



'''simple docstring'''



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	:		List[str]				=   {
    '''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   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Optional[Any]					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]					):






	'''simple docstring'''




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






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					pass






class 					UpperCAmelCase__	(		nn.Module ):



					def __init__(    self			,				UpperCamelCase     )     ->			Tuple:
						super().__init__()

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



def   __lowerCAmelCase					(							lowerCamelCase   :						Any					):






	'''simple docstring'''




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

	return f'''./{model_name}.ckpt'''


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

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

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



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]					):






	'''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   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''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   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Optional[int]=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(lowerCamelCase					) < 7:
		__lowerCAmelCase												=  DOWN_NUM_TO_LAYER[layer_num]
		__lowerCAmelCase												=  f'''down_blocks.{depth}'''
	elif depth > 0 and int(lowerCamelCase					) > 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(lowerCamelCase					) > 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(lowerCamelCase					)
	elif "attentions" in new_layer:
		__lowerCAmelCase												=  convert_attn_naming(lowerCamelCase					)
		__lowerCAmelCase												=  new_string_left

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



def   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]					):






	'''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(lowerCamelCase					)

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

	return new_state_dict



def   __lowerCAmelCase					(							lowerCamelCase   :						Dict				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	if len(lowerCamelCase					) == 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   __lowerCAmelCase					(							lowerCamelCase   :						Dict					):






	'''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(lowerCamelCase					)

	__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=lowerCamelCase				,		sample_rate=lowerCamelCase					)
	__lowerCAmelCase												=  diffusers_model.state_dict()

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

	__lowerCAmelCase												=  set(renamed_state_dict.keys()					) - set(diffusers_state_dict.keys()					)
	__lowerCAmelCase												=  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":
			__lowerCAmelCase												=  value.squeeze()

		__lowerCAmelCase												=  value

	diffusers_model.load_state_dict(lowerCamelCase					)

	__lowerCAmelCase												=  1_00
	__lowerCAmelCase												=  33

	__lowerCAmelCase												=  IPNDMScheduler(num_train_timesteps=lowerCamelCase					)

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

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

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

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

	__lowerCAmelCase												=  sampling.iplms_sample(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		{}					)
	__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"				,		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__":
	lowerCAmelCase	:		List[str]				=   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	:		Any				=   parser.parse_args()

	main(args)




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase	:		Optional[Any]				=   {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrOCRForCausalLM''',
	    '''TrOCRPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

	lowerCAmelCase	:		Optional[Any]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)