


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,
)


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

_UpperCAmelCase     :					List[str]       ="""
    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      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_=8	)->	Any:
				lowerCAmelCase_	: List[str]      			=			height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowerCAmelCase_	: str      			=			width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_=512	,				lowerCAmelCase_=512	)->	Union[str, Any]:
				lowerCAmelCase_	: Tuple      			=			pil_image.resize((w, h)	,				resample=Image.BICUBIC	,				reducing_gap=1	)
				lowerCAmelCase_	: Optional[Any]      			=			np.array(pil_image.convert('''RGB'''	)	)
				lowerCAmelCase_	: Any      			=			arr.astype(np.floataa	) / 127.5 - 1
				lowerCAmelCase_	: Union[str, Any]      			=			np.transpose(lowerCAmelCase_	,				[2, 0, 1]	)
				lowerCAmelCase_	: Union[str, Any]      			=			torch.from_numpy(lowerCAmelCase_	).unsqueeze(0	)
				return image







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  )   ->      Tuple:
										super().__init__()

										self.register_modules(
										    unet=__lowercase				,  scheduler=__lowercase				,  movq=__lowercase				,  )
										lowerCAmelCase_	: List[Any]      			=			2 ** (len(self.movq.config.block_out_channels							) - 1)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										# get the original timestep using init_timestep
										lowerCAmelCase_	: Any      			=			min(int(num_inference_steps * strength							)				,  __lowercase							)

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

										return timesteps, num_inference_steps - t_start




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase=None							)   ->      str:
										if not isinstance(__lowercase				,  (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(__lowercase							)}"""							)

										lowerCAmelCase_	: str      			=			image.to(device=__lowercase				,  dtype=__lowercase							)

										lowerCAmelCase_	: Optional[Any]      			=			batch_size * num_images_per_prompt

										if image.shape[1] == 4:
														lowerCAmelCase_	: Optional[int]      			=			image

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

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

														lowerCAmelCase_	: Union[str, Any]      			=			self.movq.config.scaling_factor * init_latents

										lowerCAmelCase_	: str      			=			torch.cat([init_latents]				,  dim=0							)

										lowerCAmelCase_	: Tuple      			=			init_latents.shape
										lowerCAmelCase_	: Any      			=			randn_tensor(__lowercase				,  generator=__lowercase				,  device=__lowercase				,  dtype=__lowercase							)

										# get latents
										lowerCAmelCase_	: Optional[int]      			=			self.scheduler.add_noise(__lowercase				,  __lowercase				,  __lowercase							)

										lowerCAmelCase_	: List[str]      			=			init_latents

										return latents




						def   lowercase_	(			self				,  __lowercase=0							)   ->      List[Any]:
										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

										lowerCAmelCase_	: Optional[int]      			=			torch.device(f"""cuda:{gpu_id}"""							)

										lowerCAmelCase_	: Optional[int]      			=			[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(__lowercase				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase=0							)   ->      Tuple:
										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.'''							)

										lowerCAmelCase_	: List[Any]      			=			torch.device(f"""cuda:{gpu_id}"""							)

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

										lowerCAmelCase_	: Optional[Any]      			=			None
										for cpu_offloaded_model in [self.unet, self.movq]:
														lowerCAmelCase_			,					lowerCAmelCase_	: Union[str, Any]      			=			cpu_offload_with_hook(__lowercase				,  __lowercase				,  prev_module_hook=__lowercase							)

										# We'll offload the last model manually.
										lowerCAmelCase_	: Dict      			=			hook




						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   lowercase_	(			self							)   ->      Tuple:
										if not hasattr(self.unet				,  '''_hf_hook'''							):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(__lowercase				,  '''_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(__lowercase							)
						def __call__(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase = 5_1_2				,  __lowercase = 5_1_2				,  __lowercase = 1_0_0				,  __lowercase = 4.0				,  __lowercase = 0.3				,  __lowercase = 1				,  __lowercase = None				,  __lowercase = "pil"				,  __lowercase = True				,  )   ->      Optional[int]:
										lowerCAmelCase_	: Optional[Any]      			=			self._execution_device

										lowerCAmelCase_	: Optional[Any]      			=			guidance_scale > 1.0

										if isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: str      			=			torch.cat(__lowercase				,  dim=0							)
										if isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: int      			=			torch.cat(__lowercase				,  dim=0							)
										if isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: Optional[Any]      			=			torch.cat(__lowercase				,  dim=0							)

										lowerCAmelCase_	: str      			=			image_embeds.shape[0]

