
"""simple docstring"""

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def 							A						(    snake_case				:int    , snake_case				:Tuple    , snake_case				:Optional[int]		)				->     Any:
 if isinstance(snake_case    , torch.Tensor		):
  return image
 elif isinstance(snake_case    , PIL.Image.Image		):
  __UpperCamelCase      =	[image]

 if isinstance(image[0]    , PIL.Image.Image		):
  __UpperCamelCase      =	[np.array(i.resize((w, h)    , resample=PIL_INTERPOLATION['lanczos']		)		)[None, :] for i in image]
  __UpperCamelCase      =	np.concatenate(snake_case    , axis=0		)
  __UpperCamelCase      =	np.array(snake_case		).astype(np.floataa		) / 255.0
  __UpperCamelCase      =	image.transpose(0    , 3    , 1    , 2		)
  __UpperCamelCase      =	2.0 * image - 1.0
  __UpperCamelCase      =	torch.from_numpy(snake_case		)
 elif isinstance(image[0]    , torch.Tensor		):
  __UpperCamelCase      =	torch.cat(snake_case    , dim=0		)
 return image






def 							A						(    snake_case				:Tuple    , snake_case				:Dict    , snake_case				:Tuple    , snake_case				:Optional[int]=0.9_995		)				->     str:
 if not isinstance(snake_case    , np.ndarray		):
  __UpperCamelCase      =	True
  __UpperCamelCase      =	va.device
  __UpperCamelCase      =	va.cpu().numpy()
  __UpperCamelCase      =	va.cpu().numpy()

 __UpperCamelCase      =	np.sum(va * va / (np.linalg.norm(snake_case		) * np.linalg.norm(snake_case		))		)
 if np.abs(snake_case		) > DOT_THRESHOLD:
  __UpperCamelCase      =	(1 - t) * va + t * va
 else:
  __UpperCamelCase      =	np.arccos(snake_case		)
  __UpperCamelCase      =	np.sin(snake_case		)
  __UpperCamelCase      =	theta_a * t
  __UpperCamelCase      =	np.sin(snake_case		)
  __UpperCamelCase      =	np.sin(theta_a - theta_t		) / sin_theta_a
  __UpperCamelCase      =	sin_theta_t / sin_theta_a
  __UpperCamelCase      =	sa * va + sa * va

 if inputs_are_torch:
  __UpperCamelCase      =	torch.from_numpy(snake_case		).to(snake_case		)

 return va






def 							A						(    snake_case				:Union[str, Any]    , snake_case				:Union[str, Any]		)				->     Tuple:
 __UpperCamelCase      =	F.normalize(snake_case    , dim=-1		)
 __UpperCamelCase      =	F.normalize(snake_case    , dim=-1		)
 return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)






def 							A						(    snake_case				:int    , snake_case				:Optional[int]		)				->     int:
 for param in model.parameters():
  __UpperCamelCase      =	value





class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					):

      '''simple docstring'''




      super().__init__()
      self.register_modules(
          vae=__UpperCAmelCase     ,					text_encoder=__UpperCAmelCase     ,					clip_model=__UpperCAmelCase     ,					tokenizer=__UpperCAmelCase     ,					unet=__UpperCAmelCase     ,					scheduler=__UpperCAmelCase     ,					feature_extractor=__UpperCAmelCase     ,					coca_model=__UpperCAmelCase     ,					coca_tokenizer=__UpperCAmelCase     ,					coca_transform=__UpperCAmelCase     ,					)
      __UpperCamelCase      =	(
          feature_extractor.size
          if isinstance(feature_extractor.size     ,					__UpperCAmelCase				)
          else feature_extractor.size['shortest_edge']
      )
      __UpperCamelCase      =	transforms.Normalize(mean=feature_extractor.image_mean     ,					std=feature_extractor.image_std				)
      set_requires_grad(self.text_encoder     ,					__UpperCAmelCase				)
      set_requires_grad(self.clip_model     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase = "auto"				):

      '''simple docstring'''




