
"""simple docstring"""






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_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase="attention"	):
 UpperCamelCase_     :					Any   	=params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
 UpperCamelCase_     :					List[str]   	=params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
 UpperCamelCase_     :					int   	=params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
 UpperCamelCase_     :					Dict   	=params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
 return k, o, q, v




def       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase=False	):
 if split_mlp_wi:
  UpperCamelCase_     :					Tuple   	=params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
  UpperCamelCase_     :					int   	=params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
  UpperCamelCase_     :					Tuple   	=(wi_a, wi_a)
 else:
  UpperCamelCase_     :					List[str]   	=params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

 UpperCamelCase_     :					List[Any]   	=params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
 return wi, wo




def       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	):
 return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']




def       A_  (				__lowercase						,	*, __lowercase						,	__lowercase	):
 UpperCamelCase_     :					Optional[Any]   	=traverse_util.flatten_dict(variables['target']	)
 UpperCamelCase_     :					Dict   	={'/'.join(__lowercase	): v for k, v in old.items()}

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

 UpperCamelCase_     :					int   	=collections.OrderedDict()

 # Shared embeddings.
 UpperCamelCase_     :					Optional[int]   	=old['token_embedder/embedding']

 # Encoder.
 for i in range(__lowercase	):
  # Block i, layer 0 (Self Attention).
  UpperCamelCase_     :					str   	=tax_layer_norm_lookup(__lowercase						,	__lowercase						,	'encoder'						,	'pre_attention_layer_norm'	)
  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=tax_attention_lookup(__lowercase						,	__lowercase						,	'encoder'						,	'attention'	)
  UpperCamelCase_     :					Tuple   	=layer_norm
  UpperCamelCase_     :					int   	=k.T
  UpperCamelCase_     :					Tuple   	=o.T
  UpperCamelCase_     :					Union[str, Any]   	=q.T
  UpperCamelCase_     :					int   	=v.T

  # Block i, layer 1 (MLP).
  UpperCamelCase_     :					str   	=tax_layer_norm_lookup(__lowercase						,	__lowercase						,	'encoder'						,	'pre_mlp_layer_norm'	)
  UpperCamelCase_	,     UpperCamelCase_     :					Tuple   	=tax_mlp_lookup(__lowercase						,	__lowercase						,	'encoder'						,	__lowercase	)
  UpperCamelCase_     :					Optional[Any]   	=layer_norm
  if split_mlp_wi:
   UpperCamelCase_     :					int   	=wi[0].T
   UpperCamelCase_     :					str   	=wi[1].T
  else:
   UpperCamelCase_     :					Tuple   	=wi.T
  UpperCamelCase_     :					Optional[Any]   	=wo.T

 UpperCamelCase_     :					Any   	=old[
     'encoder/relpos_bias/rel_embedding'
 ].T
 UpperCamelCase_     :					Tuple   	=old['encoder/encoder_norm/scale']

 if not is_encoder_only:
  # Decoder.
  for i in range(__lowercase	):
   # Block i, layer 0 (Self Attention).
   UpperCamelCase_     :					Tuple   	=tax_layer_norm_lookup(__lowercase						,	__lowercase						,	'decoder'						,	'pre_self_attention_layer_norm'	)
   UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=tax_attention_lookup(__lowercase						,	__lowercase						,	'decoder'						,	'self_attention'	)
   UpperCamelCase_     :					Optional[int]   	=layer_norm
   UpperCamelCase_     :					Union[str, Any]   	=k.T
   UpperCamelCase_     :					List[Any]   	=o.T
   UpperCamelCase_     :					List[str]   	=q.T
   UpperCamelCase_     :					Union[str, Any]   	=v.T

   # Block i, layer 1 (Cross Attention).
   UpperCamelCase_     :					Optional[int]   	=tax_layer_norm_lookup(__lowercase						,	__lowercase						,	'decoder'						,	'pre_cross_attention_layer_norm'	)
   UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					int   	=tax_attention_lookup(__lowercase						,	__lowercase						,	'decoder'						,	'encoder_decoder_attention'	)
   UpperCamelCase_     :					Tuple   	=layer_norm
   UpperCamelCase_     :					List[str]   	=k.T
   UpperCamelCase_     :					Union[str, Any]   	=o.T
   UpperCamelCase_     :					str   	=q.T
   UpperCamelCase_     :					List[str]   	=v.T

