
								    "num_inference_steps": 4,
								    "output_type": "mel",
								}
								return inputs

	def     __a	(     self				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       Tuple   					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
								lowercase__  :       Union[str, Any]   					=							self.get_dummy_components()
								lowercase__  :       Any   					=							SpectrogramDiffusionPipeline(**snake_case__				)
								lowercase__  :       Any   					=							pipe.to(snake_case__				)
								pipe.set_progress_bar_config(disable=snake_case__				)

								lowercase__  :       Any   					=							self.get_dummy_inputs(snake_case__				)
								lowercase__  :       Dict   					=							pipe(**snake_case__				)
								lowercase__  :       Optional[Any]   					=							output.audios

								lowercase__  :       str   					=							mel[0, -3:, -3:]

								assert mel_slice.shape == (3, 3)
								lowercase__  :       Dict   					=							np.array(
								    [-11.51_29_25, -4.78_82_15, -0.46_17_29_05, -2.05_17_15, -10.53_91_47, -10.97_09_63, -9.09_16_34, 4.0, 4.0]				)
								assert np.abs(mel_slice.flatten() - expected_slice				).max() < 1E-2

	@skip_mps
	def     __a	(     self				)					->			Dict:





								"""simple docstring"""


								return super().test_save_load_local()

	@skip_mps
	def     __a	(     self				)					->			int:





								"""simple docstring"""


								return super().test_dict_tuple_outputs_equivalent()

	@skip_mps
	def     __a	(     self				)					->			Union[str, Any]:





								"""simple docstring"""


								return super().test_save_load_optional_components()

	@skip_mps
	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


								return super().test_attention_slicing_forward_pass()

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





								"""simple docstring"""


								pass

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





								"""simple docstring"""


								pass







	@skip_mps
	def     __a	(     self				)					->			List[str]:





								"""simple docstring"""


								return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class       UpperCAmelCase(     unittest.TestCase     ):



	"""simple docstring"""






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





								"""simple docstring"""


								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

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





								"""simple docstring"""


								lowercase__  :       List[str]   					=							torch_device

								lowercase__  :       Tuple   					=							SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
								lowercase__  :       int   					=							pipe.melgan
								lowercase__  :       int   					=							None

								lowercase__  :       int   					=							pipe.to(snake_case__				)
								pipe.set_progress_bar_config(disable=snake_case__				)

								def callback(lowerCamelCase       ,      lowerCamelCase				):
															# decode mel to audio
															lowercase__  :       str   					=							melgan(input_features=mel_output.astype(np.floataa				)				)[0]
															assert len(audio[0]				) == 81920 * (step + 1)
															# simulate that audio is played
															return audio

								lowercase__  :       Union[str, Any]   					=							MidiProcessor()
								lowercase__  :       Optional[Any]   					=							processor(snake_case__				)

								lowercase__  :       Any   					=							input_tokens[:3]
								lowercase__  :       Any   					=							torch.manual_seed(0				)
								pipe(snake_case__       ,      num_inference_steps=5       ,      generator=snake_case__       ,      callback=snake_case__       ,      output_type="mel"				)

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





								"""simple docstring"""


								lowercase__  :       Dict   					=							torch_device

								lowercase__  :       Optional[Any]   					=							SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
								lowercase__  :       str   					=							pipe.to(snake_case__				)
								pipe.set_progress_bar_config(disable=snake_case__				)
								lowercase__  :       Any   					=							MidiProcessor()

								lowercase__  :       Optional[int]   					=							processor(snake_case__				)
								# just run two denoising loops
								lowercase__  :       str   					=							input_tokens[:2]

								lowercase__  :       Optional[Any]   					=							torch.manual_seed(0				)
								lowercase__  :       List[Any]   					=							pipe(snake_case__       ,      num_inference_steps=2       ,      generator=snake_case__				)

								lowercase__  :       Optional[Any]   					=							output.audios[0]

								assert abs(np.abs(snake_case__				).sum() - 3612.841				) < 1E-1







	def     __a	(     self				)					->			Dict:





								"""simple docstring"""


								lowercase__  :       Tuple   					=							torch_device

								lowercase__  :       List[str]   					=							SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
								lowercase__  :       Tuple   					=							pipe.to(snake_case__				)
								pipe.set_progress_bar_config(disable=snake_case__				)

								lowercase__  :       Union[str, Any]   					=							MidiProcessor()

								lowercase__  :       str   					=							processor(snake_case__				)

								# just run 4 denoising loops
								lowercase__  :       Union[str, Any]   					=							input_tokens[:4]

								lowercase__  :       int   					=							torch.manual_seed(0				)
								lowercase__  :       int   					=							pipe(snake_case__       ,      num_inference_steps=100       ,      generator=snake_case__				)

								lowercase__  :       Optional[Any]   					=							output.audios[0]
								assert abs(np.abs(snake_case__				).sum() - 9389.1111				) < 5E-2







"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	k_size // 2
       UpperCAmelCase			,				UpperCAmelCase					=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase					=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
       )
       return g






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     UpperCAmelCase					=	gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row, col - 1]
                            UpperCAmelCase					=	sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row + 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col]
                            UpperCAmelCase					=	sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   UpperCAmelCase					=	sobel_grad[row, col]

       return destination






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     if destination[row, col] >= threshold_high:
                            UpperCAmelCase					=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCAmelCase					=	0
                     else:
                            UpperCAmelCase					=	weak






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0]						):
              for col in range(1		,					image_shape[1]						):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   UpperCAmelCase					=	strong
                            else:
                                   UpperCAmelCase					=	0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowerCAmelCase_		:			Optional[Any]				   =					cva.imread(R'''../image_data/lena.jpg''', 0)
 # canny edge detection
 lowerCAmelCase_		:			Dict				   =					canny(lena)
 cva.imshow('''canny''', canny_destination)
 cva.waitKey(0)