      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       __UpperCamelCase      =	self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.enable_attention_slicing(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      set_requires_grad(self.vae     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      set_requires_grad(self.vae     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      set_requires_grad(self.unet     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      set_requires_grad(self.unet     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	min(int(num_inference_steps * strength				)     ,					__UpperCAmelCase				)

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

      return timesteps, num_inference_steps - t_start





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				):

      '''simple docstring'''




      if not isinstance(__UpperCAmelCase     ,					torch.Tensor				):
       raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(__UpperCAmelCase				)}'				)

      __UpperCamelCase      =	image.to(device=__UpperCAmelCase     ,					dtype=__UpperCAmelCase				)

      if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	[
           self.vae.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(__UpperCAmelCase				)
       ]
       __UpperCamelCase      =	torch.cat(__UpperCAmelCase     ,					dim=0				)
      else:
       __UpperCamelCase      =	self.vae.encode(__UpperCAmelCase				).latent_dist.sample(__UpperCAmelCase				)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __UpperCamelCase      =	0.1_8_2_1_5 * init_latents
      __UpperCamelCase      =	init_latents.repeat_interleave(__UpperCAmelCase     ,					dim=0				)

      __UpperCamelCase      =	randn_tensor(init_latents.shape     ,					generator=__UpperCAmelCase     ,					device=__UpperCAmelCase     ,					dtype=__UpperCAmelCase				)

      # get latents
      __UpperCamelCase      =	self.scheduler.add_noise(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	init_latents

      return latents





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.coca_transform(__UpperCAmelCase				).unsqueeze(0				)
      with torch.no_grad(), torch.cuda.amp.autocast():
       __UpperCamelCase      =	self.coca_model.generate(transformed_image.to(device=self.device     ,					dtype=self.coca_model.dtype				)				)
      __UpperCamelCase      =	self.coca_tokenizer.decode(generated[0].cpu().numpy()				)
      return generated.split('<end_of_text>'				)[0].replace('<start_of_text>'     ,					''				).rstrip(' .,'				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.feature_extractor.preprocess(__UpperCAmelCase				)
      __UpperCamelCase      =	torch.from_numpy(clip_image_input['pixel_values'][0]				).unsqueeze(0				).to(self.device				).half()
      __UpperCamelCase      =	self.clip_model.get_image_features(__UpperCAmelCase				)
      __UpperCamelCase      =	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,					dim=-1     ,					keepdim=__UpperCAmelCase				)
      __UpperCamelCase      =	image_embeddings_clip.repeat_interleave(__UpperCAmelCase     ,					dim=0				)
      return image_embeddings_clip





     @torch.enable_grad()
     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	latents.detach().requires_grad_()

      __UpperCamelCase      =	self.scheduler.scale_model_input(__UpperCAmelCase     ,					__UpperCAmelCase				)

      # predict the noise residual
      __UpperCamelCase      =	self.unet(__UpperCAmelCase     ,					__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase				).sample

      if isinstance(self.scheduler     ,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
       __UpperCamelCase      =	self.scheduler.alphas_cumprod[timestep]
       __UpperCamelCase      =	1 - alpha_prod_t
       # compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       __UpperCamelCase      =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

       __UpperCamelCase      =	torch.sqrt(__UpperCAmelCase				)
       __UpperCamelCase      =	pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	self.scheduler.sigmas[index]
       __UpperCamelCase      =	latents - sigma * noise_pred
      else:
       raise ValueError(F'scheduler type {type(self.scheduler				)} not supported'				)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __UpperCamelCase      =	1 / 0.1_8_2_1_5 * sample
      __UpperCamelCase      =	self.vae.decode(__UpperCAmelCase				).sample
      __UpperCamelCase      =	(image / 2 + 0.5).clamp(0     ,					1				)

      __UpperCamelCase      =	transforms.Resize(self.feature_extractor_size				)(__UpperCAmelCase				)
      __UpperCamelCase      =	self.normalize(__UpperCAmelCase				).to(latents.dtype				)

