





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,
)


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

__lowercase	: Dict       ="""
    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      a__			(       lowercase__	,     lowercase__	,     lowercase__=8	):






   '''simple docstring'''






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





class   A							(						__lowercase    ):



      def __init__(						self:    Optional[int]		,	_lowerCAmelCase:    UNetaDConditionModel		,	_lowerCAmelCase:    DDPMScheduler		,	_lowerCAmelCase:    VQModel		,	)    ->     List[str]:
         '''simple docstring'''

         super().__init__()

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



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Tuple		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Any							)    ->     str:
         '''simple docstring'''

         if latents is None:
            UpperCAmelCase_           =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}'							)
            UpperCAmelCase_           =latents.to(_lowerCAmelCase							)

         UpperCAmelCase_           =latents * scheduler.init_noise_sigma
         return latents



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    Dict=0							)    ->     int:
         '''simple docstring'''

         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("Please install accelerate via `pip install accelerate`"							)

         UpperCAmelCase_           =torch.device(F'cuda:{gpu_id}'							)

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



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    Dict=0							)    ->     Tuple:
         '''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."							)

         UpperCAmelCase_           =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)

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

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



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     List[str]:
         '''simple docstring'''

         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:    List[str]		,	_lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]		,	_lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    int = 512		,	_lowerCAmelCase:    int = 512		,	_lowerCAmelCase:    int = 100		,	_lowerCAmelCase:    float = 4.0		,	_lowerCAmelCase:    int = 1		,	_lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	_lowerCAmelCase:    Optional[torch.FloatTensor] = None		,	_lowerCAmelCase:    Optional[str] = "pil"		,	_lowerCAmelCase:    bool = True		,	)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =self._execution_device

         UpperCAmelCase_           =guidance_scale > 1.0

         if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							):
            UpperCAmelCase_           =torch.cat(_lowerCAmelCase		,	dim=0							)
         if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							):
            UpperCAmelCase_           =torch.cat(_lowerCAmelCase		,	dim=0							)
         if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							):
            UpperCAmelCase_           =torch.cat(_lowerCAmelCase		,	dim=0							)

         UpperCAmelCase_           =image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
            UpperCAmelCase_           =image_embeds.repeat_interleave(_lowerCAmelCase		,	dim=0							)
            UpperCAmelCase_           =negative_image_embeds.repeat_interleave(_lowerCAmelCase		,	dim=0							)
            UpperCAmelCase_           =hint.repeat_interleave(_lowerCAmelCase		,	dim=0							)

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

         UpperCAmelCase_           =self.movq.config.latent_channels

         UpperCAmelCase_    ,							UpperCAmelCase_           =downscale_height_and_width(_lowerCAmelCase		,	_lowerCAmelCase		,	self.movq_scale_factor							)

         # create initial latent
         UpperCAmelCase_           =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
            UpperCAmelCase_           =torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

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

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

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

         if output_type == "pil":
            UpperCAmelCase_           =self.numpy_to_pil(_lowerCAmelCase							)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase							)












"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase_							      = logging.getLogger()

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_	:					Optional[Any]     	=   parser.parse_args()
			return args.f







class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     None:
				UpperCAmelCase_	:					List[str]     	=   logging.StreamHandler(sys.stdout		)
				logger.addHandler(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   get_gpu_count()

				if n_gpu > 1:
							pass
							# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
							# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
							# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
							# cmd = [sys.executable] + distributed_args + args
							# execute_subprocess_async(cmd, env=self.get_env())
							# XXX: test the results - need to save them first into .json file
				else:
							args.insert(0      ,      "run_glue_deebert.py"		)
							with patch.object(lowerCAmelCase_      ,      "argv"      ,      lowerCAmelCase_		):
										UpperCAmelCase_	:					Dict     	=   run_glue_deebert.main()
										for value in result.values():
													self.assertGreaterEqual(lowerCAmelCase_      ,      0.6_6_6		)





	@slow
	@require_torch_non_multi_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					Tuple     	=   "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)



