

import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


lowerCamelCase      				=     {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 1_3_1_0_7_2,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
}


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return torch.atana(UpperCAmelCase__		,					UpperCAmelCase__      ) / math.pi * 2


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							torch.sin(t * math.pi / 2      ) ** 2
					__lowerCAmelCase					=							(1 - sigma**2) ** 0.5
					return alpha_sigma_to_t(UpperCAmelCase__		,					UpperCAmelCase__      )





class     snake_case_			(				_a							):


							"""simple docstring"""





							pass





class     snake_case_			(				nn.Module							):


							"""simple docstring"""





							def __init__( self					,						_A							):
												super().__init__()

												__lowerCAmelCase					=							DiffusionAttnUnetaD(_A					,						n_attn_layers=4							)
												__lowerCAmelCase					=							deepcopy(self.diffusion							)
												__lowerCAmelCase					=							torch.quasirandom.SobolEngine(1					,						scramble=_A							)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							MODELS_MAP[model_name]['url']
					os.system(F"""wget {url} ./"""      )

					return F"""./{model_name}.ckpt"""


lowerCamelCase      				=     {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
lowerCamelCase      				=     {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
lowerCamelCase      				=     {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
lowerCamelCase      				=     {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

lowerCamelCase      				=     {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

lowerCamelCase      				=     {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if name.startswith('skip'      ):
										return name.replace('skip'		,					RES_CONV_MAP['skip']      )

					# name has to be of format main.{digit}
					if not name.startswith('main.'      ):
										raise ValueError(F"""ResConvBlock error with {name}"""      )

					return name.replace(name[:6]		,					RES_CONV_MAP[name[:6]]      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					for key, value in ATTN_MAP.items():
										if name.startswith(UpperCAmelCase__      ) and not isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
															return name.replace(UpperCAmelCase__		,					UpperCAmelCase__      )
										elif name.startswith(UpperCAmelCase__      ):
															return [name.replace(UpperCAmelCase__		,					UpperCAmelCase__      ) for v in value]
					raise ValueError(F"""Attn error with {name}"""      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=13      ):

					"""simple docstring"""






					__lowerCAmelCase					=							input_string

					if string.split('.'      )[0] == "timestep_embed":
										return string.replace('timestep_embed'		,					'time_proj'      )

					__lowerCAmelCase					=							0
					if string.startswith('net.3.'      ):
										depth += 1
										__lowerCAmelCase					=							string[6:]
					elif string.startswith('net.'      ):
										__lowerCAmelCase					=							string[4:]

					while string.startswith('main.7.'      ):
										depth += 1
										__lowerCAmelCase					=							string[7:]

					if string.startswith('main.'      ):
										__lowerCAmelCase					=							string[5:]

					# mid block
					if string[:2].isdigit():
										__lowerCAmelCase					=							string[:2]
										__lowerCAmelCase					=							string[2:]
					else:
										__lowerCAmelCase					=							string[0]
										__lowerCAmelCase					=							string[1:]

					if depth == max_depth:
										__lowerCAmelCase					=							MID_NUM_TO_LAYER[layer_num]
										__lowerCAmelCase					=							'mid_block'
					elif depth > 0 and int(UpperCAmelCase__      ) < 7:
										__lowerCAmelCase					=							DOWN_NUM_TO_LAYER[layer_num]
										__lowerCAmelCase					=							F"""down_blocks.{depth}"""
					elif depth > 0 and int(UpperCAmelCase__      ) > 7:
										__lowerCAmelCase					=							UP_NUM_TO_LAYER[layer_num]
										__lowerCAmelCase					=							F"""up_blocks.{max_depth - depth - 1}"""
					elif depth == 0:
										__lowerCAmelCase					=							DEPTH_0_TO_LAYER[layer_num]
										__lowerCAmelCase					=							F"""up_blocks.{max_depth - 1}""" if int(UpperCAmelCase__      ) > 3 else 'down_blocks.0'

					if not string_left.startswith('.'      ):
										raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""      )

					__lowerCAmelCase					=							string_left[1:]

					if "resnets" in new_layer:
										__lowerCAmelCase					=							convert_resconv_naming(UpperCAmelCase__      )
					elif "attentions" in new_layer:
										__lowerCAmelCase					=							convert_attn_naming(UpperCAmelCase__      )
										__lowerCAmelCase					=							new_string_left

					if not isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
										__lowerCAmelCase					=							prefix + '.' + new_layer + '.' + string_left
					else:
										__lowerCAmelCase					=							[prefix + '.' + new_layer + '.' + s for s in string_left]
					return new_string


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{}
					for k, v in state_dict.items():
										if k.endswith('kernel'      ):
															# up- and downsample layers, don't have trainable weights
															continue

