


"""simple docstring"""




import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

__UpperCamelCase						=      {
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

__UpperCamelCase						=      {
    '''vinai/phobert-base''': 256,
    '''vinai/phobert-large''': 256,
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE													=					set()
						SCREAMING_SNAKE_CASE													=					word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)  )
												SCREAMING_SNAKE_CASE													=					char

						SCREAMING_SNAKE_CASE													=					set(SCREAMING_SNAKE_CASE_  )
						return pairs


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[str]								=     VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_						:     List[str]								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__="<s>"						,       lowerCAmelCase__="</s>"						,       lowerCAmelCase__="</s>"						,       lowerCAmelCase__="<s>"						,       lowerCAmelCase__="<unk>"						,       lowerCAmelCase__="<pad>"						,       lowerCAmelCase__="<mask>"						,       **lowerCAmelCase__						,       )     ->							Dict:
								super().__init__(
								    bos_token=lowerCAmelCase__						,       eos_token=lowerCAmelCase__						,       unk_token=lowerCAmelCase__						,       sep_token=lowerCAmelCase__						,       cls_token=lowerCAmelCase__						,       pad_token=lowerCAmelCase__						,       mask_token=lowerCAmelCase__						,       **lowerCAmelCase__						,       )

								SCREAMING_SNAKE_CASE													=					vocab_file
								SCREAMING_SNAKE_CASE													=					merges_file

								SCREAMING_SNAKE_CASE													=					{}
								SCREAMING_SNAKE_CASE													=					0
								SCREAMING_SNAKE_CASE													=					1
								SCREAMING_SNAKE_CASE													=					2
								SCREAMING_SNAKE_CASE													=					3

								self.add_from_file(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					{v: k for k, v in self.encoder.items()}

								with open(lowerCAmelCase__						,       encoding='utf-8'				) as merges_handle:
														SCREAMING_SNAKE_CASE													=					merges_handle.read().split('\n'				)[:-1]
								SCREAMING_SNAKE_CASE													=					[tuple(merge.split()[:-1]				) for merge in merges]
								SCREAMING_SNAKE_CASE													=					dict(zip(lowerCAmelCase__						,       range(len(lowerCAmelCase__				)				)				)				)
								SCREAMING_SNAKE_CASE													=					{}







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[int]:

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







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = False				)     ->							List[int]:

								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowerCAmelCase__						,       token_ids_a=lowerCAmelCase__						,       already_has_special_tokens=lowerCAmelCase__				)

								if token_ids_a is None:
														return [1] + ([0] * len(lowerCAmelCase__				)) + [1]
								return [1] + ([0] * len(lowerCAmelCase__				)) + [1, 1] + ([0] * len(lowerCAmelCase__				)) + [1]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[int]:
								SCREAMING_SNAKE_CASE													=					[self.sep_token_id]
								SCREAMING_SNAKE_CASE													=					[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]







		@property
		def 							__A						(	self				)     ->							int:
								return len(self.encoder				)







		def 							__A						(	self				)     ->							Tuple:
								return dict(self.encoder						,       **self.added_tokens_encoder				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[str]:
								if token in self.cache:
														return self.cache[token]
								SCREAMING_SNAKE_CASE													=					tuple(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					tuple(list(word[:-1]				) + [word[-1] + '</w>']				)
								SCREAMING_SNAKE_CASE													=					get_pairs(lowerCAmelCase__				)

								if not pairs:
														return token

								while True:
														SCREAMING_SNAKE_CASE													=					min(lowerCAmelCase__						,       key=lambda lowerCAmelCase__				: self.bpe_ranks.get(lowerCAmelCase__						,       float('inf'				)				)				)
														if bigram not in self.bpe_ranks:
																				break
														SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					bigram
														SCREAMING_SNAKE_CASE													=					[]
														SCREAMING_SNAKE_CASE													=					0
														while i < len(lowerCAmelCase__				):
																				try:
																										SCREAMING_SNAKE_CASE													=					word.index(lowerCAmelCase__						,       lowerCAmelCase__				)
																				except ValueError:
																										new_word.extend(word[i:]				)
																										break
																				else:
																										new_word.extend(word[i:j]				)
																										SCREAMING_SNAKE_CASE													=					j

																				if word[i] == first and i < len(lowerCAmelCase__				) - 1 and word[i + 1] == second:
																										new_word.append(first + second				)
																										i += 2
																				else:
																										new_word.append(word[i]				)
																										i += 1
														SCREAMING_SNAKE_CASE													=					tuple(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					new_word
														if len(lowerCAmelCase__				) == 1:
																				break
														else:
																				SCREAMING_SNAKE_CASE													=					get_pairs(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					'@@ '.join(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					word[:-4]
								SCREAMING_SNAKE_CASE													=					word
								return word







