
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,
)


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

__magic_name__  							=	'''
    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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=8				):
    snake_case__    =					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    snake_case__    =					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	(   __UpperCamelCase			):
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						):
          super().__init__()

          self.register_modules(
              unet=lowerCamelCase		,						scheduler=lowerCamelCase		,						movq=lowerCamelCase		,						)
          snake_case__    =					2 ** (len(self.movq.config.block_out_channels			) - 1)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          if latents is None:
              snake_case__    =					randn_tensor(lowerCamelCase		,						generator=lowerCamelCase		,						device=lowerCamelCase		,						dtype=lowerCamelCase			)
          else:
              if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""			)
              snake_case__    =					latents.to(lowerCamelCase			)

          snake_case__    =					latents * scheduler.init_noise_sigma
          return latents
      def A_      (      self		,						lowerCamelCase=0			):
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError("Please install accelerate via `pip install accelerate`"			)

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

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

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

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

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

          # We'll offload the last model manually.
          snake_case__    =					hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def A_      (      self			):
          if not hasattr(self.unet		,						"_hf_hook"			):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(lowerCamelCase		,						"_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(lowerCamelCase			)
      def __call__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase = 5_12		,						lowerCamelCase = 5_12		,						lowerCamelCase = 1_00		,						lowerCamelCase = 4.0		,						lowerCamelCase = 1		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = "pil"		,						lowerCamelCase = True		,						):
          snake_case__    =					self._execution_device

          snake_case__    =					guidance_scale > 1.0

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

          snake_case__    =					image_embeds.shape[0] * num_images_per_prompt

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

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

          snake_case__    =					self.movq.config.latent_channels

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

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

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

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

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

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

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowerCamelCase			)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



