






"""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


lowerCamelCase_							      = {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 13_1072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
}

def 							snake_case (  A__      ,A__		):
			return torch.atana(A__      ,A__		) / math.pi * 2

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Optional[int]     	=   torch.sin(t * math.pi / 2		) ** 2
			UpperCAmelCase_	:					List[Any]     	=   (1 - sigma**2) ** 0.5
			return alpha_sigma_to_t(A__      ,A__		)







class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			(nn.Module	):





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					Optional[int]		)		->     Any:
				super().__init__()

				UpperCAmelCase_	:					Tuple     	=   DiffusionAttnUnetaD(lowerCAmelCase_      ,      n_attn_layers=4		)
				UpperCAmelCase_	:					Dict     	=   deepcopy(self.diffusion		)
				UpperCAmelCase_	:					Tuple     	=   torch.quasirandom.SobolEngine(1      ,      scramble=lowerCAmelCase_		)





def 							snake_case (  A__		):
			UpperCAmelCase_	:					int     	=   MODELS_MAP[model_name]["url"]
			os.system(F"""wget {url} ./"""		)

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


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

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

lowerCamelCase_							      = {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}

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

def 							snake_case (  A__      ,A__=13		):
			UpperCAmelCase_	:					List[str]     	=   input_string

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

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

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

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

			# mid block
			if string[:2].isdigit():
						UpperCAmelCase_	:					Dict     	=   string[:2]
						UpperCAmelCase_	:					Dict     	=   string[2:]
			else:
						UpperCAmelCase_	:					Union[str, Any]     	=   string[0]
						UpperCAmelCase_	:					str     	=   string[1:]

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

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

			UpperCAmelCase_	:					Any     	=   string_left[1:]

			if "resnets" in new_layer:
						UpperCAmelCase_	:					Dict     	=   convert_resconv_naming(A__		)
			elif "attentions" in new_layer:
						UpperCAmelCase_	:					Tuple     	=   convert_attn_naming(A__		)
						UpperCAmelCase_	:					Optional[Any]     	=   new_string_left

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

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

						UpperCAmelCase_	:					Dict     	=   rename(A__		)

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

			return new_state_dict

def 							snake_case (  A__      ,A__      ,A__		):
			if len(A__		) == 1:
						if len(v.shape		) == 3:
									# weight
									UpperCAmelCase_	:					Any     	=   v[:, :, 0]
						else:
									# bias
									UpperCAmelCase_	:					Any     	=   v
			else:
						# qkv matrices
						UpperCAmelCase_	:					Any     	=   v.shape[0]
						UpperCAmelCase_	:					Union[str, Any]     	=   trippled_shape // 3
						for i in range(3		):
									if len(v.shape		) == 3:
												UpperCAmelCase_	:					str     	=   v[i * single_shape : (i + 1) * single_shape, :, 0]
									else:
												UpperCAmelCase_	:					Optional[Any]     	=   v[i * single_shape : (i + 1) * single_shape]
			return new_state_dict

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   torch.device("cuda" if torch.cuda.is_available() else "cpu"		)

			UpperCAmelCase_	:					Optional[int]     	=   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_	:					Dict     	=   download(A__		)

			UpperCAmelCase_	:					int     	=   MODELS_MAP[model_name]["sample_rate"]
			UpperCAmelCase_	:					Optional[int]     	=   MODELS_MAP[model_name]["sample_size"]

			UpperCAmelCase_	:					Optional[int]     	=   Object()
			UpperCAmelCase_	:					Optional[Any]     	=   sample_size
			UpperCAmelCase_	:					Dict     	=   sample_rate
			UpperCAmelCase_	:					str     	=   0

			UpperCAmelCase_	:					Union[str, Any]     	=   UNetaDModel(sample_size=A__      ,sample_rate=A__		)
			UpperCAmelCase_	:					List[Any]     	=   diffusers_model.state_dict()

			UpperCAmelCase_	:					List[Any]     	=   DiffusionUncond(A__		)
			orig_model.load_state_dict(torch.load(args.model_path      ,map_location=A__		)["state_dict"]		)
			UpperCAmelCase_	:					Optional[Any]     	=   orig_model.diffusion_ema.eval()
			UpperCAmelCase_	:					Dict     	=   orig_model.state_dict()
			UpperCAmelCase_	:					Optional[Any]     	=   rename_orig_weights(A__		)

			UpperCAmelCase_	:					Optional[int]     	=   set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
			UpperCAmelCase_	:					Any     	=   set(diffusers_state_dict.keys()		) - set(renamed_state_dict.keys()		)

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

						UpperCAmelCase_	:					List[Any]     	=   value

			diffusers_model.load_state_dict(A__		)

			UpperCAmelCase_	:					Optional[int]     	=   1_00
			UpperCAmelCase_	:					Any     	=   33

			UpperCAmelCase_	:					Optional[Any]     	=   IPNDMScheduler(num_train_timesteps=A__		)

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

			UpperCAmelCase_	:					Optional[int]     	=   torch.linspace(1      ,0      ,steps + 1      ,device=A__		)[:-1]
			UpperCAmelCase_	:					Optional[int]     	=   get_crash_schedule(A__		)

			UpperCAmelCase_	:					Tuple     	=   DanceDiffusionPipeline(unet=A__      ,scheduler=A__		)

			UpperCAmelCase_	:					Optional[int]     	=   torch.manual_seed(33		)
			UpperCAmelCase_	:					int     	=   pipe(num_inference_steps=A__      ,generator=A__		).audios

			UpperCAmelCase_	:					Union[str, Any]     	=   sampling.iplms_sample(A__      ,A__      ,A__      ,{}		)
			UpperCAmelCase_	:					Dict     	=   generated.clamp(-1      ,1		)

			UpperCAmelCase_	:					str     	=   (generated - audio).abs().sum()
			UpperCAmelCase_	:					Optional[Any]     	=   (generated - audio).abs().max()

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

			print("Diff sum"      ,A__		)
			print("Diff max"      ,A__		)

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

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


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

			main(args)











"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Tuple     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Any     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[int]     	=   [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



