


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


__snake_case		:str   	=  {
    '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			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return torch.atana(A_     ,				A_       ) / math.pi * 2





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       torch.sin(t * math.pi / 2       ) ** 2
    __a		 =       (1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(A_     ,				A_       )

class 					_A (			_A					):
       pass


class 					_A (			nn.Module					):



       def __init__(	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           super().__init__()

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





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       MODELS_MAP[model_name]['''url''']
    os.system(f'wget {url} ./'       )

    return f'./{model_name}.ckpt'


__snake_case		:Optional[int]   	=  {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__snake_case		:Optional[Any]   	=  {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__snake_case		: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',
}
__snake_case		:int   	=  {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

__snake_case		:List[str]   	=  {
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

__snake_case		:int   	=  {
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}





def   __snake_case			(    _UpperCAmelCase       ):





    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			(    _UpperCAmelCase       ):





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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=13       ):
    __a		 =       input_string

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

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

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

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

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

    if depth == max_depth:
        __a		 =       MID_NUM_TO_LAYER[layer_num]
        __a		 =       '''mid_block'''
    elif depth > 0 and int(A_       ) < 7:
        __a		 =       DOWN_NUM_TO_LAYER[layer_num]
        __a		 =       f'down_blocks.{depth}'
    elif depth > 0 and int(A_       ) > 7:
        __a		 =       UP_NUM_TO_LAYER[layer_num]
        __a		 =       f'up_blocks.{max_depth - depth - 1}'
    elif depth == 0:
        __a		 =       DEPTH_0_TO_LAYER[layer_num]
        __a		 =       f'up_blocks.{max_depth - 1}' if int(A_       ) > 3 else '''down_blocks.0'''

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

    __a		 =       string_left[1:]

    if "resnets" in new_layer:
        __a		 =       convert_resconv_naming(A_       )
    elif "attentions" in new_layer:
        __a		 =       convert_attn_naming(A_       )
        __a		 =       new_string_left

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





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

        __a		 =       rename(A_       )

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

    return new_state_dict





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):





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





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )

    __a		 =       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		 =       download(A_       )

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

    __a		 =       Object()
    __a		 =       sample_size
    __a		 =       sample_rate
    __a		 =       0

    __a		 =       UNetaDModel(sample_size=A_     ,				sample_rate=A_       )
    __a		 =       diffusers_model.state_dict()

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

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

    assert len(A_       ) == 0, f'Problem with {renamed_minus_diffusers}'
    assert all(k.endswith('''kernel'''       ) for k in list(A_       )       ), 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		 =       value.squeeze()

        __a		 =       value

    diffusers_model.load_state_dict(A_       )

    __a		 =       100
    __a		 =       33

    __a		 =       IPNDMScheduler(num_train_timesteps=A_       )

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

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

    __a		 =       DanceDiffusionPipeline(unet=A_     ,				scheduler=A_       )

    __a		 =       torch.manual_seed(33       )
    __a		 =       pipe(num_inference_steps=A_     ,				generator=A_       ).audios

    __a		 =       sampling.iplms_sample(A_     ,				A_     ,				A_     ,				{}       )
    __a		 =       generated.clamp(-1     ,				1       )

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

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

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

    assert diff_max < 1E-3, f'Diff max: {diff_max} is too much :-/'

    print(f'Conversion for {model_name} successful!'       )


if __name__ == "__main__":
 __snake_case		: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.''')
 __snake_case		:Union[str, Any]   	=  parser.parse_args()

 main(args)










from __future__ import annotations





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    print(f'Vertex\tShortest Distance from vertex {src}'       )
    for i, d in enumerate(_UpperCAmelCase       ):
        print(f'{i}\t\t{d}'       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for j in range(_UpperCAmelCase       ):
        __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
            return True
    return False





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [float('''inf'''       )] * vertex_count
    __a		 =       0.0

    for _ in range(vertex_count - 1       ):
        for j in range(_UpperCAmelCase       ):
            __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                __a		 =       distance[u] + w

    __a		 =       check_negative_cycle(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''       )

    return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __snake_case		:Dict   	=  int(input('''Enter number of vertices: ''').strip())
 __snake_case		:Any   	=  int(input('''Enter number of edges: ''').strip())

 __snake_case		:list[dict[str, int]]   	=  [{} for _ in range(E)]

 for i in range(E):
  print('''Edge ''', i + 1)
  __snake_case	,__snake_case	,__snake_case		:int   	=  (
      int(x)
      for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
  )
  __snake_case		:Any   	=  {'''src''': src, '''dst''': dest, '''weight''': weight}

 __snake_case		:List[str]   	=  int(input('''\nEnter shortest path source:''').strip())
 __snake_case		:Optional[Any]   	=  bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)





