

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					:  Optional[Any]             =      {
    '''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     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     return torch.atana(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE    ) / math.pi * 2
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     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 	UpperCAmelCase__		(	__UpperCamelCase	):






  """simple docstring"""


  pass






class 	UpperCAmelCase__		(	nn.Module	):






  """simple docstring"""


  def __init__(							self						:		int     ,       __lowerCamelCase						:		Optional[int] )		->							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     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   MODELS_MAP[model_name]["""url"""]
     os.system(F'''wget {url} ./'''    )

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


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

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

_SCREAMING_SNAKE_CASE					:  str             =      {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}
def     UpperCAmelCase_       (						_A    ):





     '''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    ):





     '''simple docstring'''




     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     UpperCAmelCase_       (						_A				,		_A=13    ):





     '''simple docstring'''




     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     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     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     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     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     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     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__":
       _SCREAMING_SNAKE_CASE					:  List[str]             =      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)



import warnings

from .generation import TFGenerationMixin






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  warnings.warn(
      "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
      "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."						,					A__						,					)


