
"""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__        =		{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 131_072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	return torch.atana(_UpperCamelCase	,  _UpperCamelCase		) / math.pi * 2
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				torch.sin(t * math.pi / 2		) ** 2
	__lowerCAmelCase      :						Optional[int]						      =				(1 - sigma**2) ** 0.5
	return alpha_sigma_to_t(_UpperCamelCase	,  _UpperCamelCase		)




class 			A__	(     _lowerCamelCase):
		pass




class 			A__	(     nn.Module):

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

			__lowerCAmelCase      :						Union[str, Any]						      =				DiffusionAttnUnetaD(_SCREAMING_SNAKE_CASE	,		n_attn_layers=4			)
			__lowerCAmelCase      :						Dict						      =				deepcopy(self.diffusion			)
			__lowerCAmelCase      :						Union[str, Any]						      =				torch.quasirandom.SobolEngine(1	,		scramble=_SCREAMING_SNAKE_CASE			)



def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				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    __lowerCAmelCase    (_UpperCamelCase		):
	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    (_UpperCamelCase		):
	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    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=13		):
	__lowerCAmelCase      :						Any						      =				input_string

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

	__lowerCAmelCase      :						Tuple						      =				0
	if string.startswith('net.3.'		):
		depth += 1
		__lowerCAmelCase      :						Union[str, Any]						      =				string[6:]
	elif string.startswith('net.'		):
		__lowerCAmelCase      :						Dict						      =				string[4:]

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

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

	# mid block
	if string[:2].isdigit():
		__lowerCAmelCase      :						Union[str, Any]						      =				string[:2]
		__lowerCAmelCase      :						Union[str, Any]						      =				string[2:]
	else:
		__lowerCAmelCase      :						Dict						      =				string[0]
		__lowerCAmelCase      :						List[str]						      =				string[1:]

	if depth == max_depth:
		__lowerCAmelCase      :						int						      =				MID_NUM_TO_LAYER[layer_num]
		__lowerCAmelCase      :						List[Any]						      =				'mid_block'
	elif depth > 0 and int(_UpperCamelCase		) < 7:
		__lowerCAmelCase      :						Tuple						      =				DOWN_NUM_TO_LAYER[layer_num]
		__lowerCAmelCase      :						List[Any]						      =				F"down_blocks.{depth}"
	elif depth > 0 and int(_UpperCamelCase		) > 7:
		__lowerCAmelCase      :						str						      =				UP_NUM_TO_LAYER[layer_num]
		__lowerCAmelCase      :						Optional[int]						      =				F"up_blocks.{max_depth - depth - 1}"
	elif depth == 0:
		__lowerCAmelCase      :						List[Any]						      =				DEPTH_0_TO_LAYER[layer_num]
		__lowerCAmelCase      :						Tuple						      =				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      :						List[str]						      =				string_left[1:]

	if "resnets" in new_layer:
		__lowerCAmelCase      :						int						      =				convert_resconv_naming(_UpperCamelCase		)
	elif "attentions" in new_layer:
		__lowerCAmelCase      :						Tuple						      =				convert_attn_naming(_UpperCamelCase		)
		__lowerCAmelCase      :						Optional[int]						      =				new_string_left

	if not isinstance(_UpperCamelCase	,  _UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				prefix + '.' + new_layer + '.' + string_left
	else:
		__lowerCAmelCase      :						int						      =				[prefix + '.' + new_layer + '.' + s for s in string_left]
	return new_string
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				{}
	for k, v in state_dict.items():
		if k.endswith('kernel'		):
			# up- and downsample layers, don't have trainable weights
			continue

		__lowerCAmelCase      :						Optional[Any]						      =				rename(_UpperCamelCase		)

		# check if we need to transform from Conv => Linear for attention
		if isinstance(_UpperCamelCase	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Union[str, Any]						      =				transform_conv_attns(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		else:
			__lowerCAmelCase      :						List[Any]						      =				v

	return new_state_dict
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if len(_UpperCamelCase		) == 1:
		if len(v.shape		) == 3:
			# weight
			__lowerCAmelCase      :						List[Any]						      =				v[:, :, 0]
		else:
			# bias
			__lowerCAmelCase      :						str						      =				v
	else:
		# qkv matrices
		__lowerCAmelCase      :						Union[str, Any]						      =				v.shape[0]
		__lowerCAmelCase      :						List[Any]						      =				trippled_shape // 3
		for i in range(3		):
			if len(v.shape		) == 3:
				__lowerCAmelCase      :						Union[str, Any]						      =				v[i * single_shape : (i + 1) * single_shape, :, 0]
			else:
				__lowerCAmelCase      :						Optional[Any]						      =				v[i * single_shape : (i + 1) * single_shape]
	return new_state_dict
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[Any]						      =				torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)

	__lowerCAmelCase      :						Any						      =				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      :						int						      =				download(_UpperCamelCase		)

	__lowerCAmelCase      :						Dict						      =				MODELS_MAP[model_name]['sample_rate']
	__lowerCAmelCase      :						Tuple						      =				MODELS_MAP[model_name]['sample_size']

	__lowerCAmelCase      :						List[str]						      =				Object()
	__lowerCAmelCase      :						Optional[Any]						      =				sample_size
	__lowerCAmelCase      :						str						      =				sample_rate
	__lowerCAmelCase      :						str						      =				0

	__lowerCAmelCase      :						Optional[Any]						      =				UNetaDModel(sample_size=_UpperCamelCase	,  sample_rate=_UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				diffusers_model.state_dict()

