




"""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


a_				:    Any										=     {
    '''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 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    List[Any]     )  ->     Tuple:
   return torch.atana(lowerCamelCase__							,				lowerCamelCase__     ) / math.pi * 2





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

class __lowercase(      lowercase__				):




     '''simple docstring'''


     pass



class __lowercase(      nn.Module				):




     '''simple docstring'''


     def __init__(    self						,	__a     ):
        super().__init__()

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





def 						UpperCAmelCase				(			A__:    Dict     )  ->     int:
   __lowerCamelCase					:	List[Any]	       =				MODELS_MAP[model_name]['url']
   os.system(f'''wget {url} ./'''     )

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


a_				:    Tuple										=     {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
a_				:    str										=     {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
a_				:    Union[str, Any]										=     {
    '''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_				:    Optional[Any]										=     {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

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

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





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     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 						UpperCAmelCase				(			A__:    str     )  ->     Optional[Any]:
   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 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    Optional[Any]=13     )  ->     Any:
   __lowerCamelCase					:	Union[str, Any]	       =				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					:	Tuple	       =				string[6:]
   elif string.startswith('net.'     ):
      __lowerCamelCase					:	Optional[Any]	       =				string[4:]

   while string.startswith('main.7.'     ):
      depth += 1
      __lowerCamelCase					:	Union[str, Any]	       =				string[7:]

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

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

   if depth == max_depth:
      __lowerCamelCase					:	Tuple	       =				MID_NUM_TO_LAYER[layer_num]
      __lowerCamelCase					:	Dict	       =				'mid_block'
   elif depth > 0 and int(lowerCamelCase__     ) < 7:
      __lowerCamelCase					:	Tuple	       =				DOWN_NUM_TO_LAYER[layer_num]
      __lowerCamelCase					:	Optional[Any]	       =				f'''down_blocks.{depth}'''
   elif depth > 0 and int(lowerCamelCase__     ) > 7:
      __lowerCamelCase					:	str	       =				UP_NUM_TO_LAYER[layer_num]
      __lowerCamelCase					:	Tuple	       =				f'''up_blocks.{max_depth - depth - 1}'''
   elif depth == 0:
      __lowerCamelCase					:	List[str]	       =				DEPTH_0_TO_LAYER[layer_num]
      __lowerCamelCase					:	Optional[int]	       =				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					:	List[str]	       =				convert_resconv_naming(lowerCamelCase__     )
   elif "attentions" in new_layer:
      __lowerCamelCase					:	List[str]	       =				convert_attn_naming(lowerCamelCase__     )
      __lowerCamelCase					:	Dict	       =				new_string_left

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





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

      __lowerCamelCase					:	Optional[Any]	       =				rename(lowerCamelCase__     )

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

   return new_state_dict





def 						UpperCAmelCase				(			A__:    int							,				A__:    Optional[int]							,				A__:    int     )  ->     List[Any]:
   if len(lowerCamelCase__     ) == 1:
      if len(v.shape     ) == 3:
         # weight
         __lowerCamelCase					:	Union[str, Any]	       =				v[:, :, 0]
      else:
         # bias
         __lowerCamelCase					:	Any	       =				v
   else:
      # qkv matrices
      __lowerCamelCase					:	Any	       =				v.shape[0]
      __lowerCamelCase					:	Dict	       =				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 						UpperCAmelCase				(			A__:    List[str]     )  ->     str:
   __lowerCamelCase					:	Tuple	       =				torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )

   __lowerCamelCase					:	Optional[Any]	       =				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					:	str	       =				download(lowerCamelCase__     )

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

   __lowerCamelCase					:	Dict	       =				Object()
   __lowerCamelCase					:	List[str]	       =				sample_size
   __lowerCamelCase					:	List[Any]	       =				sample_rate
   __lowerCamelCase					:	Tuple	       =				0

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

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

   __lowerCamelCase					:	List[Any]	       =				set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
   __lowerCamelCase					:	Optional[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					:	Optional[int]	       =				value.squeeze()

      __lowerCamelCase					:	Optional[Any]	       =				value

   diffusers_model.load_state_dict(lowerCamelCase__     )

   __lowerCamelCase					:	List[str]	       =				100
   __lowerCamelCase					:	int	       =				33

   __lowerCamelCase					:	List[str]	       =				IPNDMScheduler(num_train_timesteps=lowerCamelCase__     )

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

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

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

   __lowerCamelCase					:	str	       =				torch.manual_seed(33     )
   __lowerCamelCase					:	Tuple	       =				pipe(num_inference_steps=lowerCamelCase__							,				generator=lowerCamelCase__     ).audios

   __lowerCamelCase					:	Tuple	       =				sampling.iplms_sample(lowerCamelCase__							,				lowerCamelCase__							,				lowerCamelCase__							,				{}     )
   __lowerCamelCase					:	Dict	       =				generated.clamp(-1							,				1     )

   __lowerCamelCase					:	int	       =				(generated - audio).abs().sum()
   __lowerCamelCase					:	List[Any]	       =				(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_				:    Tuple										=     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_				:    Tuple										=     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_barthez import BarthezTokenizer
else:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Union[str, Any]     		= ['input_ids', 'attention_mask']
					__a						:						Optional[Any]     		= BarthezTokenizer
					def __init__(    self						,	__a=None						,	__a=None						,	__a="<s>"						,	__a="</s>"						,	__a="</s>"						,	__a="<s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<mask>"						,	**__a						,	):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Optional[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

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

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	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     snake_case_				(    self						,	__a						,	__a = None     ):
								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(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

								return (out_vocab_file,)



