




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


__SCREAMING_SNAKE_CASE     : str      =logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE     : List[str]      ="""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							Tuple  ,					lowerCamelCase:							Tuple=8    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      lowerCamelCase__   	=					width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[Any]=5_12  ,					lowerCamelCase:							Dict=5_12    )       ->      Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					pil_image.resize((w, h)  ,					resample=Image.BICUBIC  ,					reducing_gap=1    )
      lowerCamelCase__   	=					np.array(pil_image.convert('RGB'    )    )
      lowerCamelCase__   	=					arr.astype(np.floataa    ) / 127.5 - 1
      lowerCamelCase__   	=					np.transpose(lowerCamelCase  ,					[2, 0, 1]    )
      lowerCamelCase__   	=					torch.from_numpy(lowerCamelCase    ).unsqueeze(0    )
      return image






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(     self	: List[str]						,	A__	: UNetaDConditionModel						,	A__	: DDPMScheduler						,	A__	: VQModel						,	)					->							Union[str, Any]:
           super().__init__()

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




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: Optional[Any]						,	A__	: Tuple						,	A__	: Tuple      )					->							Dict:
           # get the original timestep using init_timestep
           lowerCamelCase__   	=					min(int(num_inference_steps * strength      )						,	A__      )

           lowerCamelCase__   	=					max(num_inference_steps - init_timestep						,	0      )
           lowerCamelCase__   	=					self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start




     def 			__snake_case (     self	: Tuple						,	A__	: List[str]						,	A__	: str						,	A__	: Optional[int]						,	A__	: Any						,	A__	: Optional[int]						,	A__	: Tuple						,	A__	: List[Any]=None      )					->							Optional[int]:
           if not isinstance(A__						,	(torch.Tensor, PIL.Image.Image, list)      ):
                 raise ValueError(
                     F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A__      )}"""      )

           lowerCamelCase__   	=					image.to(device=A__						,	dtype=A__      )

           lowerCamelCase__   	=					batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                 lowerCamelCase__   	=					image

           else:
                 if isinstance(A__						,	A__      ) and len(A__      ) != batch_size:
                       raise ValueError(
                           F"""You have passed a list of generators of length {len(A__      )}, but requested an effective batch"""
                           F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

                 elif isinstance(A__						,	A__      ):
                       lowerCamelCase__   	=					[
                           self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(A__      )
                       ]
                       lowerCamelCase__   	=					torch.cat(A__						,	dim=0      )
                 else:
                       lowerCamelCase__   	=					self.movq.encode(A__      ).latent_dist.sample(A__      )

                 lowerCamelCase__   	=					self.movq.config.scaling_factor * init_latents

           lowerCamelCase__   	=					torch.cat([init_latents]						,	dim=0      )

           lowerCamelCase__   	=					init_latents.shape
           lowerCamelCase__   	=					randn_tensor(A__						,	generator=A__						,	device=A__						,	dtype=A__      )

           # get latents
           lowerCamelCase__   	=					self.scheduler.add_noise(A__						,	A__						,	A__      )

           lowerCamelCase__   	=					init_latents

           return latents




     def 			__snake_case (     self	: Any						,	A__	: str=0      )					->							str:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError('Please install accelerate via `pip install accelerate`'      )

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

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




     def 			__snake_case (     self	: List[Any]						,	A__	: Optional[Any]=0      )					->							Optional[int]:
           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.'      )

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

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

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

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




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			__snake_case (     self	: Optional[Any]      )					->							Optional[int]:
           if not hasattr(self.unet						,	'_hf_hook'      ):
                 return self.device
           for module in self.unet.modules():
                 if (
                     hasattr(A__						,	'_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(A__      )
     def __call__(     self	: int						,	A__	: Union[torch.FloatTensor, List[torch.FloatTensor]]						,	A__	: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,	A__	: Union[torch.FloatTensor, List[torch.FloatTensor]]						,	A__	: torch.FloatTensor						,	A__	: int = 5_1_2						,	A__	: int = 5_1_2						,	A__	: int = 1_0_0						,	A__	: float = 4.0						,	A__	: float = 0.3						,	A__	: int = 1						,	A__	: Optional[Union[torch.Generator, List[torch.Generator]]] = None						,	A__	: Optional[str] = "pil"						,	A__	: bool = True						,	)					->							str:
           lowerCamelCase__   	=					self._execution_device

