






"""simple docstring"""







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,
)


a :				Tuple      					=		logging.get_logger(__name__)  # pylint: disable=invalid-name

a :				Union[str, Any]      					=		"""
    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 						lowercase__(A					,		A					,		A=8						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      Optional[Any]=       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase__				:      Dict=       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





def 						lowercase__(A					,		A=512					,		A=512						)				->Optional[int]:




						"""simple docstring"""



						lowercase__				:      int=       pil_image.resize((w, h)					,		resample=Image.BICUBIC					,		reducing_gap=1						)
						lowercase__				:      Dict=       np.array(pil_image.convert("RGB"						)						)
						lowercase__				:      List[str]=       arr.astype(np.floataa						) / 127.5 - 1
						lowercase__				:      List[str]=       np.transpose(A					,		[2, 0, 1]						)
						lowercase__				:      Dict=       torch.from_numpy(A						).unsqueeze(0						)
						return image







class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												super().__init__()

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




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												# get the original timestep using init_timestep
												lowercase__				:      int=       min(int(num_inference_steps * strength			)       ,  snake_case__			)

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

												return timesteps, num_inference_steps - t_start




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__=None			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  (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(snake_case__			)}'''			)

												lowercase__				:      List[str]=       image.to(device=snake_case__       ,  dtype=snake_case__			)

												lowercase__				:      Any=       batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																		lowercase__				:      str=       image

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

																		elif isinstance(snake_case__       ,  snake_case__			):
																								lowercase__				:      Optional[int]=       [
																								    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(snake_case__			)
																								]
																								lowercase__				:      Any=       torch.cat(snake_case__       ,  dim=0			)
																		else:
																								lowercase__				:      List[str]=       self.movq.encode(snake_case__			).latent_dist.sample(snake_case__			)

																		lowercase__				:      Any=       self.movq.config.scaling_factor * init_latents

												lowercase__				:      Union[str, Any]=       torch.cat([init_latents]       ,  dim=0			)

												lowercase__				:      str=       init_latents.shape
												lowercase__				:      Optional[int]=       randn_tensor(snake_case__       ,  generator=snake_case__       ,  device=snake_case__       ,  dtype=snake_case__			)

												# get latents
												lowercase__				:      Union[str, Any]=       self.scheduler.add_noise(snake_case__       ,  snake_case__       ,  snake_case__			)

												lowercase__				:      str=       init_latents

												return latents




						def 		UpperCAmelCase_       (   self       ,  snake_case__=0			):
												'''simple docstring'''
												if is_accelerate_available():
																		from accelerate import cpu_offload
												else:
																		raise ImportError("Please install accelerate via `pip install accelerate`"			)

												lowercase__				:      Optional[int]=       torch.device(F'''cuda:{gpu_id}'''			)

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




						def 		UpperCAmelCase_       (   self       ,  snake_case__=0			):
												'''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."			)

												lowercase__				:      int=       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)

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

												# We'll offload the last model manually.
												lowercase__				:      str=       hook




						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												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       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 512       ,  snake_case__ = 512       ,  snake_case__ = 100       ,  snake_case__ = 4.0       ,  snake_case__ = 0.3       ,  snake_case__ = 1       ,  snake_case__ = None       ,  snake_case__ = "pil"       ,  snake_case__ = True       ,  ):
												'''simple docstring'''
												lowercase__				:      str=       self._execution_device

												lowercase__				:      str=       guidance_scale > 1.0

												if isinstance(snake_case__       ,  snake_case__			):
																		lowercase__				:      List[Any]=       torch.cat(snake_case__       ,  dim=0			)
												if isinstance(snake_case__       ,  snake_case__			):
																		lowercase__				:      List[str]=       torch.cat(snake_case__       ,  dim=0			)
												if isinstance(snake_case__       ,  snake_case__			):
																		lowercase__				:      List[Any]=       torch.cat(snake_case__       ,  dim=0			)

												lowercase__				:      Union[str, Any]=       image_embeds.shape[0]

												if do_classifier_free_guidance:
																		lowercase__				:      List[Any]=       image_embeds.repeat_interleave(snake_case__       ,  dim=0			)
																		lowercase__				:      List[Any]=       negative_image_embeds.repeat_interleave(snake_case__       ,  dim=0			)
																		lowercase__				:      Any=       hint.repeat_interleave(snake_case__       ,  dim=0			)

												lowercase__				:      Union[str, Any]=       torch.cat([negative_image_embeds, image_embeds]       ,  dim=0			).to(dtype=self.unet.dtype       ,  device=snake_case__			)
												lowercase__				:      Tuple=       torch.cat([hint, hint]       ,  dim=0			).to(dtype=self.unet.dtype       ,  device=snake_case__			)

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

												lowercase__				:      Union[str, Any]=       torch.cat([prepare_image(snake_case__       ,  snake_case__       ,  snake_case__			) for i in image]       ,  dim=0			)
												lowercase__				:      Dict=       image.to(dtype=image_embeds.dtype       ,  device=snake_case__			)

												lowercase__				:      int=       self.movq.encode(snake_case__			)["latents"]
												lowercase__				:      Optional[Any]=       latents.repeat_interleave(snake_case__       ,  dim=0			)
												self.scheduler.set_timesteps(snake_case__       ,  device=snake_case__			)
												lowercase__,	lowercase__				:      Tuple=       self.get_timesteps(snake_case__       ,  snake_case__       ,  snake_case__			)
												lowercase__				:      str=       timesteps[:1].repeat(batch_size * num_images_per_prompt			)
												lowercase__,	lowercase__				:      Any=       downscale_height_and_width(snake_case__       ,  snake_case__       ,  self.movq_scale_factor			)
												lowercase__				:      List[Any]=       self.prepare_latents(
												    snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  image_embeds.dtype       ,  snake_case__       ,  snake_case__			)
												for i, t in enumerate(self.progress_bar(snake_case__			)			):
																		# expand the latents if we are doing classifier free guidance
																		lowercase__				:      Dict=       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

																		lowercase__				:      List[str]=       {"image_embeds": image_embeds, "hint": hint}
																		lowercase__				:      int=       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:
																								lowercase__,	lowercase__				:      str=       noise_pred.split(latents.shape[1]       ,  dim=1			)
																								lowercase__,	lowercase__				:      List[Any]=       noise_pred.chunk(2			)
																								lowercase__,	lowercase__				:      str=       variance_pred.chunk(2			)
																								lowercase__				:      List[Any]=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																								lowercase__				:      Union[str, Any]=       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"]
																		):
																								lowercase__,	lowercase__				:      Tuple=       noise_pred.split(latents.shape[1]       ,  dim=1			)

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

																		lowercase__				:      Dict=       self.scheduler.step(
																		    snake_case__       ,  snake_case__       ,  snake_case__       ,  generator=snake_case__       ,  )[0]

												# post-processing
												lowercase__				:      List[Any]=       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"]:
																		lowercase__				:      str=       image * 0.5 + 0.5
																		lowercase__				:      List[Any]=       image.clamp(0       ,  1			)
																		lowercase__				:      Dict=       image.cpu().permute(0       ,  2       ,  3       ,  1			).float().numpy()

												if output_type == "pil":
																		lowercase__				:      Dict=       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 RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				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(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT 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."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






