





"""simple docstring"""





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Optional[Any]:





  """simple docstring"""


  return 1.0 / (1.0 + np.exp(-_outputs     ))
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :str											=				np.max(_outputs	,  axis=-1	,  keepdims=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :int											=				np.exp(_outputs - maxes     )
  return shifted_exp / shifted_exp.sum(axis=-1	,  keepdims=_SCREAMING_SNAKE_CASE     )






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Optional[Any]						    =   """sigmoid"""
       __magic_name__	:Tuple						    =   """softmax"""
       __magic_name__	:str						    =   """none"""






@add_end_docstrings(
    a    ,  r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """    ,  )
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:int						    =   False
       __magic_name__	:List[str]						    =   ClassificationFunction.NONE






       def __init__(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == 'tf'
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )






       def 	snake_case		(       self       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=""       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				tokenizer_kwargs

         lowerCAmelCase__     :str											=				{}
         if hasattr(self.model.config       ,					'return_all_scores' ) and return_all_scores is None:
           lowerCAmelCase__     :Dict											=				self.model.config.return_all_scores

         if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) or top_k is None:
           lowerCAmelCase__     :Optional[Any]											=				top_k
           lowerCAmelCase__     :Optional[Any]											=				False
         elif return_all_scores is not None:
           warnings.warn(
               '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
               ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'       ,					__UpperCAmelCase       ,					)
           if return_all_scores:
             lowerCAmelCase__     :Any											=				None
           else:
             lowerCAmelCase__     :Any											=				1

         if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Tuple											=				ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
           lowerCAmelCase__     :List[Any]											=				function_to_apply
         return preprocess_params, {}, postprocess_params






       def __call__(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				super().__call__(*__UpperCAmelCase       ,					**__UpperCAmelCase )
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         lowerCAmelCase__     :List[str]											=				'top_k' not in kwargs
         if isinstance(args[0]       ,					__UpperCAmelCase ) and _legacy:
           # This pipeline is odd, and return a list when single item is run
           return [result]
         else:
           return result






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.framework
         if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ):
           return self.tokenizer(**__UpperCAmelCase       ,					return_tensors=__UpperCAmelCase       ,					**__UpperCAmelCase )
         elif isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) and len(__UpperCAmelCase ) == 1 and isinstance(inputs[0]       ,					__UpperCAmelCase ) and len(inputs[0] ) == 2:
           # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
           return self.tokenizer(
               text=inputs[0][0]       ,					text_pair=inputs[0][1]       ,					return_tensors=__UpperCAmelCase       ,					**__UpperCAmelCase )
         elif isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ):
           # This is likely an invalid usage of the pipeline attempting to pass text pairs.
           raise ValueError(
               'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
               ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.' )
         return self.tokenizer(__UpperCAmelCase       ,					return_tensors=__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.model(**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=True ):



         '''simple docstring'''

         if function_to_apply is None:
           if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
             lowerCAmelCase__     :Optional[Any]											=				ClassificationFunction.SIGMOID
           elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
             lowerCAmelCase__     :List[Any]											=				ClassificationFunction.SOFTMAX
           elif hasattr(self.model.config       ,					'function_to_apply' ) and function_to_apply is None:
             lowerCAmelCase__     :List[Any]											=				self.model.config.function_to_apply
           else:
             lowerCAmelCase__     :List[Any]											=				ClassificationFunction.NONE

         lowerCAmelCase__     :List[str]											=				model_outputs['logits'][0]
         lowerCAmelCase__     :str											=				outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
           lowerCAmelCase__     :List[Any]											=				sigmoid(__UpperCAmelCase )
         elif function_to_apply == ClassificationFunction.SOFTMAX:
           lowerCAmelCase__     :List[str]											=				softmax(__UpperCAmelCase )
         elif function_to_apply == ClassificationFunction.NONE:
           lowerCAmelCase__     :Optional[Any]											=				outputs
         else:
           raise ValueError(F"Unrecognized `function_to_apply` argument: {function_to_apply}" )

         if top_k == 1 and _legacy:
           return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         lowerCAmelCase__     :int											=				[
             {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(__UpperCAmelCase )
         ]
         if not _legacy:
           dict_scores.sort(key=lambda __UpperCAmelCase : x["score"]       ,					reverse=__UpperCAmelCase )
           if top_k is not None:
             lowerCAmelCase__     :Any											=				dict_scores[:top_k]
         return dict_scores








"""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


_lowerCAmelCase	:	Tuple				=      {
    """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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : List[str]				)->		Optional[int]:






     '''simple docstring'''



