
"""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


SCREAMING_SNAKE_CASE			  =  {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 131_072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       return torch.atana(__A		,   __A					) / math.pi * 2



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		torch.sin(t * math.pi / 2					) ** 2
       UpperCamelCase       			=		(1 - sigma**2) ** 0.5
       return alpha_sigma_to_t(__A		,   __A					)



class 	__a	(   _lowerCAmelCase						):
       pass





class 	__a	(   nn.Module						):






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				Optional[Any]      )->	Dict:







              """simple docstring"""
              super().__init__()

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





def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		MODELS_MAP[model_name]["url"]
       os.system(F"wget {url} ./"					)

       return F"./{model_name}.ckpt"


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

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

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



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				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 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







       """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 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=13					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		input_string

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

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

       while string.startswith("main.7."					):
              depth += 1
              UpperCamelCase       			=		string[7:]

       if string.startswith("main."					):
              UpperCamelCase       			=		string[5:]

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

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

       if "resnets" in new_layer:
              UpperCamelCase       			=		convert_resconv_naming(__A					)
       elif "attentions" in new_layer:
              UpperCamelCase       			=		convert_attn_naming(__A					)
              UpperCamelCase       			=		new_string_left

       if not isinstance(__A		,   __A					):
              UpperCamelCase       			=		prefix + "." + new_layer + "." + string_left
       else:
              UpperCamelCase       			=		[prefix + "." + new_layer + "." + s for s in string_left]
       return new_string



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







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

              UpperCamelCase       			=		rename(__A					)

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

       return new_state_dict



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				str:







       """simple docstring"""
       if len(__A					) == 1:
              if len(v.shape					) == 3:
                     # weight
                     UpperCamelCase       			=		v[:, :, 0]
              else:
                     # bias
                     UpperCamelCase       			=		v
       else:
              # qkv matrices
              UpperCamelCase       			=		v.shape[0]
              UpperCamelCase       			=		trippled_shape // 3
              for i in range(3					):
                     if len(v.shape					) == 3:
                            UpperCamelCase       			=		v[i * single_shape : (i + 1) * single_shape, :, 0]
                     else:
                            UpperCamelCase       			=		v[i * single_shape : (i + 1) * single_shape]
       return new_state_dict



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		torch.device("cuda" if torch.cuda.is_available() else "cpu"					)

       UpperCamelCase       			=		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       			=		download(__A					)

       UpperCamelCase       			=		MODELS_MAP[model_name]["sample_rate"]
       UpperCamelCase       			=		MODELS_MAP[model_name]["sample_size"]

       UpperCamelCase       			=		Object()
       UpperCamelCase       			=		sample_size
       UpperCamelCase       			=		sample_rate
       UpperCamelCase       			=		0

       UpperCamelCase       			=		UNetaDModel(sample_size=__A		,   sample_rate=__A					)
       UpperCamelCase       			=		diffusers_model.state_dict()

       UpperCamelCase       			=		DiffusionUncond(__A					)
       orig_model.load_state_dict(torch.load(args.model_path		,   map_location=__A					)["state_dict"]					)
       UpperCamelCase       			=		orig_model.diffusion_ema.eval()
       UpperCamelCase       			=		orig_model.state_dict()
       UpperCamelCase       			=		rename_orig_weights(__A					)

       UpperCamelCase       			=		set(renamed_state_dict.keys()					) - set(diffusers_state_dict.keys()					)
       UpperCamelCase       			=		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       			=		value.squeeze()

              UpperCamelCase       			=		value

       diffusers_model.load_state_dict(__A					)

       UpperCamelCase       			=		1_00
       UpperCamelCase       			=		33

       UpperCamelCase       			=		IPNDMScheduler(num_train_timesteps=__A					)

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

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

       UpperCamelCase       			=		DanceDiffusionPipeline(unet=__A		,   scheduler=__A					)

       UpperCamelCase       			=		torch.manual_seed(33					)
       UpperCamelCase       			=		pipe(num_inference_steps=__A		,   generator=__A					).audios

       UpperCamelCase       			=		sampling.iplms_sample(__A		,   __A		,   __A		,   {}					)
       UpperCamelCase       			=		generated.clamp(-1		,   1					)

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

      main(args)








import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      __A    =params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__A			):
                  # Block i, layer 0 (Self Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''self_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__A     ,				__A     ,				__A     ,				__A			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      model.save_pretrained(__A			)

      # Verify that we can load the checkpoint.
      model.from_pretrained(__A			)
      print('''Done'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


