



"""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							:   Tuple      =    {
    '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				(  __snake_case					,__snake_case	)						->   Tuple:
			return torch.atana(__snake_case					,__snake_case	) / math.pi * 2


def 			_lowercase				(  __snake_case	)						->   Any:
			__lowerCAmelCase      :  Any												=       torch.sin(t * math.pi / 2	) ** 2
			__lowerCAmelCase      :  Optional[Any]												=       (1 - sigma**2) ** 0.5
			return alpha_sigma_to_t(__snake_case					,__snake_case	)



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	pass






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase      :  int												=       DiffusionAttnUnetaD(_SCREAMING_SNAKE_CASE       ,			n_attn_layers=4)
				__lowerCAmelCase      :  Union[str, Any]												=       deepcopy(self.diffusion)
				__lowerCAmelCase      :  Any												=       torch.quasirandom.SobolEngine(1       ,			scramble=_SCREAMING_SNAKE_CASE)






def 			_lowercase				(  __snake_case	)						->   Union[str, Any]:
			__lowerCAmelCase      :  Union[str, Any]												=       MODELS_MAP[model_name]["url"]
			os.system(F"""wget {url} ./"""	)

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


__snake_case							:   Tuple      =    {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__snake_case							:   int      =    {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__snake_case							:   Dict      =    {
    '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							:   List[Any]      =    {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

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

__snake_case							:   Union[str, Any]      =    {
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


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


def 			_lowercase				(  __snake_case					,__snake_case=13	)						->   str:
			__lowerCAmelCase      :  Optional[int]												=       input_string

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

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

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

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

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

			if depth == max_depth:
						__lowerCAmelCase      :  Dict												=       MID_NUM_TO_LAYER[layer_num]
						__lowerCAmelCase      :  Any												=       "mid_block"
			elif depth > 0 and int(__snake_case	) < 7:
						__lowerCAmelCase      :  int												=       DOWN_NUM_TO_LAYER[layer_num]
						__lowerCAmelCase      :  Tuple												=       F"""down_blocks.{depth}"""
			elif depth > 0 and int(__snake_case	) > 7:
						__lowerCAmelCase      :  str												=       UP_NUM_TO_LAYER[layer_num]
						__lowerCAmelCase      :  Union[str, Any]												=       F"""up_blocks.{max_depth - depth - 1}"""
			elif depth == 0:
						__lowerCAmelCase      :  Tuple												=       DEPTH_0_TO_LAYER[layer_num]
						__lowerCAmelCase      :  Any												=       F"""up_blocks.{max_depth - 1}""" if int(__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      :  Tuple												=       string_left[1:]

			if "resnets" in new_layer:
						__lowerCAmelCase      :  int												=       convert_resconv_naming(__snake_case	)
			elif "attentions" in new_layer:
						__lowerCAmelCase      :  Any												=       convert_attn_naming(__snake_case	)
						__lowerCAmelCase      :  List[str]												=       new_string_left

			if not isinstance(__snake_case					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       prefix + "." + new_layer + "." + string_left
			else:
						__lowerCAmelCase      :  Union[str, Any]												=       [prefix + "." + new_layer + "." + s for s in string_left]
			return new_string


def 			_lowercase				(  __snake_case	)						->   List[str]:
			__lowerCAmelCase      :  Optional[Any]												=       {}
			for k, v in state_dict.items():
						if k.endswith("kernel"	):
									# up- and downsample layers, don't have trainable weights
									continue

						__lowerCAmelCase      :  str												=       rename(__snake_case	)

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

			return new_state_dict


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Optional[Any]:
			if len(__snake_case	) == 1:
						if len(v.shape	) == 3:
									# weight
									__lowerCAmelCase      :  List[str]												=       v[:, :, 0]
						else:
									# bias
									__lowerCAmelCase      :  str												=       v
			else:
						# qkv matrices
						__lowerCAmelCase      :  Optional[int]												=       v.shape[0]
						__lowerCAmelCase      :  Any												=       trippled_shape // 3
						for i in range(3	):
									if len(v.shape	) == 3:
												__lowerCAmelCase      :  Tuple												=       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 			_lowercase				(  __snake_case	)						->   Tuple:
			__lowerCAmelCase      :  int												=       torch.device("cuda" if torch.cuda.is_available() else "cpu"	)

			__lowerCAmelCase      :  Dict												=       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      :  int												=       download(__snake_case	)

			__lowerCAmelCase      :  Dict												=       MODELS_MAP[model_name]["sample_rate"]
			__lowerCAmelCase      :  Optional[Any]												=       MODELS_MAP[model_name]["sample_size"]

			__lowerCAmelCase      :  int												=       Object()
			__lowerCAmelCase      :  str												=       sample_size
			__lowerCAmelCase      :  str												=       sample_rate
			__lowerCAmelCase      :  str												=       0

			__lowerCAmelCase      :  Dict												=       UNetaDModel(sample_size=__snake_case					,sample_rate=__snake_case	)
			__lowerCAmelCase      :  List[Any]												=       diffusers_model.state_dict()

			__lowerCAmelCase      :  Union[str, Any]												=       DiffusionUncond(__snake_case	)
			orig_model.load_state_dict(torch.load(args.model_path					,map_location=__snake_case	)["state_dict"]	)
			__lowerCAmelCase      :  Any												=       orig_model.diffusion_ema.eval()
			__lowerCAmelCase      :  Any												=       orig_model.state_dict()
			__lowerCAmelCase      :  int												=       rename_orig_weights(__snake_case	)

			__lowerCAmelCase      :  List[Any]												=       set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
			__lowerCAmelCase      :  Union[str, Any]												=       set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

			assert len(__snake_case	) == 0, F"""Problem with {renamed_minus_diffusers}"""
			assert all(k.endswith("kernel"	) for k in list(__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      :  int												=       value.squeeze()

						__lowerCAmelCase      :  Dict												=       value

			diffusers_model.load_state_dict(__snake_case	)

			__lowerCAmelCase      :  List[str]												=       100
			__lowerCAmelCase      :  Dict												=       33

			__lowerCAmelCase      :  Tuple												=       IPNDMScheduler(num_train_timesteps=__snake_case	)

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

			__lowerCAmelCase      :  List[str]												=       torch.linspace(1					,0					,steps + 1					,device=__snake_case	)[:-1]
			__lowerCAmelCase      :  Optional[int]												=       get_crash_schedule(__snake_case	)

			__lowerCAmelCase      :  List[str]												=       DanceDiffusionPipeline(unet=__snake_case					,scheduler=__snake_case	)

			__lowerCAmelCase      :  Any												=       torch.manual_seed(33	)
			__lowerCAmelCase      :  Any												=       pipe(num_inference_steps=__snake_case					,generator=__snake_case	).audios

			__lowerCAmelCase      :  str												=       sampling.iplms_sample(__snake_case					,__snake_case					,__snake_case					,{}	)
			__lowerCAmelCase      :  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"					,__snake_case	)
			print("Diff max"					,__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							:   int      =    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							:   List[str]      =    parser.parse_args()

	main(args)




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)