from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCamelCase       : Any      					=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase       : Optional[Any]      					=		'''\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'''





def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				str=8	):
					SCREAMING_SNAKE_CASE           =							height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					SCREAMING_SNAKE_CASE           =							width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


class     lowercase     (  A__  ):




							def __init__(    self :       int						,				_UpperCamelCase :       UNetaDConditionModel						,				_UpperCamelCase :       DDPMScheduler						,				_UpperCamelCase :       VQModel						,				)       ->					str:



												'''simple docstring'''

												super().__init__()

												self.register_modules(
												    unet=_lowerCamelCase						,				scheduler=_lowerCamelCase						,				movq=_lowerCamelCase						,				)
												SCREAMING_SNAKE_CASE           =							2 ** (len(self.movq.config.block_out_channels   ) - 1)




							def 						__snake_case(    self :       int						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       int						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Dict   )       ->					Optional[int]:



												'''simple docstring'''

												if latents is None:
																	SCREAMING_SNAKE_CASE           =							randn_tensor(_lowerCamelCase						,				generator=_lowerCamelCase						,				device=_lowerCamelCase						,				dtype=_lowerCamelCase   )
												else:
																	if latents.shape != shape:
																						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
																	SCREAMING_SNAKE_CASE           =							latents.to(_lowerCamelCase   )

												SCREAMING_SNAKE_CASE           =							latents * scheduler.init_noise_sigma
												return latents




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       List[Any]=0   )       ->					Any:



												'''simple docstring'''

												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError("Please install accelerate via `pip install accelerate`"   )

												SCREAMING_SNAKE_CASE           =							torch.device(F"cuda:{gpu_id}"   )

												SCREAMING_SNAKE_CASE           =							[
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(_lowerCamelCase						,				_lowerCamelCase   )




							def 						__snake_case(    self :       Tuple						,				_UpperCamelCase :       Union[str, Any]=0   )       ->					Tuple:



												'''simple docstring'''

												if is_accelerate_available() and is_accelerate_version(">="						,				"0.17.0.dev0"   ):
																	from accelerate import cpu_offload_with_hook
												else:
																	raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."   )

												SCREAMING_SNAKE_CASE           =							torch.device(F"cuda:{gpu_id}"   )

												if self.device.type != "cpu":
																	self.to("cpu"						,				silence_dtype_warnings=_lowerCamelCase   )
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

												SCREAMING_SNAKE_CASE           =							None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	SCREAMING_SNAKE_CASE           =							cpu_offload_with_hook(_lowerCamelCase						,				_lowerCamelCase						,				prev_module_hook=_lowerCamelCase   )

												# We'll offload the last model manually.
												SCREAMING_SNAKE_CASE           =							hook




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 						__snake_case(    self :       Union[str, Any]   )       ->					List[str]:



												'''simple docstring'''

												if not hasattr(self.unet						,				"_hf_hook"   ):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(_lowerCamelCase						,				"_hf_hook"   )
																	    and hasattr(module._hf_hook						,				"execution_device"   )
																	    and module._hf_hook.execution_device is not None
																	):
																						return torch.device(module._hf_hook.execution_device   )
												return self.device





							@torch.no_grad()
							@replace_example_docstring(_lowerCamelCase   )
							def __call__(    self :       Union[str, Any]						,				_UpperCamelCase :       Union[torch.FloatTensor, List[torch.FloatTensor]]						,				_UpperCamelCase :       Union[torch.FloatTensor, List[torch.FloatTensor]]						,				_UpperCamelCase :       int = 512						,				_UpperCamelCase :       int = 512						,				_UpperCamelCase :       int = 100						,				_UpperCamelCase :       float = 4.0						,				_UpperCamelCase :       int = 1						,				_UpperCamelCase :       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				_UpperCamelCase :       Optional[torch.FloatTensor] = None						,				_UpperCamelCase :       Optional[str] = "pil"						,				_UpperCamelCase :       bool = True						,				)       ->					Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self._execution_device

