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


__UpperCamelCase		:       Optional[int]									=     {
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 13_1072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
}
def       A	(    _lowercase		,  _lowercase						):
						return torch.atana(_lowercase		,  _lowercase						) / math.pi * 2
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							torch.sin(t * math.pi / 2						) ** 2
						SCREAMING_SNAKE_CASE							:    List[str]							=							(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(_lowercase		,  _lowercase						)





class 					lowercase__						(   UpperCamelCase_):
						pass






class 					lowercase__						(   nn.Module):


						def __init__(       self	:				Tuple    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												super().__init__()

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							DiffusionAttnUnetaD(UpperCamelCase__    ,		n_attn_layers=4						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							deepcopy(self.diffusion						)
												SCREAMING_SNAKE_CASE							:    str							=							torch.quasirandom.SobolEngine(1    ,		scramble=UpperCamelCase__						)






def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							MODELS_MAP[model_name]['''url''']
						os.system(f"""wget {url} ./"""						)

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


__UpperCamelCase		:       Tuple									=     {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__UpperCamelCase		:       Union[str, Any]									=     {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__UpperCamelCase		:       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',
}
__UpperCamelCase		:       str									=     {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

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

__UpperCamelCase		:       str									=     {
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}
def       A	(    _lowercase						):
						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	(    _lowercase						):
						for key, value in ATTN_MAP.items():
												if name.startswith(_lowercase						) and not isinstance(_lowercase		,  _lowercase						):
																		return name.replace(_lowercase		,  _lowercase						)
												elif name.startswith(_lowercase						):
																		return [name.replace(_lowercase		,  _lowercase						) for v in value]
						raise ValueError(f"""Attn error with {name}"""						)
def       A	(    _lowercase		,  _lowercase=13						):
						SCREAMING_SNAKE_CASE							:    Dict							=							input_string

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

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

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

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

						# mid block
						if string[:2].isdigit():
												SCREAMING_SNAKE_CASE							:    str							=							string[:2]
												SCREAMING_SNAKE_CASE							:    Dict							=							string[2:]
						else:
												SCREAMING_SNAKE_CASE							:    int							=							string[0]
												SCREAMING_SNAKE_CASE							:    str							=							string[1:]

						if depth == max_depth:
												SCREAMING_SNAKE_CASE							:    List[str]							=							MID_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE							:    int							=							'''mid_block'''
						elif depth > 0 and int(_lowercase						) < 7:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							DOWN_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							f"""down_blocks.{depth}"""
						elif depth > 0 and int(_lowercase						) > 7:
												SCREAMING_SNAKE_CASE							:    Tuple							=							UP_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE							:    str							=							f"""up_blocks.{max_depth - depth - 1}"""
						elif depth == 0:
												SCREAMING_SNAKE_CASE							:    List[str]							=							DEPTH_0_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							f"""up_blocks.{max_depth - 1}""" if int(_lowercase						) > 3 else '''down_blocks.0'''

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

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							string_left[1:]

						if "resnets" in new_layer:
												SCREAMING_SNAKE_CASE							:    int							=							convert_resconv_naming(_lowercase						)
						elif "attentions" in new_layer:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							convert_attn_naming(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							new_string_left

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

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							rename(_lowercase						)

												# check if we need to transform from Conv => Linear for attention
												if isinstance(_lowercase		,  _lowercase						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							transform_conv_attns(_lowercase		,  _lowercase		,  _lowercase						)
												else:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							v

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

						SCREAMING_SNAKE_CASE							:    int							=							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()}"""
												SCREAMING_SNAKE_CASE							:    List[str]							=							download(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							MODELS_MAP[model_name]['''sample_rate''']
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							MODELS_MAP[model_name]['''sample_size''']

						SCREAMING_SNAKE_CASE							:    str							=							Object()
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							sample_size
						SCREAMING_SNAKE_CASE							:    int							=							sample_rate
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							0

						SCREAMING_SNAKE_CASE							:    Any							=							UNetaDModel(sample_size=_lowercase		,  sample_rate=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							diffusers_model.state_dict()

						SCREAMING_SNAKE_CASE							:    Dict							=							DiffusionUncond(_lowercase						)
						orig_model.load_state_dict(torch.load(args.model_path		,  map_location=_lowercase						)['''state_dict''']						)
						SCREAMING_SNAKE_CASE							:    str							=							orig_model.diffusion_ema.eval()
						SCREAMING_SNAKE_CASE							:    Dict							=							orig_model.state_dict()
						SCREAMING_SNAKE_CASE							:    List[Any]							=							rename_orig_weights(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							set(diffusers_state_dict.keys()						) - set(renamed_state_dict.keys()						)

						assert len(_lowercase						) == 0, f"""Problem with {renamed_minus_diffusers}"""
						assert all(k.endswith('''kernel'''						) for k in list(_lowercase						)						), 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":
																		SCREAMING_SNAKE_CASE							:    List[str]							=							value.squeeze()

												SCREAMING_SNAKE_CASE							:    int							=							value

						diffusers_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Dict							=							100
						SCREAMING_SNAKE_CASE							:    List[Any]							=							33

						SCREAMING_SNAKE_CASE							:    List[str]							=							IPNDMScheduler(num_train_timesteps=_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.manual_seed(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.randn([1, 2, config.sample_size]		,  generator=_lowercase						).to(_lowercase						)

						SCREAMING_SNAKE_CASE							:    str							=							torch.linspace(1		,  0		,  steps + 1		,  device=_lowercase						)[:-1]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							get_crash_schedule(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							DanceDiffusionPipeline(unet=_lowercase		,  scheduler=_lowercase						)

						SCREAMING_SNAKE_CASE							:    int							=							torch.manual_seed(33						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							pipe(num_inference_steps=_lowercase		,  generator=_lowercase						).audios

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

						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(generated - audio).abs().sum()
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
					__UpperCamelCase		:       Dict									=     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.')
					__UpperCamelCase		:       Any									=     parser.parse_args()

					main(args)






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''		,  [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42		,  dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]		,  )
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							split_dict._to_yaml_list()
						assert len(_lowercase						) == len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							SplitDict._from_yaml_list(_lowercase						)
						for split_name, split_info in split_dict.items():
												# dataset_name field is deprecated, and is therefore not part of the YAML dump
												SCREAMING_SNAKE_CASE							:    Any							=							None
												# the split name of split_dict takes over the name of the split info object
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							split_name
						assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info'''		,  [SplitInfo(), SplitInfo(dataset_name=_lowercase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def       A	(    _lowercase						):
						# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
						# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
						SCREAMING_SNAKE_CASE							:    List[Any]							=							asdict(SplitDict({'''train''': split_info}						)						)
						assert "dataset_name" in split_dict_asdict["train"]
						assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