      __UpperCamelCase      =	self.clip_model.get_image_features(__UpperCAmelCase				)
      __UpperCamelCase      =	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,					dim=-1     ,					keepdim=__UpperCAmelCase				)

      __UpperCamelCase      =	spherical_dist_loss(__UpperCAmelCase     ,					__UpperCAmelCase				).mean() * clip_guidance_scale

      __UpperCamelCase      =	-torch.autograd.grad(__UpperCAmelCase     ,					__UpperCAmelCase				)[0]

      if isinstance(self.scheduler     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	latents.detach() + grads * (sigma**2)
       __UpperCamelCase      =	noise_pred_original
      else:
       __UpperCamelCase      =	noise_pred_original - torch.sqrt(__UpperCAmelCase				) * grads
      return noise_pred, latents







     @torch.no_grad()
     def __call__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 512     ,					__UpperCAmelCase = 512     ,					__UpperCAmelCase = 0.6     ,					__UpperCAmelCase = 50     ,					__UpperCAmelCase = 7.5     ,					__UpperCAmelCase = 1     ,					__UpperCAmelCase = 0.0     ,					__UpperCAmelCase = 100     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = "pil"     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = 0.8     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 0.1     ,					):

      '''simple docstring'''




      if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) and len(__UpperCAmelCase				) != batch_size:
       raise ValueError(F'You have passed {batch_size} batch_size, but only {len(__UpperCAmelCase				)} generators.'				)

      if height % 8 != 0 or width % 8 != 0:
       raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'				)

      if isinstance(__UpperCAmelCase     ,					torch.Generator				) and batch_size > 1:
       __UpperCamelCase      =	[generator] + [None] * (batch_size - 1)

      __UpperCamelCase      =	[
          ('model', self.coca_model is None),
          ('tokenizer', self.coca_tokenizer is None),
          ('transform', self.coca_transform is None),
      ]
      __UpperCamelCase      =	[x[0] for x in coca_is_none if x[1]]
      __UpperCamelCase      =	', '.join(__UpperCAmelCase				)
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
       if len(__UpperCAmelCase				):
        raise ValueError(
            F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
            F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'				)
       __UpperCamelCase      =	self.get_image_description(__UpperCAmelCase				)
      if style_prompt is None:
       if len(__UpperCAmelCase				):
        raise ValueError(
            F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
            F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'				)
       __UpperCamelCase      =	self.get_image_description(__UpperCAmelCase				)

      # get prompt text embeddings for content and style
      __UpperCamelCase      =	self.tokenizer(
          __UpperCAmelCase     ,					padding='max_length'     ,					max_length=self.tokenizer.model_max_length     ,					truncation=__UpperCAmelCase     ,					return_tensors='pt'     ,					)
      __UpperCamelCase      =	self.text_encoder(content_text_input.input_ids.to(self.device				)				)[0]

      __UpperCamelCase      =	self.tokenizer(
          __UpperCAmelCase     ,					padding='max_length'     ,					max_length=self.tokenizer.model_max_length     ,					truncation=__UpperCAmelCase     ,					return_tensors='pt'     ,					)
      __UpperCamelCase      =	self.text_encoder(style_text_input.input_ids.to(self.device				)				)[0]

      __UpperCamelCase      =	slerp(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

      # duplicate text embeddings for each generation per prompt
      __UpperCamelCase      =	text_embeddings.repeat_interleave(__UpperCAmelCase     ,					dim=0				)

      # set timesteps
      __UpperCamelCase      =	'offset' in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
      __UpperCamelCase      =	{}
      if accepts_offset:
       __UpperCamelCase      =	1

      self.scheduler.set_timesteps(__UpperCAmelCase     ,					**__UpperCAmelCase				)
      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      self.scheduler.timesteps.to(self.device				)

      __UpperCamelCase  ,     __UpperCamelCase      =	self.get_timesteps(__UpperCAmelCase     ,					__UpperCAmelCase     ,					self.device				)
      __UpperCamelCase      =	timesteps[:1].repeat(__UpperCAmelCase				)