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					[]

								SCREAMING_SNAKE_CASE													=					re.findall(r'\S+\n?'						,       lowerCAmelCase__				)

								for token in words:
														split_tokens.extend(list(self.bpe(lowerCAmelCase__				).split(' '				)				)				)
								return split_tokens







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							int:
								return self.encoder.get(lowerCAmelCase__						,       self.encoder.get(self.unk_token				)				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[Any]:
								return self.decoder.get(lowerCAmelCase__						,       self.unk_token				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							int:
								SCREAMING_SNAKE_CASE													=					' '.join(lowerCAmelCase__				).replace('@@ '						,       ''				).strip()
								return out_string







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							Tuple[str]:
								if not os.path.isdir(lowerCAmelCase__				):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
														return
								SCREAMING_SNAKE_CASE													=					os.path.join(
								    lowerCAmelCase__						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
								SCREAMING_SNAKE_CASE													=					os.path.join(
								    lowerCAmelCase__						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)

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

								if os.path.abspath(self.merges_file				) != os.path.abspath(lowerCAmelCase__				):
														copyfile(self.merges_file						,       lowerCAmelCase__				)

								return out_vocab_file, out_merge_file







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							str:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														try:
																				with open(lowerCAmelCase__						,       'r'						,       encoding='utf-8'				) as fd:
																										self.add_from_file(lowerCAmelCase__				)
														except FileNotFoundError as fnfe:
																				raise fnfe
														except UnicodeError:
																				raise Exception(F'Incorrect encoding detected in {f}, please rebuild the dataset'				)
														return

								SCREAMING_SNAKE_CASE													=					f.readlines()
								for lineTmp in lines:
														SCREAMING_SNAKE_CASE													=					lineTmp.strip()
														SCREAMING_SNAKE_CASE													=					line.rfind(' '				)
														if idx == -1:
																				raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''				)
														SCREAMING_SNAKE_CASE													=					line[:idx]
														SCREAMING_SNAKE_CASE													=					len(self.encoder				)






"""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


__UpperCamelCase						=      {
    '''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     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						return torch.atana(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) / math.pi * 2


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						int:
						SCREAMING_SNAKE_CASE													=					torch.sin(t * math.pi / 2  ) ** 2
						SCREAMING_SNAKE_CASE													=					(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		pass





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							int:
								super().__init__()

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





def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['url']
						os.system(F'wget {url} ./'  )

						return F'./{model_name}.ckpt'


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

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

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[Any]:
						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_		:				Optional[Any]  )   ->						Dict:
						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_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple=13  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					input_string

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

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

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

						if string.startswith('main.'  ):
												SCREAMING_SNAKE_CASE													=					string[5:]

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

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

						SCREAMING_SNAKE_CASE													=					string_left[1:]

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

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					{}
						for k, v in state_dict.items():
												if k.endswith('kernel'  ):
																		# up- and downsample layers, don't have trainable weights
																		continue

												SCREAMING_SNAKE_CASE													=					rename(SCREAMING_SNAKE_CASE_  )

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

						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						str:
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												if len(v.shape  ) == 3:
																		# weight
																		SCREAMING_SNAKE_CASE													=					v[:, :, 0]
												else:
																		# bias
																		SCREAMING_SNAKE_CASE													=					v
						else:
												# qkv matrices
												SCREAMING_SNAKE_CASE													=					v.shape[0]
												SCREAMING_SNAKE_CASE													=					trippled_shape // 3
												for i in range(3  ):
																		if len(v.shape  ) == 3:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

						SCREAMING_SNAKE_CASE													=					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()}'
												SCREAMING_SNAKE_CASE													=					download(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['sample_rate']
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['sample_size']

						SCREAMING_SNAKE_CASE													=					Object()
						SCREAMING_SNAKE_CASE													=					sample_size
						SCREAMING_SNAKE_CASE													=					sample_rate
						SCREAMING_SNAKE_CASE													=					0

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

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

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

												SCREAMING_SNAKE_CASE													=					value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					1_00
						SCREAMING_SNAKE_CASE													=					33

						SCREAMING_SNAKE_CASE													=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_  )

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

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

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

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

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

						SCREAMING_SNAKE_CASE													=					(generated - audio).abs().sum()
						SCREAMING_SNAKE_CASE													=					(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__":
	__UpperCamelCase						=      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.''')
	__UpperCamelCase						=      parser.parse_args()

	main(args)