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


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






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							torch.sin(t * math.pi / 2				) ** 2
     UpperCAmelCase__						:    Optional[int]							=							(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(snake_case			,					snake_case				)




class 			lowerCAmelCase__			(    __magic_name__   ):
   pass




class 			lowerCAmelCase__			(    nn.Module   ):
   def __init__(		self      :	Any							,			snake_case__      :	str							):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Any							=							DiffusionAttnUnetaD(snake_case__							,			n_attn_layers=4							)
        UpperCAmelCase__						:    Any							=							deepcopy(self.diffusion							)
        UpperCAmelCase__						:    Optional[Any]							=							torch.quasirandom.SobolEngine(1							,			scramble=snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		str:






     '''simple docstring'''



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

     return f'./{model_name}.ckpt'


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

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

_lowerCAmelCase	:	List[Any]				=      {
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		Dict:






     '''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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		int:






     '''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 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Optional[Any]=13				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    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__						:    Dict							=							string[6:]
     elif string.startswith("net."				):
          UpperCAmelCase__						:    Tuple							=							string[4:]

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

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

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

     if depth == max_depth:
          UpperCAmelCase__						:    Any							=							MID_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							"mid_block"
     elif depth > 0 and int(snake_case				) < 7:
          UpperCAmelCase__						:    Optional[Any]							=							DOWN_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							f'down_blocks.{depth}'
     elif depth > 0 and int(snake_case				) > 7:
          UpperCAmelCase__						:    Optional[int]							=							UP_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Tuple							=							f'up_blocks.{max_depth - depth - 1}'
     elif depth == 0:
          UpperCAmelCase__						:    Tuple							=							DEPTH_0_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Optional[Any]							=							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}.'				)

     UpperCAmelCase__						:    List[Any]							=							string_left[1:]

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

     if not isinstance(snake_case			,					snake_case				):
          UpperCAmelCase__						:    Tuple							=							prefix + "." + new_layer + "." + string_left
     else:
          UpperCAmelCase__						:    str							=							[prefix + "." + new_layer + "." + s for s in string_left]
     return new_string


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






     '''simple docstring'''



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

          UpperCAmelCase__						:    Optional[int]							=							rename(snake_case				)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(snake_case			,					snake_case				):
               UpperCAmelCase__						:    Optional[Any]							=							transform_conv_attns(snake_case			,					snake_case			,					snake_case				)
          else:
               UpperCAmelCase__						:    List[Any]							=							v

     return new_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : List[Any]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

     UpperCAmelCase__						:    List[str]							=							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[int]							=							download(snake_case				)

     UpperCAmelCase__						:    List[str]							=							MODELS_MAP[model_name]["sample_rate"]
     UpperCAmelCase__						:    Dict							=							MODELS_MAP[model_name]["sample_size"]

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

     UpperCAmelCase__						:    int							=							UNetaDModel(sample_size=snake_case			,					sample_rate=snake_case				)
     UpperCAmelCase__						:    int							=							diffusers_model.state_dict()

     UpperCAmelCase__						:    Optional[Any]							=							DiffusionUncond(snake_case				)
     orig_model.load_state_dict(torch.load(args.model_path			,					map_location=snake_case				)["state_dict"]				)
     UpperCAmelCase__						:    List[str]							=							orig_model.diffusion_ema.eval()
     UpperCAmelCase__						:    Optional[int]							=							orig_model.state_dict()
     UpperCAmelCase__						:    Any							=							rename_orig_weights(snake_case				)

     UpperCAmelCase__						:    List[Any]							=							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(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":
               UpperCAmelCase__						:    Union[str, Any]							=							value.squeeze()

          UpperCAmelCase__						:    List[str]							=							value

     diffusers_model.load_state_dict(snake_case				)

     UpperCAmelCase__						:    Any							=							100
     UpperCAmelCase__						:    str							=							33

     UpperCAmelCase__						:    Optional[int]							=							IPNDMScheduler(num_train_timesteps=snake_case				)

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

     UpperCAmelCase__						:    Dict							=							torch.linspace(1			,					0			,					steps + 1			,					device=snake_case				)[:-1]
     UpperCAmelCase__						:    List[str]							=							get_crash_schedule(snake_case				)

     UpperCAmelCase__						:    List[str]							=							DanceDiffusionPipeline(unet=snake_case			,					scheduler=snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							torch.manual_seed(33				)
     UpperCAmelCase__						:    int							=							pipe(num_inference_steps=snake_case			,					generator=snake_case				).audios

     UpperCAmelCase__						:    Optional[Any]							=							sampling.iplms_sample(snake_case			,					snake_case			,					snake_case			,					{}				)
     UpperCAmelCase__						:    Tuple							=							generated.clamp(-1			,					1				)

     UpperCAmelCase__						:    Optional[int]							=							(generated - audio).abs().sum()
     UpperCAmelCase__						:    List[Any]							=							(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__":
 _lowerCAmelCase	:	Dict				=      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.""")
 _lowerCAmelCase	:	Tuple				=      parser.parse_args()

 main(args)


