





"""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


a_			=      {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 65536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 65536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 131072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
}







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



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Optional[int]						)       ->Any:



    '''simple docstring'''







    __A   :						Any									=					torch.sin(t * math.pi / 2						) ** 2
    __A   :						Optional[Any]									=					(1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(snake_case_    ,snake_case_						)






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 pass






class 		__snake_case (		nn.Module					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__()

     __A   :						str									=					DiffusionAttnUnetaD(__lowerCamelCase ,						n_attn_layers=4  )
     __A   :						List[str]									=					deepcopy(self.diffusion  )
     __A   :						List[Any]									=					torch.quasirandom.SobolEngine(1 ,						scramble=__lowerCamelCase  )







def 		__lowercase					(       snake_case_  :  Optional[int]						)       ->str:



    '''simple docstring'''







    __A   :						Dict									=					MODELS_MAP[model_name]['''url''']
    os.system(F"""wget {url} ./"""						)

    return F"""./{model_name}.ckpt"""


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

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

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







def 		__lowercase					(       snake_case_  :  List[Any]						)       ->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 		__lowercase					(       snake_case_  :  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 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  int=13						)       ->List[Any]:



    '''simple docstring'''







    __A   :						Dict									=					input_string

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

    __A   :						int									=					0
    if string.startswith('''net.3.'''						):
        depth += 1
        __A   :						Union[str, Any]									=					string[6:]
    elif string.startswith('''net.'''						):
        __A   :						Any									=					string[4:]

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

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

    # mid block
    if string[:2].isdigit():
        __A   :						Optional[Any]									=					string[:2]
        __A   :						Dict									=					string[2:]
    else:
        __A   :						Tuple									=					string[0]
        __A   :						Any									=					string[1:]

    if depth == max_depth:
        __A   :						List[str]									=					MID_NUM_TO_LAYER[layer_num]
        __A   :						str									=					'''mid_block'''
    elif depth > 0 and int(snake_case_						) < 7:
        __A   :						Dict									=					DOWN_NUM_TO_LAYER[layer_num]
        __A   :						Any									=					F"""down_blocks.{depth}"""
    elif depth > 0 and int(snake_case_						) > 7:
        __A   :						int									=					UP_NUM_TO_LAYER[layer_num]
        __A   :						int									=					F"""up_blocks.{max_depth - depth - 1}"""
    elif depth == 0:
        __A   :						Dict									=					DEPTH_0_TO_LAYER[layer_num]
        __A   :						Union[str, 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}."""						)

    __A   :						Tuple									=					string_left[1:]

    if "resnets" in new_layer:
        __A   :						int									=					convert_resconv_naming(snake_case_						)
    elif "attentions" in new_layer:
        __A   :						List[str]									=					convert_attn_naming(snake_case_						)
        __A   :						int									=					new_string_left

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







def 		__lowercase					(       snake_case_  :  Union[str, Any]						)       ->str:



    '''simple docstring'''







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

        __A   :						Dict									=					rename(snake_case_						)

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

    return new_state_dict







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  Tuple    ,snake_case_  :  Union[str, Any]						)       ->Optional[Any]:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Optional[int]						)       ->Optional[int]:



    '''simple docstring'''







    __A   :						Optional[int]									=					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''						)

    __A   :						int									=					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()}"""
        __A   :						str									=					download(snake_case_						)

    __A   :						int									=					MODELS_MAP[model_name]['''sample_rate''']
    __A   :						str									=					MODELS_MAP[model_name]['''sample_size''']

    __A   :						List[Any]									=					Object()
    __A   :						List[str]									=					sample_size
    __A   :						Tuple									=					sample_rate
    __A   :						List[str]									=					0

    __A   :						Optional[int]									=					UNetaDModel(sample_size=snake_case_    ,sample_rate=snake_case_						)
    __A   :						Optional[Any]									=					diffusers_model.state_dict()

    __A   :						Any									=					DiffusionUncond(snake_case_						)
    orig_model.load_state_dict(torch.load(args.model_path    ,map_location=snake_case_						)['''state_dict''']						)
    __A   :						List[str]									=					orig_model.diffusion_ema.eval()
    __A   :						Dict									=					orig_model.state_dict()
    __A   :						str									=					rename_orig_weights(snake_case_						)

    __A   :						Optional[Any]									=					set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
    __A   :						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":
            __A   :						str									=					value.squeeze()

        __A   :						Tuple									=					value

    diffusers_model.load_state_dict(snake_case_						)

    __A   :						Tuple									=					100
    __A   :						str									=					33

    __A   :						Any									=					IPNDMScheduler(num_train_timesteps=snake_case_						)

    __A   :						List[str]									=					torch.manual_seed(snake_case_						)
    __A   :						int									=					torch.randn([1, 2, config.sample_size]    ,generator=snake_case_						).to(snake_case_						)

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

    __A   :						Dict									=					DanceDiffusionPipeline(unet=snake_case_    ,scheduler=snake_case_						)

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

    __A   :						Optional[int]									=					sampling.iplms_sample(snake_case_    ,snake_case_    ,snake_case_    ,{}						)
    __A   :						Optional[Any]									=					generated.clamp(-1    ,1						)

    __A   :						List[str]									=					(generated - audio).abs().sum()
    __A   :						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__":
       a_			=      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.""")
       a_			=      parser.parse_args()

       main(args)











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_			=      logging.get_logger(__name__)

a_			=      {
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""dinat"""

 _lowerCamelCase				=					{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(   self ,						__lowerCamelCase=4 ,						__lowerCamelCase=3 ,						__lowerCamelCase=64 ,						__lowerCamelCase=[3, 4, 6, 5] ,						__lowerCamelCase=[2, 4, 8, 16] ,						__lowerCamelCase=7 ,						__lowerCamelCase=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,						__lowerCamelCase=3.0 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     __A   :						Dict									=					patch_size
     __A   :						Union[str, Any]									=					num_channels
     __A   :						str									=					embed_dim
     __A   :						Optional[Any]									=					depths
     __A   :						int									=					len(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					num_heads
     __A   :						Tuple									=					kernel_size
     __A   :						Optional[int]									=					dilations
     __A   :						Tuple									=					mlp_ratio
     __A   :						Optional[int]									=					qkv_bias
     __A   :						int									=					hidden_dropout_prob
     __A   :						Dict									=					attention_probs_dropout_prob
     __A   :						int									=					drop_path_rate
     __A   :						Dict									=					hidden_act
     __A   :						Any									=					layer_norm_eps
     __A   :						Tuple									=					initializer_range
     # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     __A   :						str									=					int(embed_dim * 2 ** (len(__lowerCamelCase  ) - 1)  )
     __A   :						List[Any]									=					layer_scale_init_value
     __A   :						Any									=					['''stem'''] + [F"""stage{idx}""" for idx in range(1 ,						len(__lowerCamelCase  ) + 1  )]
     __A			,	__A   :						Union[str, Any]									=					get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase ,						out_indices=__lowerCamelCase ,						stage_names=self.stage_names  )




