'''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=  {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 1_3_1_0_7_2,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
}







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	str			):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	Any			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  torch.sin(t * math.pi / 2			) ** 2
    __UpperCamelCase	:	Any		    =  (1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(_a				,     _a			)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						nn.Module						):







     """simple docstring"""






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

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







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  MODELS_MAP[model_name]['url']
    os.system(f"""wget {url} ./"""			)

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


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

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

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







def  _UpperCamelCase (     _a	:	List[Any]			):




    """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  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    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  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	Optional[int]=1_3			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  input_string

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

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

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

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

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

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

    if "resnets" in new_layer:
        __UpperCamelCase	:	Optional[int]		    =  convert_resconv_naming(_a			)
    elif "attentions" in new_layer:
        __UpperCamelCase	:	Optional[Any]		    =  convert_attn_naming(_a			)
        __UpperCamelCase	:	Any		    =  new_string_left

    if not isinstance(_a				,     _a			):
        __UpperCamelCase	:	Any		    =  prefix + '.' + new_layer + '.' + string_left
    else:
        __UpperCamelCase	:	int		    =  [prefix + '.' + new_layer + '.' + s for s in string_left]
    return new_string







def  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  {}
    for k, v in state_dict.items():
        if k.endswith('kernel'			):
            # up- and downsample layers, don't have trainable weights
            continue

        __UpperCamelCase	:	Union[str, Any]		    =  rename(_a			)

        # check if we need to transform from Conv => Linear for attention
        if isinstance(_a				,     _a			):
            __UpperCamelCase	:	Any		    =  transform_conv_attns(_a				,     _a				,     _a			)
        else:
            __UpperCamelCase	:	int		    =  v

    return new_state_dict







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	int				,     _a	:	str			):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

    __UpperCamelCase	:	Union[str, 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()}"""
        __UpperCamelCase	:	List[str]		    =  download(_a			)

    __UpperCamelCase	:	int		    =  MODELS_MAP[model_name]['sample_rate']
    __UpperCamelCase	:	Tuple		    =  MODELS_MAP[model_name]['sample_size']

    __UpperCamelCase	:	List[Any]		    =  Object()
    __UpperCamelCase	:	Tuple		    =  sample_size
    __UpperCamelCase	:	Any		    =  sample_rate
    __UpperCamelCase	:	Optional[int]		    =  0

    __UpperCamelCase	:	Any		    =  UNetaDModel(sample_size=_a				,     sample_rate=_a			)
    __UpperCamelCase	:	Optional[Any]		    =  diffusers_model.state_dict()

    __UpperCamelCase	:	str		    =  DiffusionUncond(_a			)
    orig_model.load_state_dict(torch.load(args.model_path				,     map_location=_a			)['state_dict']			)
    __UpperCamelCase	:	Optional[int]		    =  orig_model.diffusion_ema.eval()
    __UpperCamelCase	:	Optional[int]		    =  orig_model.state_dict()
    __UpperCamelCase	:	Tuple		    =  rename_orig_weights(_a			)

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

        __UpperCamelCase	:	Union[str, Any]		    =  value

    diffusers_model.load_state_dict(_a			)

    __UpperCamelCase	:	Any		    =  1_0_0
    __UpperCamelCase	:	str		    =  3_3

    __UpperCamelCase	:	Dict		    =  IPNDMScheduler(num_train_timesteps=_a			)

    __UpperCamelCase	:	Any		    =  torch.manual_seed(_a			)
    __UpperCamelCase	:	Tuple		    =  torch.randn([1, 2, config.sample_size]				,     generator=_a			).to(_a			)

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

    __UpperCamelCase	:	Dict		    =  DanceDiffusionPipeline(unet=_a				,     scheduler=_a			)

    __UpperCamelCase	:	List[Any]		    =  torch.manual_seed(3_3			)
    __UpperCamelCase	:	Any		    =  pipe(num_inference_steps=_a				,     generator=_a			).audios

    __UpperCamelCase	:	int		    =  sampling.iplms_sample(_a				,     _a				,     _a				,     {}			)
    __UpperCamelCase	:	str		    =  generated.clamp(-1				,     1			)

    __UpperCamelCase	:	Optional[Any]		    =  (generated - audio).abs().sum()
    __UpperCamelCase	:	str		    =  (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__":
  a=  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=  parser.parse_args()

  main(args)