   # Block i, layer 2 (MLP).
   UpperCamelCase_     :					Tuple   	=tax_layer_norm_lookup(__lowercase						,	__lowercase						,	'decoder'						,	'pre_mlp_layer_norm'	)
   UpperCamelCase_	,     UpperCamelCase_     :					Tuple   	=tax_mlp_lookup(__lowercase						,	__lowercase						,	'decoder'						,	__lowercase	)
   UpperCamelCase_     :					Dict   	=layer_norm
   if split_mlp_wi:
    UpperCamelCase_     :					List[str]   	=wi[0].T
    UpperCamelCase_     :					int   	=wi[1].T
   else:
    UpperCamelCase_     :					Any   	=wi.T
   UpperCamelCase_     :					int   	=wo.T

  UpperCamelCase_     :					Optional[int]   	=old['decoder/decoder_norm/scale']
  UpperCamelCase_     :					str   	=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:
   UpperCamelCase_     :					str   	=old['decoder/logits_dense/kernel'].T

 return new




def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	=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:
  UpperCamelCase_     :					List[Any]   	=state_dict['shared.weight']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   UpperCamelCase_     :					Optional[Any]   	=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.'	)
   UpperCamelCase_     :					Tuple   	=state_dict['shared.weight']

 return state_dict




def       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Dict   	=checkpoints.load_tax_checkpoint(__lowercase	)
 UpperCamelCase_     :					List[Any]   	=convert_tax_to_pytorch(__lowercase						,	num_layers=config.num_layers						,	is_encoder_only=__lowercase	)
 UpperCamelCase_     :					int   	=make_state_dict(__lowercase						,	__lowercase	)
 model.load_state_dict(__lowercase						,	strict=__lowercase	)




def       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase = False	):
 UpperCamelCase_     :					Optional[Any]   	=TaConfig.from_json_file(__lowercase	)
 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:
  UpperCamelCase_     :					Optional[Any]   	=TaEncoderModel(__lowercase	)
 else:
  UpperCamelCase_     :					str   	=TaForConditionalGeneration(__lowercase	)

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

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

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


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     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
 )
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
 )


"""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       A_  (				__lowercase						,	__lowercase	):
 return torch.atana(__lowercase						,	__lowercase	) / math.pi * 2




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=torch.sin(t * math.pi / 2	) ** 2
 UpperCamelCase_     :					Union[str, Any]   	=(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(__lowercase						,	__lowercase	)





class 				a__					(						A__       ):
     pass





class 				a__					(						nn.Module       ):



     def __init__(							self :Any      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__()

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




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=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       A_  (				__lowercase	):
 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       A_  (				__lowercase	):
 for key, value in ATTN_MAP.items():
  if name.startswith(__lowercase	) and not isinstance(__lowercase						,	__lowercase	):
   return name.replace(__lowercase						,	__lowercase	)
  elif name.startswith(__lowercase	):
   return [name.replace(__lowercase						,	__lowercase	) for v in value]
 raise ValueError(F'''Attn error with {name}'''	)




def       A_  (				__lowercase						,	__lowercase=13	):
 UpperCamelCase_     :					Optional[Any]   	=input_string

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

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

 while string.startswith('main.7.'	):
  depth += 1
  UpperCamelCase_     :					Optional[int]   	=string[7:]

 if string.startswith('main.'	):
  UpperCamelCase_     :					Union[str, Any]   	=string[5:]

 # mid block
 if string[:2].isdigit():
  UpperCamelCase_     :					List[str]   	=string[:2]
  UpperCamelCase_     :					Any   	=string[2:]
 else:
  UpperCamelCase_     :					List[Any]   	=string[0]
  UpperCamelCase_     :					Tuple   	=string[1:]

 if depth == max_depth:
  UpperCamelCase_     :					Optional[Any]   	=MID_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Optional[int]   	='mid_block'
 elif depth > 0 and int(__lowercase	) < 7:
  UpperCamelCase_     :					int   	=DOWN_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Dict   	=F'''down_blocks.{depth}'''
 elif depth > 0 and int(__lowercase	) > 7:
  UpperCamelCase_     :					Optional[Any]   	=UP_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Tuple   	=F'''up_blocks.{max_depth - depth - 1}'''
 elif depth == 0:
  UpperCamelCase_     :					Optional[int]   	=DEPTH_0_TO_LAYER[layer_num]
  UpperCamelCase_     :					List[str]   	=F'''up_blocks.{max_depth - 1}''' if int(__lowercase	) > 3 else 'down_blocks.0'

 if not string_left.startswith('.'	):
  raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''	)