										__lowerCAmelCase					=							rename(UpperCAmelCase__      )

										# check if we need to transform from Conv => Linear for attention
										if isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
															__lowerCAmelCase					=							transform_conv_attns(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )
										else:
															__lowerCAmelCase					=							v

					return new_state_dict


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if len(UpperCAmelCase__      ) == 1:
										if len(v.shape      ) == 3:
															# weight
															__lowerCAmelCase					=							v[:, :, 0]
										else:
															# bias
															__lowerCAmelCase					=							v
					else:
										# qkv matrices
										__lowerCAmelCase					=							v.shape[0]
										__lowerCAmelCase					=							trippled_shape // 3
										for i in range(3      ):
															if len(v.shape      ) == 3:
																				__lowerCAmelCase					=							v[i * single_shape : (i + 1) * single_shape, :, 0]
															else:
																				__lowerCAmelCase					=							v[i * single_shape : (i + 1) * single_shape]
					return new_state_dict


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

					__lowerCAmelCase					=							args.model_path.split('/'      )[-1].split('.'      )[0]
					if not os.path.isfile(args.model_path      ):
										assert (
										    model_name == args.model_path
										), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
										__lowerCAmelCase					=							download(UpperCAmelCase__      )

					__lowerCAmelCase					=							MODELS_MAP[model_name]['sample_rate']
					__lowerCAmelCase					=							MODELS_MAP[model_name]['sample_size']

					__lowerCAmelCase					=							Object()
					__lowerCAmelCase					=							sample_size
					__lowerCAmelCase					=							sample_rate
					__lowerCAmelCase					=							0

					__lowerCAmelCase					=							UNetaDModel(sample_size=UpperCAmelCase__		,					sample_rate=UpperCAmelCase__      )
					__lowerCAmelCase					=							diffusers_model.state_dict()

					__lowerCAmelCase					=							DiffusionUncond(UpperCAmelCase__      )
					orig_model.load_state_dict(torch.load(args.model_path		,					map_location=UpperCAmelCase__      )['state_dict']      )
					__lowerCAmelCase					=							orig_model.diffusion_ema.eval()
					__lowerCAmelCase					=							orig_model.state_dict()
					__lowerCAmelCase					=							rename_orig_weights(UpperCAmelCase__      )

					__lowerCAmelCase					=							set(renamed_state_dict.keys()      ) - set(diffusers_state_dict.keys()      )
					__lowerCAmelCase					=							set(diffusers_state_dict.keys()      ) - set(renamed_state_dict.keys()      )

					assert len(UpperCAmelCase__      ) == 0, F"""Problem with {renamed_minus_diffusers}"""
					assert all(k.endswith('kernel'      ) for k in list(UpperCAmelCase__      )      ), F"""Problem with {diffusers_minus_renamed}"""

					for key, value in renamed_state_dict.items():
										assert (
										    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
										), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
										if key == "time_proj.weight":
															__lowerCAmelCase					=							value.squeeze()

										__lowerCAmelCase					=							value

					diffusers_model.load_state_dict(UpperCAmelCase__      )

					__lowerCAmelCase					=							100
					__lowerCAmelCase					=							33

					__lowerCAmelCase					=							IPNDMScheduler(num_train_timesteps=UpperCAmelCase__      )

					__lowerCAmelCase					=							torch.manual_seed(UpperCAmelCase__      )
					__lowerCAmelCase					=							torch.randn([1, 2, config.sample_size]		,					generator=UpperCAmelCase__      ).to(UpperCAmelCase__      )

					__lowerCAmelCase					=							torch.linspace(1		,					0		,					steps + 1		,					device=UpperCAmelCase__      )[:-1]
					__lowerCAmelCase					=							get_crash_schedule(UpperCAmelCase__      )

					__lowerCAmelCase					=							DanceDiffusionPipeline(unet=UpperCAmelCase__		,					scheduler=UpperCAmelCase__      )

					__lowerCAmelCase					=							torch.manual_seed(33      )
					__lowerCAmelCase					=							pipe(num_inference_steps=UpperCAmelCase__		,					generator=UpperCAmelCase__      ).audios

					__lowerCAmelCase					=							sampling.iplms_sample(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					{}      )
					__lowerCAmelCase					=							generated.clamp(-1		,					1      )

					__lowerCAmelCase					=							(generated - audio).abs().sum()
					__lowerCAmelCase					=							(generated - audio).abs().max()

					if args.save:
										pipe.save_pretrained(args.checkpoint_path      )

					print('Diff sum'		,					UpperCAmelCase__      )
					print('Diff max'		,					UpperCAmelCase__      )

					assert diff_max < 1E-3, F"""Diff max: {diff_max} is too much :-/"""

					print(F"""Conversion for {model_name} successful!"""      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()

		parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
		parser.add_argument(
		    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
		)
		parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		lowerCamelCase      				=     parser.parse_args()

		main(args)









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3					,						_A=2_2_4					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( self							):
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}





@require_torch
@require_vision
class     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =ViTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							EfficientFormerImageProcessorTester(self							)




							@property
							def        A__  ( self							):
												return self.image_proc_tester.prepare_image_processor_dict()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												self.assertTrue(hasattr(_A					,						'image_mean'							)							)
												self.assertTrue(hasattr(_A					,						'image_std'							)							)
												self.assertTrue(hasattr(_A					,						'do_normalize'							)							)
												self.assertTrue(hasattr(_A					,						'do_resize'							)							)
												self.assertTrue(hasattr(_A					,						'size'							)							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						Image.Image							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						numpify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						np.ndarray							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						torchify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						torch.Tensor							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)






