






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      :       Optional[int]				  =   {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48000,
        "sample_size": 131072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
}



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple    )						->      Any:





    """simple docstring"""

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


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     pass


class       __lowercase      (	nn.Module  ):







     '''simple docstring'''




     def __init__(				self       :			Union[str, Any] ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__()

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      List[str]:





    """simple docstring"""

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

    return f"./{model_name}.ckpt"


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

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

__a      :       int				  =   {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Tuple:





    """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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      str:





    """simple docstring"""

    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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			List[str]=1_3    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	input_string

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

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

    while string.startswith("""main.7."""    ):
        depth += 1
        __A         =	string[7:]

    if string.startswith("""main."""    ):
        __A         =	string[5:]

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

    if depth == max_depth:
        __A         =	MID_NUM_TO_LAYER[layer_num]
        __A         =	"""mid_block"""
    elif depth > 0 and int(__lowercase    ) < 7:
        __A         =	DOWN_NUM_TO_LAYER[layer_num]
        __A         =	f"down_blocks.{depth}"
    elif depth > 0 and int(__lowercase    ) > 7:
        __A         =	UP_NUM_TO_LAYER[layer_num]
        __A         =	f"up_blocks.{max_depth - depth - 1}"
    elif depth == 0:
        __A         =	DEPTH_0_TO_LAYER[layer_num]
        __A         =	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}."    )

    __A         =	string_left[1:]

    if "resnets" in new_layer:
        __A         =	convert_resconv_naming(__lowercase    )
    elif "attentions" in new_layer:
        __A         =	convert_attn_naming(__lowercase    )
        __A         =	new_string_left

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Dict:





    """simple docstring"""

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

        __A         =	rename(__lowercase    )

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

    return new_state_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			List[Any]			,				__lowercase   :			int    )						->      Any:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any    )						->      List[Any]:





    """simple docstring"""

    __A         =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )

    __A         =	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()}"
        __A         =	download(__lowercase    )

    __A         =	MODELS_MAP[model_name]["""sample_rate"""]
    __A         =	MODELS_MAP[model_name]["""sample_size"""]

    __A         =	Object()
    __A         =	sample_size
    __A         =	sample_rate
    __A         =	0

    __A         =	UNetaDModel(sample_size=__lowercase			,				sample_rate=__lowercase    )
    __A         =	diffusers_model.state_dict()

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

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

        __A         =	value

    diffusers_model.load_state_dict(__lowercase    )

    __A         =	1_0_0
    __A         =	3_3

    __A         =	IPNDMScheduler(num_train_timesteps=__lowercase    )

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

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

    __A         =	DanceDiffusionPipeline(unet=__lowercase			,				scheduler=__lowercase    )

    __A         =	torch.manual_seed(3_3    )
    __A         =	pipe(num_inference_steps=__lowercase			,				generator=__lowercase    ).audios

    __A         =	sampling.iplms_sample(__lowercase			,				__lowercase			,				__lowercase			,				{}    )
    __A         =	generated.clamp(-1			,				1    )

    __A         =	(generated - audio).abs().sum()
    __A         =	(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__":
  __a      :       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.")
  __a      :       int				  =   parser.parse_args()

  main(args)












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__a      :       List[str]				  =   pytest.mark.integration



@pytest.mark.parametrize("""path"""			,				["""paws""", """csv"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Optional[int]    )						->      Tuple:





    """simple docstring"""

    inspect_dataset(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""    )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
@pytest.mark.parametrize("""path"""			,				["""accuracy"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Any:





    """simple docstring"""

    inspect_metric(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.parametrize(
    """path, config_name, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Optional[int]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	get_dataset_config_info(__lowercase			,				config_name=__lowercase    )
    assert info.config_name == config_name
    assert list(info.splits.keys()    ) == expected_splits



@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			int			,				__lowercase   :			List[str]    )						->      int:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_config_info(__lowercase			,				config_name=__lowercase    )



@pytest.mark.parametrize(
    """path, expected"""			,				[
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Optional[Any]    )						->      Any:





    """simple docstring"""

    __A         =	get_dataset_config_names(__lowercase    )
    assert expected in config_names



@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""			,				[
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int			,				__lowercase   :			Tuple			,				__lowercase   :			List[Any]    )						->      List[str]:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert list(infos.keys()    ) == expected_configs
    __A         =	expected_configs[0]
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits_in_first_config



@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			str			,				__lowercase   :			Union[str, Any]    )						->      Dict:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits






@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Union[str, Any]:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_split_names(__lowercase			,				config_name=__lowercase    )