           lowerCamelCase__   	=					guidance_scale > 1.0

           if isinstance(A__						,	A__      ):
                 lowerCamelCase__   	=					torch.cat(A__						,	dim=0      )
           if isinstance(A__						,	A__      ):
                 lowerCamelCase__   	=					torch.cat(A__						,	dim=0      )
           if isinstance(A__						,	A__      ):
                 lowerCamelCase__   	=					torch.cat(A__						,	dim=0      )

           lowerCamelCase__   	=					image_embeds.shape[0]

           if do_classifier_free_guidance:
                 lowerCamelCase__   	=					image_embeds.repeat_interleave(A__						,	dim=0      )
                 lowerCamelCase__   	=					negative_image_embeds.repeat_interleave(A__						,	dim=0      )
                 lowerCamelCase__   	=					hint.repeat_interleave(A__						,	dim=0      )

           lowerCamelCase__   	=					torch.cat([negative_image_embeds, image_embeds]						,	dim=0      ).to(dtype=self.unet.dtype						,	device=A__      )
           lowerCamelCase__   	=					torch.cat([hint, hint]						,	dim=0      ).to(dtype=self.unet.dtype						,	device=A__      )

           if not isinstance(A__						,	A__      ):
                 lowerCamelCase__   	=					[image]
           if not all(isinstance(A__						,	(PIL.Image.Image, torch.Tensor)      ) for i in image      ):
                 raise ValueError(
                     F"""Input is in incorrect format: {[type(A__      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""      )

           lowerCamelCase__   	=					torch.cat([prepare_image(A__						,	A__						,	A__      ) for i in image]						,	dim=0      )
           lowerCamelCase__   	=					image.to(dtype=image_embeds.dtype						,	device=A__      )

           lowerCamelCase__   	=					self.movq.encode(A__      )['latents']
           lowerCamelCase__   	=					latents.repeat_interleave(A__						,	dim=0      )
           self.scheduler.set_timesteps(A__						,	device=A__      )
           lowerCamelCase__	,		lowerCamelCase__   	=					self.get_timesteps(A__						,	A__						,	A__      )
           lowerCamelCase__   	=					timesteps[:1].repeat(batch_size * num_images_per_prompt      )
           lowerCamelCase__	,		lowerCamelCase__   	=					downscale_height_and_width(A__						,	A__						,	self.movq_scale_factor      )
           lowerCamelCase__   	=					self.prepare_latents(
               A__						,	A__						,	A__						,	A__						,	image_embeds.dtype						,	A__						,	A__      )
           for i, t in enumerate(self.progress_bar(A__      )      ):
                 # expand the latents if we are doing classifier free guidance
                 lowerCamelCase__   	=					torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

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

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

                 # compute the previous noisy sample x_t -> x_t-1

                 lowerCamelCase__   	=					self.scheduler.step(
                     A__						,	A__						,	A__						,	generator=A__						,	)[0]

           # post-processing
           lowerCamelCase__   	=					self.movq.decode(A__						,	force_not_quantize=A__      )['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"]:
                 lowerCamelCase__   	=					image * 0.5 + 0.5
                 lowerCamelCase__   	=					image.clamp(0						,	1      )
                 lowerCamelCase__   	=					image.cpu().permute(0						,	2						,	3						,	1      ).float().numpy()

           if output_type == "pil":
                 lowerCamelCase__   	=					self.numpy_to_pil(A__      )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__      )





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							List[Any]=0    )       ->      Dict:


      '''simple docstring'''




      if name is None:
            lowerCamelCase__   	=					None
      else:
            lowerCamelCase__   	=					'.' * max(0  ,					spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowerCamelCase__   	=					fmt.format(lowerCamelCase    )