'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


a=  logging.get_logger(__name__)

a=  '''▁'''

a=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a=  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

a=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a=  {'''mustc''': MUSTC_LANGS}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						MAX_MODEL_INPUT_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE__		   =						[]







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			do_upper_case=_lowerCamelCase     ,			do_lower_case=_lowerCamelCase     ,			tgt_lang=_lowerCamelCase     ,			lang_codes=_lowerCamelCase     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**_lowerCamelCase     ,			)
         __UpperCamelCase	:	Union[str, Any]		    =  do_upper_case
         __UpperCamelCase	:	Dict		    =  do_lower_case

         __UpperCamelCase	:	List[str]		    =  load_json(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  {v: k for k, v in self.encoder.items()}
         __UpperCamelCase	:	int		    =  spm_file
         __UpperCamelCase	:	List[Any]		    =  load_spm(_lowerCamelCase     ,			self.sp_model_kwargs    )

         if lang_codes is not None:
             __UpperCamelCase	:	Any		    =  lang_codes
             __UpperCamelCase	:	Any		    =  LANGUAGES[lang_codes]
             __UpperCamelCase	:	str		    =  [f"""<lang:{lang}>""" for lang in self.langs]
             __UpperCamelCase	:	List[str]		    =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

             __UpperCamelCase	:	str		    =  self.lang_tokens
             __UpperCamelCase	:	str		    =  tgt_lang if tgt_lang is not None else self.langs[0]

             self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
             __UpperCamelCase	:	Dict		    =  {}







     @property
     def lowerCAmelCase     (		self    ):
         return len(self.encoder    )







     @property
     def lowerCAmelCase     (		self    ):
         return self._tgt_lang







     @tgt_lang.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self.lang_code_to_id[tgt_lang]
         __UpperCamelCase	:	List[str]		    =  [lang_code_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.sp_model.encode(_lowerCamelCase     ,			out_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.encoder.get(_lowerCamelCase     ,			self.encoder[self.unk_token]    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.decoder.get(_lowerCamelCase     ,			self.unk_token    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  []
         __UpperCamelCase	:	List[str]		    =  ''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 __UpperCamelCase	:	int		    =  self.sp_model.decode(_lowerCamelCase    )
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 __UpperCamelCase	:	int		    =  []
             else:
                 current_sub_tokens.append(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.sp_model.decode(_lowerCamelCase    )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False    ):

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

         __UpperCamelCase	:	Union[str, Any]		    =  [1] * len(self.prefix_tokens    )
         __UpperCamelCase	:	Any		    =  [1]
         if token_ids_a is None:
             return prefix_ones + ([0] * len(_lowerCamelCase    )) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCamelCase    )) + ([0] * len(_lowerCamelCase    )) + suffix_ones







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab







     def __getstate__(		self    ):
         __UpperCamelCase	:	int		    =  self.__dict__.copy()
         __UpperCamelCase	:	Dict		    =  None
         return state







     def __setstate__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  d

         # for backward compatibility
         if not hasattr(self     ,			'sp_model_kwargs'    ):
             __UpperCamelCase	:	Optional[int]		    =  {}

         __UpperCamelCase	:	Union[str, Any]		    =  load_spm(self.spm_file     ,			self.sp_model_kwargs    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  Path(_lowerCamelCase    )
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         __UpperCamelCase	:	Optional[int]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         __UpperCamelCase	:	Union[str, Any]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder     ,			_lowerCamelCase    )

         if os.path.abspath(self.spm_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.spm_file    ):
             copyfile(self.spm_file     ,			_lowerCamelCase    )
         elif not os.path.isfile(self.spm_file    ):
             with open(_lowerCamelCase     ,			'wb'    ) as fi:
                 __UpperCamelCase	:	Dict		    =  self.sp_model.serialized_model_proto()
                 fi.write(_lowerCamelCase    )

         return (str(_lowerCamelCase    ), str(_lowerCamelCase    ))







def  _UpperCamelCase (     _a	:	str				,     _a	:	Dict[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  sentencepiece.SentencePieceProcessor(**_a			)
    spm.Load(str(_a			)			)
    return spm







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'r'			) as f:
        return json.load(_a			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'w'			) as f:
        json.dump(_a				,     _a				,     indent=2			)