												SCREAMING_SNAKE_CASE           =							guidance_scale > 1.0

												if isinstance(_lowerCamelCase						,				_lowerCamelCase   ):
																	SCREAMING_SNAKE_CASE           =							torch.cat(_lowerCamelCase						,				dim=0   )
												SCREAMING_SNAKE_CASE           =							image_embeds.shape[0] * num_images_per_prompt
												if isinstance(_lowerCamelCase						,				_lowerCamelCase   ):
																	SCREAMING_SNAKE_CASE           =							torch.cat(_lowerCamelCase						,				dim=0   )

												if do_classifier_free_guidance:
																	SCREAMING_SNAKE_CASE           =							image_embeds.repeat_interleave(_lowerCamelCase						,				dim=0   )
																	SCREAMING_SNAKE_CASE           =							negative_image_embeds.repeat_interleave(_lowerCamelCase						,				dim=0   )

												SCREAMING_SNAKE_CASE           =							torch.cat([negative_image_embeds, image_embeds]						,				dim=0   ).to(dtype=self.unet.dtype						,				device=_lowerCamelCase   )

												self.scheduler.set_timesteps(_lowerCamelCase						,				device=_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							self.scheduler.timesteps

												SCREAMING_SNAKE_CASE           =							self.unet.config.in_channels

												SCREAMING_SNAKE_CASE           =							downscale_height_and_width(_lowerCamelCase						,				_lowerCamelCase						,				self.movq_scale_factor   )

												# create initial latent
												SCREAMING_SNAKE_CASE           =							self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)						,				image_embeds.dtype						,				_lowerCamelCase						,				_lowerCamelCase						,				_lowerCamelCase						,				self.scheduler						,				)

												for i, t in enumerate(self.progress_bar(_lowerCamelCase   )   ):
																	# expand the latents if we are doing classifier free guidance
																	SCREAMING_SNAKE_CASE           =							torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

																	SCREAMING_SNAKE_CASE           =							{'image_embeds': image_embeds}
																	SCREAMING_SNAKE_CASE           =							self.unet(
																	    sample=_lowerCamelCase						,				timestep=_lowerCamelCase						,				encoder_hidden_states=_lowerCamelCase						,				added_cond_kwargs=_lowerCamelCase						,				return_dict=_lowerCamelCase						,				)[0]

																	if do_classifier_free_guidance:
																						SCREAMING_SNAKE_CASE           =							noise_pred.split(latents.shape[1]						,				dim=1   )
																						SCREAMING_SNAKE_CASE           =							noise_pred.chunk(2   )
																						SCREAMING_SNAKE_CASE           =							variance_pred.chunk(2   )
																						SCREAMING_SNAKE_CASE           =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						SCREAMING_SNAKE_CASE           =							torch.cat([noise_pred, variance_pred_text]						,				dim=1   )

																	if not (
																	    hasattr(self.scheduler.config						,				"variance_type"   )
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																						SCREAMING_SNAKE_CASE           =							noise_pred.split(latents.shape[1]						,				dim=1   )

																	# compute the previous noisy sample x_t -> x_t-1
																	SCREAMING_SNAKE_CASE           =							self.scheduler.step(
																	    _lowerCamelCase						,				_lowerCamelCase						,				_lowerCamelCase						,				generator=_lowerCamelCase						,				)[0]
												# post-processing
												SCREAMING_SNAKE_CASE           =							self.movq.decode(_lowerCamelCase						,				force_not_quantize=_lowerCamelCase   )['sample']

												if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"   )

												if output_type in ["np", "pil"]:
																	SCREAMING_SNAKE_CASE           =							image * 0.5 + 0.5
																	SCREAMING_SNAKE_CASE           =							image.clamp(0						,				1   )
																	SCREAMING_SNAKE_CASE           =							image.cpu().permute(0						,				2						,				3						,				1   ).float().numpy()

												if output_type == "pil":
																	SCREAMING_SNAKE_CASE           =							self.numpy_to_pil(_lowerCamelCase   )

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_lowerCamelCase   )






"""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


__SCREAMING_SNAKE_CASE							=     {
    '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       A_  (				__lowercase						,	__lowercase	):
 return torch.atana(__lowercase						,	__lowercase	) / math.pi * 2




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=torch.sin(t * math.pi / 2	) ** 2
 UpperCamelCase_     :					Union[str, Any]   	=(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(__lowercase						,	__lowercase	)





class 				a__					(						A__       ):
     pass





class 				a__					(						nn.Module       ):



     def __init__(							self :Any      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__()

