

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		={
    """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__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	return torch.atana(lowerCamelCase__     ,			lowerCamelCase__      ) / math.pi * 2






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    List[Any] 	=			torch.sin(t * math.pi / 2      ) ** 2
	__lowerCamelCase    :    int 	=			(1 - sigma**2) ** 0.5
	return alpha_sigma_to_t(lowerCamelCase__     ,			lowerCamelCase__      )






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							pass






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str):
								super().__init__()

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    List[Any] 	=			MODELS_MAP[model_name]['url']
	os.system(F"wget {url} ./"      )

	return F"./{model_name}.ckpt"


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

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

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	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__	(  lowerCamelCase__      )  ->					int:
	for key, value in ATTN_MAP.items():
		if name.startswith(lowerCamelCase__      ) and not isinstance(lowerCamelCase__     ,			lowerCamelCase__      ):
			return name.replace(lowerCamelCase__     ,			lowerCamelCase__      )
		elif name.startswith(lowerCamelCase__      ):
			return [name.replace(lowerCamelCase__     ,			lowerCamelCase__      ) for v in value]
	raise ValueError(F"Attn error with {name}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=1_3      )  ->					Tuple:
	__lowerCamelCase    :    List[str] 	=			input_string

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

	__lowerCamelCase    :    Any 	=			0
	if string.startswith('net.3.'      ):
		depth += 1
		__lowerCamelCase    :    Dict 	=			string[6:]
	elif string.startswith('net.'      ):
		__lowerCamelCase    :    Dict 	=			string[4:]

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

	if string.startswith('main.'      ):
		__lowerCamelCase    :    Union[str, Any] 	=			string[5:]

	# mid block
	if string[:2].isdigit():
		__lowerCamelCase    :    Dict 	=			string[:2]
		__lowerCamelCase    :    Tuple 	=			string[2:]
	else:
		__lowerCamelCase    :    Dict 	=			string[0]
		__lowerCamelCase    :    Optional[int] 	=			string[1:]

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

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

	__lowerCamelCase    :    Any 	=			string_left[1:]

	if "resnets" in new_layer:
		__lowerCamelCase    :    Dict 	=			convert_resconv_naming(lowerCamelCase__      )
	elif "attentions" in new_layer:
		__lowerCamelCase    :    Tuple 	=			convert_attn_naming(lowerCamelCase__      )
		__lowerCamelCase    :    Optional[int] 	=			new_string_left

	if not isinstance(lowerCamelCase__     ,			lowerCamelCase__      ):
		__lowerCamelCase    :    List[Any] 	=			prefix + '.' + new_layer + '.' + string_left
	else:
		__lowerCamelCase    :    Optional[int] 	=			[prefix + '.' + new_layer + '.' + s for s in string_left]
	return new_string






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    str 	=			{}
	for k, v in state_dict.items():
		if k.endswith('kernel'      ):
			# up- and downsample layers, don't have trainable weights
			continue

		__lowerCamelCase    :    List[Any] 	=			rename(lowerCamelCase__      )

		# check if we need to transform from Conv => Linear for attention
		if isinstance(lowerCamelCase__     ,			lowerCamelCase__      ):
			__lowerCamelCase    :    Optional[int] 	=			transform_conv_attns(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
		else:
			__lowerCamelCase    :    Tuple 	=			v

	return new_state_dict






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	if len(lowerCamelCase__      ) == 1:
		if len(v.shape      ) == 3:
			# weight
			__lowerCamelCase    :    int 	=			v[:, :, 0]
		else:
			# bias
			__lowerCamelCase    :    Optional[int] 	=			v
	else:
		# qkv matrices
		__lowerCamelCase    :    Optional[Any] 	=			v.shape[0]
		__lowerCamelCase    :    int 	=			trippled_shape // 3
		for i in range(3      ):
			if len(v.shape      ) == 3:
				__lowerCamelCase    :    Optional[Any] 	=			v[i * single_shape : (i + 1) * single_shape, :, 0]
			else:
				__lowerCamelCase    :    int 	=			v[i * single_shape : (i + 1) * single_shape]
	return new_state_dict






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Dict 	=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

	__lowerCamelCase    :    List[str] 	=			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    :    Union[str, Any] 	=			download(lowerCamelCase__      )

	__lowerCamelCase    :    str 	=			MODELS_MAP[model_name]['sample_rate']
	__lowerCamelCase    :    str 	=			MODELS_MAP[model_name]['sample_size']

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

	__lowerCamelCase    :    Tuple 	=			UNetaDModel(sample_size=lowerCamelCase__     ,			sample_rate=lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			diffusers_model.state_dict()

	__lowerCamelCase    :    Tuple 	=			DiffusionUncond(lowerCamelCase__      )
	orig_model.load_state_dict(torch.load(args.model_path     ,			map_location=lowerCamelCase__      )['state_dict']      )
	__lowerCamelCase    :    str 	=			orig_model.diffusion_ema.eval()
	__lowerCamelCase    :    List[str] 	=			orig_model.state_dict()
	__lowerCamelCase    :    int 	=			rename_orig_weights(lowerCamelCase__      )

	__lowerCamelCase    :    Any 	=			set(renamed_state_dict.keys()      ) - set(diffusers_state_dict.keys()      )
	__lowerCamelCase    :    int 	=			set(diffusers_state_dict.keys()      ) - set(renamed_state_dict.keys()      )

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

		__lowerCamelCase    :    Any 	=			value

	diffusers_model.load_state_dict(lowerCamelCase__      )

	__lowerCamelCase    :    str 	=			1_0_0
	__lowerCamelCase    :    Tuple 	=			3_3

	__lowerCamelCase    :    Dict 	=			IPNDMScheduler(num_train_timesteps=lowerCamelCase__      )

	__lowerCamelCase    :    Any 	=			torch.manual_seed(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			torch.randn([1, 2, config.sample_size]     ,			generator=lowerCamelCase__      ).to(lowerCamelCase__      )

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

	__lowerCamelCase    :    Tuple 	=			DanceDiffusionPipeline(unet=lowerCamelCase__     ,			scheduler=lowerCamelCase__      )

	__lowerCamelCase    :    List[Any] 	=			torch.manual_seed(3_3      )
	__lowerCamelCase    :    int 	=			pipe(num_inference_steps=lowerCamelCase__     ,			generator=lowerCamelCase__      ).audios

	__lowerCamelCase    :    Union[str, Any] 	=			sampling.iplms_sample(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			{}      )
	__lowerCamelCase    :    Optional[int] 	=			generated.clamp(-1     ,			1      )

	__lowerCamelCase    :    Any 	=			(generated - audio).abs().sum()
	__lowerCamelCase    :    List[str] 	=			(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
					a		=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		=parser.parse_args()

					main(args)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