      # Preprocess image
      __UpperCamelCase      =	preprocess(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	self.prepare_latents(
          __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					text_embeddings.dtype     ,					self.device     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	preprocess(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	self.prepare_latents(
          __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					text_embeddings.dtype     ,					self.device     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	slerp(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

      if clip_guidance_scale > 0:
       __UpperCamelCase      =	self.get_clip_image_embeddings(__UpperCAmelCase     ,					__UpperCAmelCase				)
       __UpperCamelCase      =	self.get_clip_image_embeddings(__UpperCAmelCase     ,					__UpperCAmelCase				)
       __UpperCamelCase      =	slerp(
           __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      __UpperCamelCase      =	guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
       __UpperCamelCase      =	content_text_input.input_ids.shape[-1]
       __UpperCamelCase      =	self.tokenizer(['']     ,					padding='max_length'     ,					max_length=__UpperCAmelCase     ,					return_tensors='pt'				)
       __UpperCamelCase      =	self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
       # duplicate unconditional embeddings for each generation per prompt
       __UpperCamelCase      =	uncond_embeddings.repeat_interleave(__UpperCAmelCase     ,					dim=0				)

       # For classifier free guidance, we need to do two forward passes.
       # Here we concatenate the unconditional and text embeddings into a single batch
       # to avoid doing two forward passes
       __UpperCamelCase      =	torch.cat([uncond_embeddings, text_embeddings]				)

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      __UpperCamelCase      =	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
      __UpperCamelCase      =	text_embeddings.dtype
      if latents is None:
       if self.device.type == "mps":
        # randn does not work reproducibly on mps
        __UpperCamelCase      =	torch.randn(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					device='cpu'     ,					dtype=__UpperCAmelCase				).to(
            self.device				)
       else:
        __UpperCamelCase      =	torch.randn(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					device=self.device     ,					dtype=__UpperCAmelCase				)
      else:
       if latents.shape != latents_shape:
        raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'				)
       __UpperCamelCase      =	latents.to(self.device				)

      # scale the initial noise by the standard deviation required by the scheduler
      __UpperCamelCase      =	latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      __UpperCamelCase      =	'eta' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
      __UpperCamelCase      =	{}
      if accepts_eta:
       __UpperCamelCase      =	eta

      # check if the scheduler accepts generator
      __UpperCamelCase      =	'generator' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
      if accepts_generator:
       __UpperCamelCase      =	generator

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

        # predict the noise residual
        __UpperCamelCase      =	self.unet(__UpperCAmelCase     ,					__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase				).sample

        # perform classifier free guidance
        if do_classifier_free_guidance:
         __UpperCamelCase  ,     __UpperCamelCase      =	noise_pred.chunk(2				)
         __UpperCamelCase      =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # perform clip guidance
        if clip_guidance_scale > 0:
         __UpperCamelCase      =	(
             text_embeddings.chunk(2				)[1] if do_classifier_free_guidance else text_embeddings
         )
         __UpperCamelCase  ,     __UpperCamelCase      =	self.cond_fn(
             __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)

        # compute the previous noisy sample x_t -> x_t-1
        __UpperCamelCase      =	self.scheduler.step(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase				).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __UpperCamelCase      =	1 / 0.1_8_2_1_5 * latents
      __UpperCamelCase      =	self.vae.decode(__UpperCAmelCase				).sample

      __UpperCamelCase      =	(image / 2 + 0.5).clamp(0     ,					1				)
      __UpperCamelCase      =	image.cpu().permute(0     ,					2     ,					3     ,					1				).numpy()

      if output_type == "pil":
       __UpperCamelCase      =	self.numpy_to_pil(__UpperCAmelCase				)

      if not return_dict:
       return (image, None)

      return StableDiffusionPipelineOutput(images=__UpperCAmelCase     ,					nsfw_content_detected=__UpperCAmelCase				)





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



