

'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_UpperCamelCase						:  int							       =				{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Tuple							       =				[
         "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TrOCRForCausalLM",
         "TrOCRPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
     from .processing_trocr import TrOCRProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
     import sys

     _UpperCamelCase						:  Tuple							       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''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


_UpperCamelCase						:  Union[str, Any]							       =				{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 13_1072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
}


def       snake_case   (	snake_case      : int					,		snake_case      : Tuple			) ->  Optional[int]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : str			) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							torch.sin(t * math.pi / 2			) ** 2
    lowerCAmelCase 						=							(1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(snake_case					,		snake_case			)



class 				_snake_case					(   a_			):
  pass



class 				_snake_case					(   nn.Module			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__()

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



def       snake_case   (	snake_case      : Any			) ->  List[Any]:


    """simple docstring"""


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

    return F'./{model_name}.ckpt'


_UpperCamelCase						:  Union[str, Any]							       =				{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
_UpperCamelCase						:  int							       =				{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
_UpperCamelCase						:  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",
}
_UpperCamelCase						:  Dict							       =				{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

_UpperCamelCase						:  int							       =				{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

_UpperCamelCase						:  Optional[int]							       =				{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}


def       snake_case   (	snake_case      : str			) ->  List[str]:


    """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       snake_case   (	snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : str					,		snake_case      : Union[str, Any]=13			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							input_string

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

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

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

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

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

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

    lowerCAmelCase 						=							string_left[1:]

    if "resnets" in new_layer:
        lowerCAmelCase 						=							convert_resconv_naming(snake_case			)
    elif "attentions" in new_layer:
        lowerCAmelCase 						=							convert_attn_naming(snake_case			)
        lowerCAmelCase 						=							new_string_left

    if not isinstance(snake_case					,		snake_case			):
        lowerCAmelCase 						=							prefix + '.' + new_layer + '.' + string_left
    else:
        lowerCAmelCase 						=							[prefix + '.' + new_layer + '.' + s for s in string_left]
    return new_string


def       snake_case   (	snake_case      : str			) ->  Dict:


    """simple docstring"""


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

        lowerCAmelCase 						=							rename(snake_case			)

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

    return new_state_dict


def       snake_case   (	snake_case      : int					,		snake_case      : int					,		snake_case      : Optional[int]			) ->  Any:


    """simple docstring"""


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


def       snake_case   (	snake_case      : Optional[int]			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase 						=							torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

    lowerCAmelCase 						=							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 						=							download(snake_case			)

    lowerCAmelCase 						=							MODELS_MAP[model_name]['sample_rate']
    lowerCAmelCase 						=							MODELS_MAP[model_name]['sample_size']

    lowerCAmelCase 						=							Object()
    lowerCAmelCase 						=							sample_size
    lowerCAmelCase 						=							sample_rate
    lowerCAmelCase 						=							0

    lowerCAmelCase 						=							UNetaDModel(sample_size=snake_case					,		sample_rate=snake_case			)
    lowerCAmelCase 						=							diffusers_model.state_dict()

    lowerCAmelCase 						=							DiffusionUncond(snake_case			)
    orig_model.load_state_dict(torch.load(args.model_path					,		map_location=snake_case			)['state_dict']			)
    lowerCAmelCase 						=							orig_model.diffusion_ema.eval()
    lowerCAmelCase 						=							orig_model.state_dict()
    lowerCAmelCase 						=							rename_orig_weights(snake_case			)

    lowerCAmelCase 						=							set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
    lowerCAmelCase 						=							set(diffusers_state_dict.keys()			) - set(renamed_state_dict.keys()			)

    assert len(snake_case			) == 0, F'Problem with {renamed_minus_diffusers}'
    assert all(k.endswith('kernel'			) for k in list(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":
            lowerCAmelCase 						=							value.squeeze()

        lowerCAmelCase 						=							value

    diffusers_model.load_state_dict(snake_case			)

    lowerCAmelCase 						=							100
    lowerCAmelCase 						=							33

    lowerCAmelCase 						=							IPNDMScheduler(num_train_timesteps=snake_case			)

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

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

    lowerCAmelCase 						=							DanceDiffusionPipeline(unet=snake_case					,		scheduler=snake_case			)

    lowerCAmelCase 						=							torch.manual_seed(33			)
    lowerCAmelCase 						=							pipe(num_inference_steps=snake_case					,		generator=snake_case			).audios

    lowerCAmelCase 						=							sampling.iplms_sample(snake_case					,		snake_case					,		snake_case					,		{}			)
    lowerCAmelCase 						=							generated.clamp(-1					,		1			)

    lowerCAmelCase 						=							(generated - audio).abs().sum()
    lowerCAmelCase 						=							(generated - audio).abs().max()

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

    print('Diff sum'					,		snake_case			)
    print('Diff max'					,		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__":
     _UpperCamelCase						:  Optional[int]							       =				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.")
     _UpperCamelCase						:  List[str]							       =				parser.parse_args()

     main(args)