      UpperCamelCase_     :					Optional[int]   	=DiffusionAttnUnetaD(_lowerCamelCase      ,  n_attn_layers=4   )
      UpperCamelCase_     :					List[str]   	=deepcopy(self.diffusion   )
      UpperCamelCase_     :					int   	=torch.quasirandom.SobolEngine(1      ,  scramble=_lowerCamelCase   )




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=MODELS_MAP[model_name]['url']
 os.system(F'''wget {url} ./'''	)

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


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

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

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




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




def       A_  (				__lowercase						,	__lowercase=13	):
 UpperCamelCase_     :					Optional[Any]   	=input_string

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

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

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

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

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

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

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

 UpperCamelCase_     :					Union[str, Any]   	=string_left[1:]

 if "resnets" in new_layer:
  UpperCamelCase_     :					Dict   	=convert_resconv_naming(__lowercase	)
 elif "attentions" in new_layer:
  UpperCamelCase_     :					List[Any]   	=convert_attn_naming(__lowercase	)
  UpperCamelCase_     :					str   	=new_string_left

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




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	={}
 for k, v in state_dict.items():
  if k.endswith('kernel'	):
   # up- and downsample layers, don't have trainable weights
   continue

  UpperCamelCase_     :					Optional[Any]   	=rename(__lowercase	)

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

 return new_state_dict




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




def       A_  (				__lowercase	):
 UpperCamelCase_     :					int   	=torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

 UpperCamelCase_     :					Dict   	=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()}'''
  UpperCamelCase_     :					Optional[Any]   	=download(__lowercase	)

 UpperCamelCase_     :					str   	=MODELS_MAP[model_name]['sample_rate']
 UpperCamelCase_     :					Any   	=MODELS_MAP[model_name]['sample_size']

 UpperCamelCase_     :					str   	=Object()
 UpperCamelCase_     :					List[str]   	=sample_size
 UpperCamelCase_     :					Optional[int]   	=sample_rate
 UpperCamelCase_     :					Tuple   	=0

 UpperCamelCase_     :					Optional[int]   	=UNetaDModel(sample_size=__lowercase						,	sample_rate=__lowercase	)
 UpperCamelCase_     :					Union[str, Any]   	=diffusers_model.state_dict()

 UpperCamelCase_     :					Any   	=DiffusionUncond(__lowercase	)
 orig_model.load_state_dict(torch.load(args.model_path						,	map_location=__lowercase	)['state_dict']	)
 UpperCamelCase_     :					List[str]   	=orig_model.diffusion_ema.eval()
 UpperCamelCase_     :					Tuple   	=orig_model.state_dict()
 UpperCamelCase_     :					Optional[int]   	=rename_orig_weights(__lowercase	)

 UpperCamelCase_     :					Any   	=set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
 UpperCamelCase_     :					Tuple   	=set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

 assert len(__lowercase	) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith('kernel'	) for k in list(__lowercase	)	), 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":
   UpperCamelCase_     :					List[str]   	=value.squeeze()

  UpperCamelCase_     :					str   	=value

 diffusers_model.load_state_dict(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=1_00
 UpperCamelCase_     :					List[str]   	=33

 UpperCamelCase_     :					Any   	=IPNDMScheduler(num_train_timesteps=__lowercase	)

 UpperCamelCase_     :					List[Any]   	=torch.manual_seed(__lowercase	)
 UpperCamelCase_     :					List[str]   	=torch.randn([1, 2, config.sample_size]						,	generator=__lowercase	).to(__lowercase	)

 UpperCamelCase_     :					str   	=torch.linspace(1						,	0						,	steps + 1						,	device=__lowercase	)[:-1]
 UpperCamelCase_     :					Dict   	=get_crash_schedule(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=DanceDiffusionPipeline(unet=__lowercase						,	scheduler=__lowercase	)

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

 UpperCamelCase_     :					Dict   	=sampling.iplms_sample(__lowercase						,	__lowercase						,	__lowercase						,	{}	)
 UpperCamelCase_     :					List[Any]   	=generated.clamp(-1						,	1	)

 UpperCamelCase_     :					Optional[int]   	=(generated - audio).abs().sum()
 UpperCamelCase_     :					Union[str, Any]   	=(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     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.')
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()

 main(args)
