




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_									=	{
    """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__       (						A__       :					Any  ,			A__       :					List[str]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Union[str, Any]	):





       '''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     lowerCamelCase__(					UpperCAmelCase__):
      pass





class     lowerCamelCase__(					nn.Module):
      def __init__(				self: str    ,       UpperCamelCase_: List[str]   ):
             super().__init__()

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




def 					lowerCamelCase__       (						A__       :					int	):





       '''simple docstring'''



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

       return f'./{model_name}.ckpt'


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

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

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




def 					lowerCamelCase__       (						A__       :					Optional[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__       (						A__       :					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 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					Union[str, Any]=13	):





       '''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 					lowerCamelCase__       (						A__       :					Optional[int]	):





       '''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 					lowerCamelCase__       (						A__       :					int  ,			A__       :					int  ,			A__       :					Tuple	):





       '''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 					lowerCamelCase__       (						A__       :					Tuple	):





       '''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_									=	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_									=	parser.parse_args()

       main(args)







"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					process
							lowerCamelCase			      =					params





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.dataset[i]
							lowerCamelCase			      =					self.process(_a		, **self.params )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a=None ):





							"""simple docstring"""
							lowerCamelCase			      =					loader
							lowerCamelCase			      =					infer
							lowerCamelCase			      =					params
							if loader_batch_size == 1:
										# Let's spare some time by deactivating altogether
										lowerCamelCase			      =					None
							lowerCamelCase			      =					loader_batch_size

							# Internal bookkeeping
							lowerCamelCase			      =					None
							lowerCamelCase			      =					None





				def __len__(       self ):





							"""simple docstring"""
							return len(self.loader )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if isinstance(self._loader_batch_data		, torch.Tensor ):
										# Batch data is simple tensor, just fetch the slice
										lowerCamelCase			      =					self._loader_batch_data[self._loader_batch_index]
							else:
										# Batch data is assumed to be BaseModelOutput (or dict)
										lowerCamelCase			      =					{}
										for k, element in self._loader_batch_data.items():
													if isinstance(_a		, _a ):
																# Convert ModelOutput to tuple first
																lowerCamelCase			      =					element.to_tuple()
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					tuple(np.expand_dims(el[self._loader_batch_index]		, 0 ) for el in element )
																continue
													if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_a		, _a ):
																# Those are stored as lists of tensors so need specific unbatching.
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					tuple(np.expand_dims(el[self._loader_batch_index]		, 0 ) for el in element )
																continue
													if element is None:
																# This can happen for optional data that get passed around
																lowerCamelCase			      =					None
													elif isinstance(element[self._loader_batch_index]		, torch.Tensor ):
																# Take correct batch data, but make it looked like batch_size=1
																# For compatibility with other methods within transformers

																lowerCamelCase			      =					element[self._loader_batch_index].unsqueeze(0 )
													elif isinstance(element[self._loader_batch_index]		, np.ndarray ):
																# Take correct batch data, but make it looked like batch_size=1
																# For compatibility with other methods within transformers
																lowerCamelCase			      =					np.expand_dims(element[self._loader_batch_index]		, 0 )
													else:
																# This is typically a list, so no need to `unsqueeze`.
																lowerCamelCase			      =					element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
										lowerCamelCase			      =					self._loader_batch_data.__class__(_a )
							self._loader_batch_index += 1
							return result





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
										# We are currently unrolling a batch so we just need to return
										# the current item within a batch
										return self.loader_batch_item()

							# We're out of items within a batch
							lowerCamelCase			      =					next(self.iterator )
							lowerCamelCase			      =					self.infer(_a		, **self.params )
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
										# Try to infer the size of the batch
										if isinstance(_a		, torch.Tensor ):
													lowerCamelCase			      =					processed
										else:
													lowerCamelCase			      =					list(processed.keys() )[0]
													lowerCamelCase			      =					processed[key]
										if isinstance(_a		, _a ):
													lowerCamelCase			      =					len(_a )
										else:
													lowerCamelCase			      =					first_tensor.shape[0]
										if 0 < observed_batch_size < self.loader_batch_size:
													# could be last batch so we can't unroll as many
													# elements.
													lowerCamelCase			      =					observed_batch_size
										# Setting internal index to unwrap the batch
										lowerCamelCase			      =					processed
										lowerCamelCase			      =					0
										return self.loader_batch_item()
							else:
										# We're not unrolling batches
										return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a=None ):





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





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							lowerCamelCase			      =					None
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self.subiterator is None:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
							try:
										# Try to return next item
										lowerCamelCase			      =					next(self.subiterator )
							except StopIteration:
										# When a preprocess iterator ends, we can start lookig at the next item
										# ChunkIterator will keep feeding until ALL elements of iterator
										# all have created their subiterator and have been iterating against.
										#
										# Another way to look at it, is we're basically flattening lists of lists
										# into a single list, but with generators
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										lowerCamelCase			      =					next(self.subiterator )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							lowerCamelCase			      =					False
							lowerCamelCase			      =					[]
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
										while self._loader_batch_index < self.loader_batch_size:
													lowerCamelCase			      =					self.loader_batch_item()
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
													if is_last:
																return accumulator

							while not is_last:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										if self.loader_batch_size is not None:
													if isinstance(_a		, torch.Tensor ):
																lowerCamelCase			      =					processed
													else:
																lowerCamelCase			      =					list(processed.keys() )[0]
																lowerCamelCase			      =					processed[key]
													if isinstance(_a		, _a ):
																lowerCamelCase			      =					len(_a )
													else:
																lowerCamelCase			      =					first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																# could be last batch so we can't unroll as many
																# elements.
																lowerCamelCase			      =					observed_batch_size
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					0
													while self._loader_batch_index < self.loader_batch_size:
																lowerCamelCase			      =					self.loader_batch_item()
																lowerCamelCase			      =					item.pop("""is_last""" )
																accumulator.append(_a )
																if is_last:
																			return accumulator
										else:
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
							return accumulator






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					key





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return self.dataset[i][self.key]






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					keya
							lowerCamelCase			      =					keya





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