 UpperCamelCase_     :					Union[str, Any]   	=string_left[1:]

 if "resnets" in new_layer:
  UpperCamelCase_     :					Dict   	=convert_resconv_naming(__lowercase	)
 elif "attentions" in new_layer:
  UpperCamelCase_     :					List[Any]   	=convert_attn_naming(__lowercase	)
  UpperCamelCase_     :					str   	=new_string_left

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




def       A_  (				__lowercase	):
 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_     :					Optional[Any]   	=rename(__lowercase	)

  # check if we need to transform from Conv => Linear for attention
  if isinstance(__lowercase						,	__lowercase	):
   UpperCamelCase_     :					Union[str, Any]   	=transform_conv_attns(__lowercase						,	__lowercase						,	__lowercase	)
  else:
   UpperCamelCase_     :					Union[str, Any]   	=v

 return new_state_dict




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) == 1:
  if len(v.shape	) == 3:
   # weight
   UpperCamelCase_     :					Tuple   	=v[:, :, 0]
  else:
   # bias
   UpperCamelCase_     :					Any   	=v
 else:
  # qkv matrices
  UpperCamelCase_     :					Tuple   	=v.shape[0]
  UpperCamelCase_     :					Optional[Any]   	=trippled_shape // 3
  for i in range(3	):
   if len(v.shape	) == 3:
    UpperCamelCase_     :					str   	=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       A_  (				__lowercase	):
 UpperCamelCase_     :					int   	=torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

 UpperCamelCase_     :					Dict   	=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_     :					Optional[Any]   	=download(__lowercase	)

 UpperCamelCase_     :					str   	=MODELS_MAP[model_name]['sample_rate']
 UpperCamelCase_     :					Any   	=MODELS_MAP[model_name]['sample_size']

 UpperCamelCase_     :					str   	=Object()
 UpperCamelCase_     :					List[str]   	=sample_size
 UpperCamelCase_     :					Optional[int]   	=sample_rate
 UpperCamelCase_     :					Tuple   	=0

 UpperCamelCase_     :					Optional[int]   	=UNetaDModel(sample_size=__lowercase						,	sample_rate=__lowercase	)
 UpperCamelCase_     :					Union[str, Any]   	=diffusers_model.state_dict()

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

 UpperCamelCase_     :					Any   	=set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
 UpperCamelCase_     :					Tuple   	=set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

 assert len(__lowercase	) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith('kernel'	) for k in list(__lowercase	)	), 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[str]   	=value.squeeze()

  UpperCamelCase_     :					str   	=value

 diffusers_model.load_state_dict(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=1_00
 UpperCamelCase_     :					List[str]   	=33

 UpperCamelCase_     :					Any   	=IPNDMScheduler(num_train_timesteps=__lowercase	)

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

 UpperCamelCase_     :					str   	=torch.linspace(1						,	0						,	steps + 1						,	device=__lowercase	)[:-1]
 UpperCamelCase_     :					Dict   	=get_crash_schedule(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=DanceDiffusionPipeline(unet=__lowercase						,	scheduler=__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=torch.manual_seed(33	)
 UpperCamelCase_     :					Optional[Any]   	=pipe(num_inference_steps=__lowercase						,	generator=__lowercase	).audios

 UpperCamelCase_     :					Dict   	=sampling.iplms_sample(__lowercase						,	__lowercase						,	__lowercase						,	{}	)
 UpperCamelCase_     :					List[Any]   	=generated.clamp(-1						,	1	)

 UpperCamelCase_     :					Optional[int]   	=(generated - audio).abs().sum()
 UpperCamelCase_     :					Union[str, Any]   	=(generated - audio).abs().max()

 if args.save:
  pipe.save_pretrained(args.checkpoint_path	)

 print('Diff sum'						,	__lowercase	)
 print('Diff max'						,	__lowercase	)

 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)
