






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


A__	:			str			   = {
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 13_10_72,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
}






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				return torch.atana(lowerCamelCase_		,   lowerCamelCase_				) / math.pi * 2






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			torch.sin(t * math.pi / 2				) ** 2
				lowercase__    =			(1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(lowerCamelCase_		,   lowerCamelCase_				)
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				pass



class       _UpperCAmelCase					(       nn.Module	):

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								super().__init__()

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






def   a			(						lowerCamelCase_				):






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

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


A__	:			Any			   = {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
A__	:			Any			   = {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
A__	:			Optional[Any]			   = {
    '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',
}
A__	:			List[Any]			   = {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

A__	:			List[str]			   = {
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

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






def   a			(						lowerCamelCase_				):






				'''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   a			(						lowerCamelCase_				):






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






def   a			(						lowerCamelCase_		,   lowerCamelCase_=13				):






				'''simple docstring'''
				lowercase__    =			input_string

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

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

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

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

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

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

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

				lowercase__    =			string_left[1:]

				if "resnets" in new_layer:
								lowercase__    =			convert_resconv_naming(lowerCamelCase_				)
				elif "attentions" in new_layer:
								lowercase__    =			convert_attn_naming(lowerCamelCase_				)
								lowercase__    =			new_string_left

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






def   a			(						lowerCamelCase_				):






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

								lowercase__    =			rename(lowerCamelCase_				)

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

				return new_state_dict






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






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






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)

				lowercase__    =			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()}"""
								lowercase__    =			download(lowerCamelCase_				)

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

				lowercase__    =			Object()
				lowercase__    =			sample_size
				lowercase__    =			sample_rate
				lowercase__    =			0

				lowercase__    =			UNetaDModel(sample_size=lowerCamelCase_		,   sample_rate=lowerCamelCase_				)
				lowercase__    =			diffusers_model.state_dict()

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

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

				assert len(lowerCamelCase_				) == 0, F"""Problem with {renamed_minus_diffusers}"""
				assert all(k.endswith('''kernel'''				) for k in list(lowerCamelCase_				)				), 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":
												lowercase__    =			value.squeeze()

								lowercase__    =			value

				diffusers_model.load_state_dict(lowerCamelCase_				)

				lowercase__    =			100
				lowercase__    =			33

				lowercase__    =			IPNDMScheduler(num_train_timesteps=lowerCamelCase_				)

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

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

				lowercase__    =			DanceDiffusionPipeline(unet=lowerCamelCase_		,   scheduler=lowerCamelCase_				)

				lowercase__    =			torch.manual_seed(33				)
				lowercase__    =			pipe(num_inference_steps=lowerCamelCase_		,   generator=lowerCamelCase_				).audios

				lowercase__    =			sampling.iplms_sample(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   {}				)
				lowercase__    =			generated.clamp(-1		,   1				)

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

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

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

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

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


if __name__ == "__main__":
							A__	:			Any			   = 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.')
							A__	:			int			   = parser.parse_args()

							main(args)












import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       CTRLTokenizer
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__    =			['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
								lowercase__    =			dict(zip(lowerCamelCase,  range(len(lowerCamelCase  )  )  )  )
								lowercase__    =			['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
								lowercase__    =			{'''unk_token''': '''<unk>'''}

								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']  )
								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write(json.dumps(lowerCamelCase  ) + '''\n'''  )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write('''\n'''.join(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    Union[str, Any],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map  )
								return CTRLTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt react readapt apt'''
								return input_text, output_text




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			CTRLTokenizer(self.vocab_file,  self.merges_file,  **self.special_tokens_map  )
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
								lowercase__    =			tokenizer.tokenize(lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			tokens + [tokenizer.unk_token]

								lowercase__    =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),  lowerCamelCase  )




