






'''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__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     torch.sin(t * math.pi / 2							) ** 2
   lowercase_	:    List[Any]           =     (1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(_UpperCamelCase						,		_UpperCamelCase							)


class        _UpperCAmelCase						(		snake_case			):
    pass


class        _UpperCAmelCase						(		nn.Module			):




    def __init__(			self  :       Optional[int]  ,     a  :       Any			):






       '''simple docstring'''






       super().__init__()

       lowercase_	:    Optional[Any]           =     DiffusionAttnUnetaD(a  ,     n_attn_layers=4			)
       lowercase_	:    List[Any]           =     deepcopy(self.diffusion			)
       lowercase_	:    int           =     torch.quasirandom.SobolEngine(1  ,     scramble=a			)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """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					(  _UpperCamelCase							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=13							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     input_string

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

   lowercase_	:    Optional[int]           =     0
   if string.startswith("net.3."							):
      depth += 1
      lowercase_	:    int           =     string[6:]
   elif string.startswith("net."							):
      lowercase_	:    Optional[Any]           =     string[4:]

   while string.startswith("main.7."							):
      depth += 1
      lowercase_	:    Optional[int]           =     string[7:]

   if string.startswith("main."							):
      lowercase_	:    int           =     string[5:]

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

   if depth == max_depth:
      lowercase_	:    List[str]           =     MID_NUM_TO_LAYER[layer_num]
      lowercase_	:    Any           =     "mid_block"
   elif depth > 0 and int(_UpperCamelCase							) < 7:
      lowercase_	:    Optional[int]           =     DOWN_NUM_TO_LAYER[layer_num]
      lowercase_	:    List[str]           =     F"""down_blocks.{depth}"""
   elif depth > 0 and int(_UpperCamelCase							) > 7:
      lowercase_	:    str           =     UP_NUM_TO_LAYER[layer_num]
      lowercase_	:    Any           =     F"""up_blocks.{max_depth - depth - 1}"""
   elif depth == 0:
      lowercase_	:    int           =     DEPTH_0_TO_LAYER[layer_num]
      lowercase_	:    Optional[Any]           =     F"""up_blocks.{max_depth - 1}""" if int(_UpperCamelCase							) > 3 else "down_blocks.0"

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

   lowercase_	:    Tuple           =     string_left[1:]

   if "resnets" in new_layer:
      lowercase_	:    Tuple           =     convert_resconv_naming(_UpperCamelCase							)
   elif "attentions" in new_layer:
      lowercase_	:    Union[str, Any]           =     convert_attn_naming(_UpperCamelCase							)
      lowercase_	:    Tuple           =     new_string_left

   if not isinstance(_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Optional[int]           =     prefix + "." + new_layer + "." + string_left
   else:
      lowercase_	:    Any           =     [prefix + "." + new_layer + "." + s for s in string_left]
   return new_string




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

      lowercase_	:    List[str]           =     rename(_UpperCamelCase							)

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

   return new_state_dict




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

   lowercase_	:    List[Any]           =     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()}"""
      lowercase_	:    Optional[Any]           =     download(_UpperCamelCase							)

   lowercase_	:    Any           =     MODELS_MAP[model_name]["sample_rate"]
   lowercase_	:    List[str]           =     MODELS_MAP[model_name]["sample_size"]

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

   lowercase_	:    Any           =     UNetaDModel(sample_size=_UpperCamelCase						,		sample_rate=_UpperCamelCase							)
   lowercase_	:    Any           =     diffusers_model.state_dict()

   lowercase_	:    int           =     DiffusionUncond(_UpperCamelCase							)
   orig_model.load_state_dict(torch.load(args.model_path						,		map_location=_UpperCamelCase							)["state_dict"]							)
   lowercase_	:    Dict           =     orig_model.diffusion_ema.eval()
   lowercase_	:    Dict           =     orig_model.state_dict()
   lowercase_	:    str           =     rename_orig_weights(_UpperCamelCase							)

   lowercase_	:    int           =     set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
   lowercase_	:    int           =     set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

