


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": 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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




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




class    UpperCAmelCase					(       __snake_case    ):
			pass




class    UpperCAmelCase					(       nn.Module    ):




			def __init__(						self							:		List[str]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						super().__init__()

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		Any=13	):





			"""simple docstring"""




			UpperCamelCase         =					input_string

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

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

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

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

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

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

			UpperCamelCase         =					string_left[1:]

			if "resnets" in new_layer:
						UpperCamelCase         =					convert_resconv_naming(SCREAMING_SNAKE_CASE_	)
			elif "attentions" in new_layer:
						UpperCamelCase         =					convert_attn_naming(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					new_string_left

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




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

						UpperCamelCase         =					rename(SCREAMING_SNAKE_CASE_	)

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

			return new_state_dict






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




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

			UpperCamelCase         =					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()}'
						UpperCamelCase         =					download(SCREAMING_SNAKE_CASE_	)

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

			UpperCamelCase         =					Object()
			UpperCamelCase         =					sample_size
			UpperCamelCase         =					sample_rate
			UpperCamelCase         =					0

			UpperCamelCase         =					UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_       ,  sample_rate=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					diffusers_model.state_dict()

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

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

						UpperCamelCase         =					value

			diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					100
			UpperCamelCase         =					33

			UpperCamelCase         =					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_	)

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

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

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

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

			UpperCamelCase         =					sampling.iplms_sample(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  {}	)
			UpperCamelCase         =					generated.clamp(-1       ,  1	)

			UpperCamelCase         =					(generated - audio).abs().sum()
			UpperCamelCase         =					(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)








import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""						)
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""						)

						super().__init__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




			def __call__(						self							:		int       ,       __magic_name__							:		Optional[ImageInput] = None       ,       __magic_name__							:		Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = False       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

						if text is not None:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.tokenizer.batch_decode(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return self.tokenizer.decode(*__magic_name__       ,       **__magic_name__						)




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,       __magic_name__       ,       )
						return self.image_processor_class





			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,       __magic_name__       ,       )
						return self.image_processor