      # Print and recurse (if needed).
      if isinstance(lowerCamelCase  ,					lowerCamelCase    ):
            if msg is not None:
                  print(lowerCamelCase    )
            for k in val.keys():
                  recursive_print(lowerCamelCase  ,					val[k]  ,					spaces + 2    )
      elif isinstance(lowerCamelCase  ,					torch.Tensor    ):
            print(lowerCamelCase  ,					':'  ,					val.size()    )
      else:
            print(lowerCamelCase  ,					':'  ,					lowerCamelCase    )

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any    )       ->      Optional[int]:


      '''simple docstring'''




      lowerCamelCase__   	=					param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCamelCase__   	=					(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      lowerCamelCase__   	=					param.transpose(0  ,					1    ).contiguous()
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      return param

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							int    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					config.n_positions
      lowerCamelCase__   	=					config.n_layer
      lowerCamelCase__   	=					config.vocab_size
      lowerCamelCase__   	=					config.n_head
      lowerCamelCase__   	=					config.n_embd // config.n_head

      lowerCamelCase__   	=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCamelCase__   	=					{
          'transformer.wte.weight': word_embeddings,
          'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
          'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
          'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
      }

      lowerCamelCase__   	=					'model.language_model.encoder.layers.'
      for i in range(lowerCamelCase    ):
            lowerCamelCase__   	=					torch.tril(torch.ones((n_positions, n_positions)  ,					dtype=torch.bool    )    )
            lowerCamelCase__   	=					causal_mask.view(1  ,					1  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					causal_mask
            lowerCamelCase__   	=					torch.tensor(-1e4  ,					dtype=torch.bfloataa    )

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala.transpose(0  ,					1    ).contiguous()

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

      # For LM head, transformers' wants the matrix to weight embeddings.
      lowerCamelCase__   	=					word_embeddings

      return sd_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					6_40_00
      lowerCamelCase__   	=					config_megatron['encoder_seq_length']
      lowerCamelCase__   	=					config_megatron['hidden_size']
      lowerCamelCase__   	=					config_megatron['num_layers']
      lowerCamelCase__   	=					config_megatron['num_attention_heads']
      lowerCamelCase__   	=					config_megatron['ffn_hidden_size']
      lowerCamelCase__   	=					'gelu'
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					config_megatron['layernorm_epsilon']  # 1e-5
      lowerCamelCase__   	=					config_megatron['init_method_std']  # 0.02
      lowerCamelCase__   	=					config_megatron['apply_query_key_layer_scaling']  # True
      lowerCamelCase__   	=					True
      lowerCamelCase__   	=					True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					0  # <unk>
      else:
            lowerCamelCase__   	=					2  # <s>
            lowerCamelCase__   	=					3  # <|endoftext|>
            lowerCamelCase__   	=					0  # <pad>

      return config_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Dict:


      '''simple docstring'''




      print(lowerCamelCase    )

      lowerCamelCase__   	=					args.checkpoint_path
      lowerCamelCase__   	=					args.save_path
      if isfile(lowerCamelCase    ):
            raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

      # Load the model.
      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )

      # Load the config.
      lowerCamelCase__   	=					checkpoint['hyper_parameters']['cfg']
      lowerCamelCase__   	=					GPTaConfig()
      lowerCamelCase__   	=					copy_config(config_hf=lowerCamelCase  ,					config_megatron=lowerCamelCase    )
      lowerCamelCase__   	=					['GPT2LMHeadModel']

      lowerCamelCase__   	=					checkpoint['state_dict']

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase    )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(lowerCamelCase  ,					lowerCamelCase    )

      lowerCamelCase__   	=					'GPTSw3Tokenizer'

      # Store the config to file.
      print('Saving config'    )
      config_hf.save_pretrained(lowerCamelCase    )

      # Store the state_dict to file.
      lowerCamelCase__   	=					os.path.join(lowerCamelCase  ,					'pytorch_model.bin'    )
      print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
      torch.save(lowerCamelCase  ,					lowerCamelCase    )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Optional[int]      =argparse.ArgumentParser()
      parser.add_argument(
          """--checkpoint_path""",
          type=str,
          required=True,
          help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
      )
      parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
      parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
      __SCREAMING_SNAKE_CASE     : Tuple      =parser.parse_args()
      main(_args)