'''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_     :						Optional[Any]    							={
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 13_10_72,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
}



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Dict)			->					List[Any]:
   return torch.atana(__snake_case	,   __snake_case) / math.pi * 2



def snake_case_      (					__snake_case  :							Optional[int])			->					Dict:
   lowerCAmelCase_			=      torch.sin(t * math.pi / 2) ** 2
   lowerCAmelCase_			=      (1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(__snake_case	,   __snake_case)


class     __UpperCAmelCase       ( __a		):
  pass






class     __UpperCAmelCase       ( nn.Module		):





  def __init__(	self    ,     _lowerCamelCase ):
     super().__init__()

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






def snake_case_      (					__snake_case  :							Dict)			->					str:
   lowerCAmelCase_			=      MODELS_MAP[model_name]['''url''']
   os.system(F'''wget {url} ./''')

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


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

A_     :						int    							={
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

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



def snake_case_      (					__snake_case  :							str)			->					str:
   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_      (					__snake_case  :							str)			->					Union[str, Any]:
   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 snake_case_      (					__snake_case  :							Optional[Any]	,   __snake_case  :							Dict=13)			->					Optional[Any]:
   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 snake_case_      (					__snake_case  :							List[str])			->					List[Any]:
   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 snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							str	,   __snake_case  :							Dict)			->					Optional[Any]:
   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 snake_case_      (					__snake_case  :							Union[str, Any])			->					Optional[Any]:
   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__":
 A_     :						List[Any]    							=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_     :						Union[str, Any]    							=parser.parse_args()

 main(args)






'''simple docstring'''

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
 from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
 from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
 from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A_     :						Optional[Any]    							=DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A_     :						Tuple    							='''main'''
# Default branch name
A_     :						Dict    							='''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
A_     :						int    							='''aaaaaaa'''
# This commit does not exist, so we should 404.

A_     :						List[str]    							='''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
A_     :						List[str]    							='''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''



@contextlib.contextmanager
def snake_case_      (					)			->					Tuple:
   print('''Welcome!''')
   yield
   print('''Bye!''')



@contextlib.contextmanager
def snake_case_      (					)			->					str:
   print('''Bonjour!''')
   yield
   print('''Au revoir!''')


class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self ):
     # If the spec is missing, importlib would not be able to import the module dynamically.
     assert transformers.__spec__ is not None
     assert importlib.util.find_spec('''transformers''' ) is not None






class     __UpperCAmelCase       ( unittest.TestCase		):





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([] ):
        print('''Transformers are awesome!''' )
     # The print statement adds a new line at the end of the output
     self.assertEqual(mock_stdout.getvalue()    ,     '''Transformers are awesome!\n''' )





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([context_en()] ):
        print('''Transformers are awesome!''' )
     # The output should be wrapped with an English welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()    ,     '''Welcome!\nTransformers are awesome!\nBye!\n''' )





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([context_fr(), context_en()] ):
        print('''Transformers are awesome!''' )
     # The output should be wrapped with an English and French welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()    ,     '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )





  @require_torch
  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels''', '''next_sentence_label'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''start_positions''', '''end_positions'''] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )





  @require_tf
  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels''', '''next_sentence_label'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''start_positions''', '''end_positions'''] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )







  @require_flax
  def 						UpperCAmelCase_	(	self ):
     # Flax models don't have labels
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )





