

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,
)


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

snake_case_        =			'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[str]=8      ):



     '''simple docstring'''





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


class 				SCREAMING_SNAKE_CASE__ (_snake_case					):





    def __init__(				self						,       a						,       a						,       a						,       ):
         super().__init__()

         self.register_modules(
             unet=UpperCamelCase__						,       scheduler=UpperCamelCase__						,       movq=UpperCamelCase__						,       )
         lowercase__   :       Optional[int]				=	2 ** (len(self.movq.config.block_out_channels) - 1)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a):
         if latents is None:
              lowercase__   :       List[Any]				=	randn_tensor(UpperCamelCase__						,       generator=UpperCamelCase__						,       device=UpperCamelCase__						,       dtype=UpperCamelCase__)
         else:
              if latents.shape != shape:
                   raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
              lowercase__   :       str				=	latents.to(UpperCamelCase__)

         lowercase__   :       Optional[Any]				=	latents * scheduler.init_noise_sigma
         return latents





    def 				snake_case_    (				self						,       a=0):
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`')

         lowercase__   :       Union[str, Any]				=	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(UpperCamelCase__						,       UpperCamelCase__)





    def 				snake_case_    (				self						,       a=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.')

         lowercase__   :       Any				=	torch.device(f"""cuda:{gpu_id}""")

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

         lowercase__   :       List[Any]				=	None
         for cpu_offloaded_model in [self.unet, self.movq]:
              lowercase__				,			lowercase__   :       Dict				=	cpu_offload_with_hook(UpperCamelCase__						,       UpperCamelCase__						,       prev_module_hook=UpperCamelCase__)

         # We'll offload the last model manually.
         lowercase__   :       Optional[int]				=	hook





    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 				snake_case_    (				self):
         if not hasattr(self.unet						,       '_hf_hook'):
              return self.device
         for module in self.unet.modules():
              if (
                  hasattr(UpperCamelCase__						,       '_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(UpperCamelCase__)
    def __call__(				self						,       a						,       a						,       a						,       a = 512						,       a = 512						,       a = 100						,       a = 4.0						,       a = 1						,       a = None						,       a = None						,       a = "pil"						,       a = True						,       ):
         lowercase__   :       Optional[Any]				=	self._execution_device

         lowercase__   :       Union[str, Any]				=	guidance_scale > 1.0

         if isinstance(UpperCamelCase__						,       UpperCamelCase__):
              lowercase__   :       Optional[int]				=	torch.cat(UpperCamelCase__						,       dim=0)
         if isinstance(UpperCamelCase__						,       UpperCamelCase__):
              lowercase__   :       Optional[int]				=	torch.cat(UpperCamelCase__						,       dim=0)
         if isinstance(UpperCamelCase__						,       UpperCamelCase__):
              lowercase__   :       int				=	torch.cat(UpperCamelCase__						,       dim=0)

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

         if do_classifier_free_guidance:
              lowercase__   :       Tuple				=	image_embeds.repeat_interleave(UpperCamelCase__						,       dim=0)
              lowercase__   :       Union[str, Any]				=	negative_image_embeds.repeat_interleave(UpperCamelCase__						,       dim=0)
              lowercase__   :       List[Any]				=	hint.repeat_interleave(UpperCamelCase__						,       dim=0)

         lowercase__   :       List[str]				=	torch.cat([negative_image_embeds, image_embeds]						,       dim=0).to(dtype=self.unet.dtype						,       device=UpperCamelCase__)
         lowercase__   :       Tuple				=	torch.cat([hint, hint]						,       dim=0).to(dtype=self.unet.dtype						,       device=UpperCamelCase__)
         self.scheduler.set_timesteps(UpperCamelCase__						,       device=UpperCamelCase__)
         lowercase__   :       List[Any]				=	self.scheduler.timesteps

         lowercase__   :       Dict				=	self.movq.config.latent_channels

         lowercase__				,			lowercase__   :       Dict				=	downscale_height_and_width(UpperCamelCase__						,       UpperCamelCase__						,       self.movq_scale_factor)

         # create initial latent
         lowercase__   :       List[str]				=	self.prepare_latents(
             (batch_size, num_channels_latents, height, width)						,       image_embeds.dtype						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       self.scheduler						,       )

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

              lowercase__   :       Union[str, Any]				=	{'image_embeds': image_embeds, 'hint': hint}
              lowercase__   :       Union[str, Any]				=	self.unet(
                  sample=UpperCamelCase__						,       timestep=UpperCamelCase__						,       encoder_hidden_states=UpperCamelCase__						,       added_cond_kwargs=UpperCamelCase__						,       return_dict=UpperCamelCase__						,       )[0]

              if do_classifier_free_guidance:
                   lowercase__				,			lowercase__   :       Optional[Any]				=	noise_pred.split(latents.shape[1]						,       dim=1)
                   lowercase__				,			lowercase__   :       Tuple				=	noise_pred.chunk(2)
                   lowercase__				,			lowercase__   :       List[Any]				=	variance_pred.chunk(2)
                   lowercase__   :       str				=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   lowercase__   :       Optional[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__   :       Union[str, Any]				=	noise_pred.split(latents.shape[1]						,       dim=1)

              # compute the previous noisy sample x_t -> x_t-1
              lowercase__   :       Union[str, Any]				=	self.scheduler.step(
                  UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       generator=UpperCamelCase__						,       )[0]
         # post-processing
         lowercase__   :       List[str]				=	self.movq.decode(UpperCamelCase__						,       force_not_quantize=UpperCamelCase__)['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__   :       Optional[Any]				=	image * 0.5 + 0.5
              lowercase__   :       Optional[Any]				=	image.clamp(0						,       1)
              lowercase__   :       Optional[Any]				=	image.cpu().permute(0						,       2						,       3						,       1).float().numpy()

         if output_type == "pil":
              lowercase__   :       Optional[int]				=	self.numpy_to_pil(UpperCamelCase__)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=UpperCamelCase__)




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

