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,
)


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

UpperCamelCase__											=					'\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    lowerCAmelCase_ (     __A,  __A,  __A=8							)     ->				int:







      '''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							(				UpperCAmelCase_     ):


   def __init__(self     :						int       ,  __UpperCAmelCase     :						UNetaDConditionModel       ,  __UpperCAmelCase     :						DDPMScheduler       ,  __UpperCAmelCase     :						VQModel       ,  )						->				List[str]:
         """simple docstring"""



         super().__init__()

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


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				List[Any]:
         """simple docstring"""



         if latents is None:
               UpperCAmelCase__								=  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}"""		)
               UpperCAmelCase__								=  latents.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  latents * scheduler.init_noise_sigma
         return latents


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						int=0		)						->				Tuple:
         """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(__UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int=0		)						->				List[str]:
         """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=__UpperCAmelCase		)
               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(__UpperCAmelCase       ,  __UpperCAmelCase       ,  prev_module_hook=__UpperCAmelCase		)

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


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



         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     :						int       ,  __UpperCAmelCase     :						Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  __UpperCAmelCase     :						Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						int = 5_1_2       ,  __UpperCAmelCase     :						int = 5_1_2       ,  __UpperCAmelCase     :						int = 1_0_0       ,  __UpperCAmelCase     :						float = 4.0       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  __UpperCAmelCase     :						Optional[torch.FloatTensor] = None       ,  __UpperCAmelCase     :						Optional[str] = "pil"       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self._execution_device

         UpperCAmelCase__								=  guidance_scale > 1.0

         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  torch.cat(__UpperCAmelCase       ,  dim=0		)
         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  torch.cat(__UpperCAmelCase       ,  dim=0		)
         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  torch.cat(__UpperCAmelCase       ,  dim=0		)

         UpperCAmelCase__								=  image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
               UpperCAmelCase__								=  image_embeds.repeat_interleave(__UpperCAmelCase       ,  dim=0		)
               UpperCAmelCase__								=  negative_image_embeds.repeat_interleave(__UpperCAmelCase       ,  dim=0		)
               UpperCAmelCase__								=  hint.repeat_interleave(__UpperCAmelCase       ,  dim=0		)

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

         UpperCAmelCase__								=  self.movq.config.latent_channels

         UpperCAmelCase__		,      UpperCAmelCase__								=  downscale_height_and_width(__UpperCAmelCase       ,  __UpperCAmelCase       ,  self.movq_scale_factor		)

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

               UpperCAmelCase__								=  {"image_embeds": image_embeds, "hint": hint}
               UpperCAmelCase__								=  self.unet(
                   sample=__UpperCAmelCase       ,  timestep=__UpperCAmelCase       ,  encoder_hidden_states=__UpperCAmelCase       ,  added_cond_kwargs=__UpperCAmelCase       ,  return_dict=__UpperCAmelCase       ,  )[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(
                   __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  generator=__UpperCAmelCase       ,  )[0]
         # post-processing
         UpperCAmelCase__								=  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"]:
               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(__UpperCAmelCase		)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=__UpperCAmelCase		)




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
      from sagemaker import Session, TrainingJobAnalytics
      from sagemaker.huggingface import HuggingFace





@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'		,				'False'     )     ) is not True		,				reason='Skipping test because should only be run when releasing minor transformers version'		,				)
@pytest.mark.usefixtures('sm_env'     )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_50, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]     )
class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """simple docstring"""



         if self.framework == "pytorch":
               subprocess.run(
                   f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()       ,  encoding="utf-8"       ,  check=__UpperCAmelCase       ,  )
         assert hasattr(self       ,  "env"		)


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]=1		)						->				Dict:
         """simple docstring"""



         return HuggingFace(
             entry_point=self.script       ,  source_dir=self.env.test_path       ,  role=self.env.role       ,  image_uri=self.env.image_uri       ,  base_job_name=f"""{self.env.base_job_name}-single"""       ,  instance_count=__UpperCAmelCase       ,  instance_type=self.instance_type       ,  debugger_hook_config=__UpperCAmelCase       ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  py_version="py36"       ,  )


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         TrainingJobAnalytics(__UpperCAmelCase		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)





   def 			lowercase_      (self     :						Any		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator()

         # run training
         estimator.fit()

         # result dataframe
         UpperCAmelCase__								=  TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

         # extract kpis
         UpperCAmelCase__								=  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
         UpperCAmelCase__								=  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         UpperCAmelCase__								=  (
             Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"       ,  9_9_9_9_9_9		)
         )

         # assert kpis
         assert train_runtime <= self.results["train_runtime"]
         assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy		)
         assert all(t <= self.results["eval_loss"] for t in eval_loss		)

         # dump tests result into json file to share in PR
         with open(f"""{estimator.latest_training_job.name}.json"""       ,  "w"		) as outfile:
               json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,  __UpperCAmelCase		)



