
"""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


_snake_case          =						{
    "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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			torch.sin(t * math.pi / 2						) ** 2
							_lowerCAmelCase  	=			(1 - sigma**2) ** 0.5
							return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_						)
class        _SCREAMING_SNAKE_CASE  (  _lowerCamelCase							):






				'''simple docstring'''



				pass






class        _SCREAMING_SNAKE_CASE  (  nn.Module							):






				'''simple docstring'''



				def __init__(					self	:			int				,     UpperCAmelCase_	:			Dict  )		->  Dict:







											"""simple docstring"""




											super().__init__()

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



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

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


_snake_case          =						{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
_snake_case          =						{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
_snake_case          =						{
    "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",
}
_snake_case          =						{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

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

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]=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(SCREAMING_SNAKE_CASE_						) < 7:
														_lowerCAmelCase  	=			DOWN_NUM_TO_LAYER[layer_num]
														_lowerCAmelCase  	=			f"""down_blocks.{depth}"""
							elif depth > 0 and int(SCREAMING_SNAKE_CASE_						) > 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(SCREAMING_SNAKE_CASE_						) > 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(SCREAMING_SNAKE_CASE_						)
							elif "attentions" in new_layer:
														_lowerCAmelCase  	=			convert_attn_naming(SCREAMING_SNAKE_CASE_						)
														_lowerCAmelCase  	=			new_string_left

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""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(SCREAMING_SNAKE_CASE_						)

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

							return new_state_dict






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							if len(SCREAMING_SNAKE_CASE_						) == 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 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""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(SCREAMING_SNAKE_CASE_						)

							_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=SCREAMING_SNAKE_CASE_     ,  sample_rate=SCREAMING_SNAKE_CASE_						)
							_lowerCAmelCase  	=			diffusers_model.state_dict()

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

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

							assert len(SCREAMING_SNAKE_CASE_						) == 0, f"""Problem with {renamed_minus_diffusers}"""
							assert all(k.endswith('kernel'						) for k in list(SCREAMING_SNAKE_CASE_						)						), 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(SCREAMING_SNAKE_CASE_						)

							_lowerCAmelCase  	=			100
							_lowerCAmelCase  	=			33

							_lowerCAmelCase  	=			IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_						)

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

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

							_lowerCAmelCase  	=			DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_     ,  scheduler=SCREAMING_SNAKE_CASE_						)

							_lowerCAmelCase  	=			torch.manual_seed(33						)
							_lowerCAmelCase  	=			pipe(num_inference_steps=SCREAMING_SNAKE_CASE_     ,  generator=SCREAMING_SNAKE_CASE_						).audios

							_lowerCAmelCase  	=			sampling.iplms_sample(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  {}						)
							_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'     ,  SCREAMING_SNAKE_CASE_						)
							print('Diff max'     ,  SCREAMING_SNAKE_CASE_						)

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

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


if __name__ == "__main__":
				_snake_case          =						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.''')
				_snake_case          =						parser.parse_args()

				main(args)








"""simple docstring"""



import pytest


_snake_case          =						'''__dummy_dataset1__'''

_snake_case          =						'''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dataset_loading_script_name
							_lowerCAmelCase  	=			tmp_path / 'datasets' / script_name
							script_dir.mkdir(parents=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			script_dir / f"""{script_name}.py"""
							with open(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.write(SCREAMING_SNAKE_CASE						)
							return str(SCREAMING_SNAKE_CASE						)






