"""simple docstring"""




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    :							Optional[int]													=						logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCAmelCase    :							List[Any]													=						"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=8     ):





		"""simple docstring"""




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






class       a_      (     __UpperCamelCase			):




					def __init__(	self						:     int  ,						snake_case__						:     UNetaDConditionModel  ,						snake_case__						:     DDPMScheduler  ,						snake_case__						:     VQModel  ,						):
							super().__init__()

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     str  ,						snake_case__						:     Any  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]     ):
							if latents is None:
									A        =   randn_tensor(snake_case__  ,						generator=snake_case__  ,						device=snake_case__  ,						dtype=snake_case__     )
							else:
									if latents.shape != shape:
											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
									A        =   latents.to(snake_case__     )

							A        =   latents * scheduler.init_noise_sigma
							return latents




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str=0     ):
							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     List[Any]=0     ):
							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."""     )

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

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

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

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




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							if not hasattr(self.unet  ,						"""_hf_hook"""     ):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(snake_case__  ,						"""_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(snake_case__     )
					def __call__(	self						:     Optional[int]  ,						snake_case__						:     Union[torch.FloatTensor, List[torch.FloatTensor]]  ,						snake_case__						:     Union[torch.FloatTensor, List[torch.FloatTensor]]  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     int = 512  ,						snake_case__						:     int = 512  ,						snake_case__						:     int = 100  ,						snake_case__						:     float = 4.0  ,						snake_case__						:     int = 1  ,						snake_case__						:     Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,						snake_case__						:     Optional[torch.FloatTensor] = None  ,						snake_case__						:     Optional[str] = "pil"  ,						snake_case__						:     bool = True  ,						):
							A        =   self._execution_device

							A        =   guidance_scale > 1.0

							if isinstance(snake_case__  ,						snake_case__     ):
									A        =   torch.cat(snake_case__  ,						dim=0     )
							if isinstance(snake_case__  ,						snake_case__     ):
									A        =   torch.cat(snake_case__  ,						dim=0     )
							if isinstance(snake_case__  ,						snake_case__     ):
									A        =   torch.cat(snake_case__  ,						dim=0     )

							A        =   image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
									A        =   image_embeds.repeat_interleave(snake_case__  ,						dim=0     )
									A        =   negative_image_embeds.repeat_interleave(snake_case__  ,						dim=0     )
									A        =   hint.repeat_interleave(snake_case__  ,						dim=0     )

							A        =   torch.cat([negative_image_embeds, image_embeds]  ,						dim=0     ).to(dtype=self.unet.dtype  ,						device=snake_case__     )
							A        =   torch.cat([hint, hint]  ,						dim=0     ).to(dtype=self.unet.dtype  ,						device=snake_case__     )
							self.scheduler.set_timesteps(snake_case__  ,						device=snake_case__     )
							A        =   self.scheduler.timesteps

							A        =   self.movq.config.latent_channels

							A   ,					A        =   downscale_height_and_width(snake_case__  ,						snake_case__  ,						self.movq_scale_factor     )

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

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

									A        =   {"""image_embeds""": image_embeds, """hint""": hint}
									A        =   self.unet(
									    sample=snake_case__  ,						timestep=snake_case__  ,						encoder_hidden_states=snake_case__  ,						added_cond_kwargs=snake_case__  ,						return_dict=snake_case__  ,						)[0]

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

									# compute the previous noisy sample x_t -> x_t-1
									A        =   self.scheduler.step(
									    snake_case__  ,						snake_case__  ,						snake_case__  ,						generator=snake_case__  ,						)[0]
							# post-processing
							A        =   self.movq.decode(snake_case__  ,						force_not_quantize=snake_case__     )["""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"]:
									A        =   image * 0.5 + 0.5
									A        =   image.clamp(0  ,						1     )
									A        =   image.cpu().permute(0  ,						2  ,						3  ,						1     ).float().numpy()

							if output_type == "pil":
									A        =   self.numpy_to_pil(snake_case__     )

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=snake_case__     )





"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




