






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


SCREAMING_SNAKE_CASE__   :      Tuple				=		{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 6_55_36,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 6_55_36,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 13_10_72,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
}

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






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

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.sin(t * math.pi / 2			) ** 2
				__magic_name__		:Optional[int]   =   (1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(snake_case,	snake_case			)
class     lowerCamelCase_					(   lowerCamelCase			):
				pass







class     lowerCamelCase_					(   nn.Module			):







				def __init__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__()

								__magic_name__		:List[str]   =   DiffusionAttnUnetaD(__lowerCAmelCase							,       n_attn_layers=4  )
								__magic_name__		:Any   =   deepcopy(self.diffusion  )
								__magic_name__		:Tuple   =   torch.quasirandom.SobolEngine(1							,       scramble=__lowerCAmelCase  )







def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   MODELS_MAP[model_name]['''url''']
				os.system(f'''wget {url} ./'''			)

				return f'''./{model_name}.ckpt'''


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
SCREAMING_SNAKE_CASE__   :      List[str]				=		{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
SCREAMING_SNAKE_CASE__   :      int				=		{
    """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""",
}
SCREAMING_SNAKE_CASE__   :      List[str]				=		{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

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

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

def   __lowercase       (			snake_case			):







				"""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       (			snake_case			):







				"""simple docstring"""






				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=1_3			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   input_string

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

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

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

				if string.startswith('''main.'''			):
								__magic_name__		:Optional[int]   =   string[5:]

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

				if depth == max_depth:
								__magic_name__		:Optional[int]   =   MID_NUM_TO_LAYER[layer_num]
								__magic_name__		:Tuple   =   '''mid_block'''
				elif depth > 0 and int(snake_case			) < 7:
								__magic_name__		:List[Any]   =   DOWN_NUM_TO_LAYER[layer_num]
								__magic_name__		:Optional[Any]   =   f'''down_blocks.{depth}'''
				elif depth > 0 and int(snake_case			) > 7:
								__magic_name__		:int   =   UP_NUM_TO_LAYER[layer_num]
								__magic_name__		:Union[str, Any]   =   f'''up_blocks.{max_depth - depth - 1}'''
				elif depth == 0:
								__magic_name__		:Dict   =   DEPTH_0_TO_LAYER[layer_num]
								__magic_name__		: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}.'''			)

				__magic_name__		:str   =   string_left[1:]

				if "resnets" in new_layer:
								__magic_name__		:Optional[Any]   =   convert_resconv_naming(snake_case			)
				elif "attentions" in new_layer:
								__magic_name__		:List[Any]   =   convert_attn_naming(snake_case			)
								__magic_name__		:Optional[Any]   =   new_string_left

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

def   __lowercase       (			snake_case			):







				"""simple docstring"""






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

								__magic_name__		:Tuple   =   rename(snake_case			)

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

				return new_state_dict

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)

				__magic_name__		: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()}'''
								__magic_name__		:Optional[Any]   =   download(snake_case			)

				__magic_name__		:int   =   MODELS_MAP[model_name]['''sample_rate''']
				__magic_name__		:Optional[int]   =   MODELS_MAP[model_name]['''sample_size''']

				__magic_name__		:List[Any]   =   Object()
				__magic_name__		:Any   =   sample_size
				__magic_name__		:str   =   sample_rate
				__magic_name__		:str   =   0

				__magic_name__		:Tuple   =   UNetaDModel(sample_size=snake_case,	sample_rate=snake_case			)
				__magic_name__		:Any   =   diffusers_model.state_dict()

				__magic_name__		:Dict   =   DiffusionUncond(snake_case			)
				orig_model.load_state_dict(torch.load(args.model_path,	map_location=snake_case			)['''state_dict''']			)
				__magic_name__		:List[Any]   =   orig_model.diffusion_ema.eval()
				__magic_name__		:Union[str, Any]   =   orig_model.state_dict()
				__magic_name__		:Tuple   =   rename_orig_weights(snake_case			)

				__magic_name__		:int   =   set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
				__magic_name__		:List[str]   =   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":
												__magic_name__		:Dict   =   value.squeeze()

								__magic_name__		:Optional[int]   =   value

				diffusers_model.load_state_dict(snake_case			)

				__magic_name__		:Tuple   =   1_0_0
				__magic_name__		:List[Any]   =   3_3

				__magic_name__		:Union[str, Any]   =   IPNDMScheduler(num_train_timesteps=snake_case			)

				__magic_name__		:Any   =   torch.manual_seed(snake_case			)
				__magic_name__		:List[str]   =   torch.randn([1, 2, config.sample_size],	generator=snake_case			).to(snake_case			)

				__magic_name__		:Any   =   torch.linspace(1,	0,	steps + 1,	device=snake_case			)[:-1]
				__magic_name__		:Optional[Any]   =   get_crash_schedule(snake_case			)

				__magic_name__		:int   =   DanceDiffusionPipeline(unet=snake_case,	scheduler=snake_case			)

				__magic_name__		:Any   =   torch.manual_seed(3_3			)
				__magic_name__		:int   =   pipe(num_inference_steps=snake_case,	generator=snake_case			).audios

				__magic_name__		:Optional[Any]   =   sampling.iplms_sample(snake_case,	snake_case,	snake_case,	{}			)
				__magic_name__		:Tuple   =   generated.clamp(-1,	1			)

				__magic_name__		:Tuple   =   (generated - audio).abs().sum()
				__magic_name__		:Optional[Any]   =   (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__":
					SCREAMING_SNAKE_CASE__   :      List[Any]				=		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.""")
					SCREAMING_SNAKE_CASE__   :      str				=		parser.parse_args()

					main(args)









'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)
    processor.save_pretrained(
        self.tmpdirname ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			speaker_embeddings_directory=self.speaker_embeddings_directory ,			)

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        self.tmpdirname ,			self.speaker_embeddings_dict_path ,			bos_token='(BOS)' ,			eos_token='(EOS)' ,			)

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)  ),
        'fine_prompt': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from the hub
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=self.voice_preset  )





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key] ,			encoded_processor[key].squeeze().tolist()  )