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

      lowercase_	:    List[Any]           =     value

   diffusers_model.load_state_dict(_UpperCamelCase							)

   lowercase_	:    int           =     100
   lowercase_	:    List[Any]           =     33

   lowercase_	:    Union[str, Any]           =     IPNDMScheduler(num_train_timesteps=_UpperCamelCase							)

   lowercase_	:    List[Any]           =     torch.manual_seed(_UpperCamelCase							)
   lowercase_	:    Any           =     torch.randn([1, 2, config.sample_size]						,		generator=_UpperCamelCase							).to(_UpperCamelCase							)

   lowercase_	:    int           =     torch.linspace(1						,		0						,		steps + 1						,		device=_UpperCamelCase							)[:-1]
   lowercase_	:    int           =     get_crash_schedule(_UpperCamelCase							)

   lowercase_	:    str           =     DanceDiffusionPipeline(unet=_UpperCamelCase						,		scheduler=_UpperCamelCase							)

   lowercase_	:    Optional[Any]           =     torch.manual_seed(33							)
   lowercase_	:    List[Any]           =     pipe(num_inference_steps=_UpperCamelCase						,		generator=_UpperCamelCase							).audios

   lowercase_	:    int           =     sampling.iplms_sample(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		{}							)
   lowercase_	:    Any           =     generated.clamp(-1						,		1							)

   lowercase_	:    Tuple           =     (generated - audio).abs().sum()
   lowercase_	:    Tuple           =     (generated - audio).abs().max()

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

   print("Diff sum"						,		_UpperCamelCase							)
   print("Diff max"						,		_UpperCamelCase							)

   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'''
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image


UpperCamelCase__ =			['text', 'image', 'audio']




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     []

   for input_type in input_types:
      if input_type == "text":
         inputs.append("Text input"							)
      elif input_type == "image":
         inputs.append(
             Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / "000000039769.png"							).resize((512, 512)							)							)
      elif input_type == "audio":
         inputs.append(torch.ones(3000							)							)
      elif isinstance(_UpperCamelCase						,		_UpperCamelCase							):
         inputs.append(create_inputs(_UpperCamelCase							)							)
      else:
         raise ValueError(F"""Invalid type requested: {input_type}"""							)

   return inputs




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     []

   for output in outputs:
      if isinstance(_UpperCamelCase						,		(str, AgentText)							):
         output_types.append("text"							)
      elif isinstance(_UpperCamelCase						,		(Image.Image, AgentImage)							):
         output_types.append("image"							)
      elif isinstance(_UpperCamelCase						,		(torch.Tensor, AgentAudio)							):
         output_types.append("audio"							)
      else:
         raise ValueError(F"""Invalid output: {output}"""							)

   return output_types





@is_tool_test
class        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       self.assertTrue(hasattr(self.tool  ,     "inputs"			)			)
       self.assertTrue(hasattr(self.tool  ,     "outputs"			)			)

       lowercase_	:    Optional[Any]           =     self.tool.inputs
       for _input in inputs:
          if isinstance(_input  ,     a			):
             for __input in _input:
                self.assertTrue(__input in authorized_types			)
          else:
             self.assertTrue(_input in authorized_types			)

       lowercase_	:    Any           =     self.tool.outputs
       for _output in outputs:
          self.assertTrue(_output in authorized_types			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     create_inputs(self.tool.inputs			)
       lowercase_	:    List[str]           =     self.tool(*a			)

       # There is a single output
       if len(self.tool.outputs			) == 1:
          lowercase_	:    Union[str, Any]           =     [outputs]

       self.assertListEqual(output_types(a			)  ,     self.tool.outputs			)




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       self.assertTrue(hasattr(self.tool  ,     "description"			)			)
       self.assertTrue(hasattr(self.tool  ,     "default_checkpoint"			)			)
       self.assertTrue(self.tool.description.startswith("This is a tool that"			)			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     create_inputs(self.tool.inputs			)
       lowercase_	:    str           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [outputs]

       self.assertEqual(len(a			)  ,     len(self.tool.outputs			)			)

       for output, output_type in zip(a  ,     self.tool.outputs			):
          lowercase_	:    int           =     AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(a  ,     a			)			)





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     create_inputs(self.tool.inputs			)

       lowercase_	:    Optional[int]           =     []

       for _input, input_type in zip(a  ,     self.tool.inputs			):
          if isinstance(a  ,     a			):
             _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input			) for _input_type in input_type]			)
          else:
             _inputs.append(AGENT_TYPE_MAPPING[input_type](_input			)			)

        # Should not raise an error
       lowercase_	:    Any           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    Any           =     [outputs]

       self.assertEqual(len(a			)  ,     len(self.tool.outputs			)			)