	__lowerCAmelCase      :						str						      =				DiffusionUncond(_UpperCamelCase		)
	orig_model.load_state_dict(torch.load(args.model_path	,  map_location=_UpperCamelCase		)['state_dict']		)
	__lowerCAmelCase      :						Any						      =				orig_model.diffusion_ema.eval()
	__lowerCAmelCase      :						Dict						      =				orig_model.state_dict()
	__lowerCAmelCase      :						Optional[int]						      =				rename_orig_weights(_UpperCamelCase		)

	__lowerCAmelCase      :						str						      =				set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
	__lowerCAmelCase      :						Optional[int]						      =				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      :						Optional[int]						      =				value.squeeze()

		__lowerCAmelCase      :						Dict						      =				value

	diffusers_model.load_state_dict(_UpperCamelCase		)

	__lowerCAmelCase      :						Tuple						      =				100
	__lowerCAmelCase      :						Tuple						      =				33

	__lowerCAmelCase      :						List[str]						      =				IPNDMScheduler(num_train_timesteps=_UpperCamelCase		)

	__lowerCAmelCase      :						int						      =				torch.manual_seed(_UpperCamelCase		)
	__lowerCAmelCase      :						Optional[int]						      =				torch.randn([1, 2, config.sample_size]	,  generator=_UpperCamelCase		).to(_UpperCamelCase		)

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

	__lowerCAmelCase      :						Optional[Any]						      =				DanceDiffusionPipeline(unet=_UpperCamelCase	,  scheduler=_UpperCamelCase		)

	__lowerCAmelCase      :						Any						      =				torch.manual_seed(33		)
	__lowerCAmelCase      :						List[Any]						      =				pipe(num_inference_steps=_UpperCamelCase	,  generator=_UpperCamelCase		).audios

	__lowerCAmelCase      :						Any						      =				sampling.iplms_sample(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  {}		)
	__lowerCAmelCase      :						List[str]						      =				generated.clamp(-1	,  1		)

	__lowerCAmelCase      :						Tuple						      =				(generated - audio).abs().sum()
	__lowerCAmelCase      :						List[str]						      =				(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)

"""simple docstring"""







import argparse
import datetime
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[Any]						      =				{
	    '0': 'Sunday',
	    '1': 'Monday',
	    '2': 'Tuesday',
	    '3': 'Wednesday',
	    '4': 'Thursday',
	    '5': 'Friday',
	    '6': 'Saturday',
	}

	__lowerCAmelCase      :						Optional[Any]						      =				{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

	# Validate
	if not 0 < len(_UpperCamelCase		) < 11:
		raise ValueError('Must be 10 characters long'		)

	# Get month
	__lowerCAmelCase      :						int						      =				int(date_input[0] + date_input[1]		)
	# Validate
	if not 0 < m < 13:
		raise ValueError('Month must be between 1 - 12'		)

	__lowerCAmelCase      :						str						      =				date_input[2]
	# Validate
	if sep_a not in ["-", "/"]:
		raise ValueError('Date separator must be \'-\' or \'/\''		)

	# Get day
	__lowerCAmelCase      :						int						      =				int(date_input[3] + date_input[4]		)
	# Validate
	if not 0 < d < 32:
		raise ValueError('Date must be between 1 - 31'		)

	# Get second separator
	__lowerCAmelCase      :						str						      =				date_input[5]
	# Validate
	if sep_a not in ["-", "/"]:
		raise ValueError('Date separator must be \'-\' or \'/\''		)

	# Get year
	__lowerCAmelCase      :						int						      =				int(date_input[6] + date_input[7] + date_input[8] + date_input[9]		)
	# Arbitrary year range
	if not 45 < y < 8500:
		raise ValueError(
		    'Year out of range. There has to be some sort of limit...right?'		)

	# Get datetime obj for validation
	__lowerCAmelCase      :						Tuple						      =				datetime.date(int(_UpperCamelCase		)	,  int(_UpperCamelCase		)	,  int(_UpperCamelCase		)		)

	# Start math
	if m <= 2:
		__lowerCAmelCase      :						int						      =				y - 1
		__lowerCAmelCase      :						Tuple						      =				m + 12
	# maths var
	__lowerCAmelCase      :						int						      =				int(str(_UpperCamelCase		)[:2]		)
	__lowerCAmelCase      :						int						      =				int(str(_UpperCamelCase		)[2:]		)
	__lowerCAmelCase      :						int						      =				int(2.6 * m - 5.39		)
	__lowerCAmelCase      :						int						      =				int(c / 4		)
	__lowerCAmelCase      :						int						      =				int(k / 4		)
	__lowerCAmelCase      :						int						      =				int(d + k		)
	__lowerCAmelCase      :						int						      =				int(t + u + v + x		)
	__lowerCAmelCase      :						int						      =				int(z - (2 * c)		)
	__lowerCAmelCase      :						int						      =				round(w % 7		)
	# End math

	# Validate math
	if f != convert_datetime_days[dt_ck.weekday()]:
		raise AssertionError('The date was evaluated incorrectly. Contact developer.'		)

	# Response
	__lowerCAmelCase      :						str						      =				F"Your date {date_input}, is a {days[str(_UpperCamelCase		)]}!"
	return response


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							lowerCamelCase__        =		argparse.ArgumentParser(
							    description=(
							        """Find out what day of the week nearly any date is or was. Enter """
							        """date as a string in the mm-dd-yyyy or mm/dd/yyyy format"""
							    )
							)
							parser.add_argument(
							    """date_input""", type=str, help="""Date as a string (mm-dd-yyyy or mm/dd/yyyy)"""
							)
							lowerCamelCase__        =		parser.parse_args()
							zeller(args.date_input)