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

										lowerCAmelCase_	: Tuple      			=			torch.cat([negative_image_embeds, image_embeds]				,  dim=0							).to(dtype=self.unet.dtype				,  device=__lowercase							)
										lowerCAmelCase_	: List[str]      			=			torch.cat([hint, hint]				,  dim=0							).to(dtype=self.unet.dtype				,  device=__lowercase							)

										if not isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: Any      			=			[image]
										if not all(isinstance(__lowercase				,  (PIL.Image.Image, torch.Tensor)							) for i in image							):
														raise ValueError(
														    f"""Input is in incorrect format: {[type(__lowercase							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

										lowerCAmelCase_	: Any      			=			torch.cat([prepare_image(__lowercase				,  __lowercase				,  __lowercase							) for i in image]				,  dim=0							)
										lowerCAmelCase_	: Tuple      			=			image.to(dtype=image_embeds.dtype				,  device=__lowercase							)

										lowerCAmelCase_	: Optional[int]      			=			self.movq.encode(__lowercase							)['''latents''']
										lowerCAmelCase_	: List[str]      			=			latents.repeat_interleave(__lowercase				,  dim=0							)
										self.scheduler.set_timesteps(__lowercase				,  device=__lowercase							)
										lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			self.get_timesteps(__lowercase				,  __lowercase				,  __lowercase							)
										lowerCAmelCase_	: str      			=			timesteps[:1].repeat(batch_size * num_images_per_prompt							)
										lowerCAmelCase_			,					lowerCAmelCase_	: Any      			=			downscale_height_and_width(__lowercase				,  __lowercase				,  self.movq_scale_factor							)
										lowerCAmelCase_	: str      			=			self.prepare_latents(
										    __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  image_embeds.dtype				,  __lowercase				,  __lowercase							)
										for i, t in enumerate(self.progress_bar(__lowercase							)							):
														# expand the latents if we are doing classifier free guidance
														lowerCAmelCase_	: Union[str, Any]      			=			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

														lowerCAmelCase_	: Optional[Any]      			=			{'''image_embeds''': image_embeds, '''hint''': hint}
														lowerCAmelCase_	: Optional[Any]      			=			self.unet(
														    sample=__lowercase				,  timestep=__lowercase				,  encoder_hidden_states=__lowercase				,  added_cond_kwargs=__lowercase				,  return_dict=__lowercase				,  )[0]

														if do_classifier_free_guidance:
																		lowerCAmelCase_			,					lowerCAmelCase_	: int      			=			noise_pred.split(latents.shape[1]				,  dim=1							)
																		lowerCAmelCase_			,					lowerCAmelCase_	: Union[str, Any]      			=			noise_pred.chunk(2							)
																		lowerCAmelCase_			,					lowerCAmelCase_	: Union[str, Any]      			=			variance_pred.chunk(2							)
																		lowerCAmelCase_	: Union[str, Any]      			=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		lowerCAmelCase_	: Dict      			=			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"]
														):
																		lowerCAmelCase_			,					lowerCAmelCase_	: str      			=			noise_pred.split(latents.shape[1]				,  dim=1							)

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

														lowerCAmelCase_	: List[str]      			=			self.scheduler.step(
														    __lowercase				,  __lowercase				,  __lowercase				,  generator=__lowercase				,  )[0]

										# post-processing
										lowerCAmelCase_	: Optional[Any]      			=			self.movq.decode(__lowercase				,  force_not_quantize=__lowercase							)['''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"]:
														lowerCAmelCase_	: Any      			=			image * 0.5 + 0.5
														lowerCAmelCase_	: Union[str, Any]      			=			image.clamp(0				,  1							)
														lowerCAmelCase_	: Any      			=			image.cpu().permute(0				,  2				,  3				,  1							).float().numpy()

										if output_type == "pil":
														lowerCAmelCase_	: Optional[int]      			=			self.numpy_to_pil(__lowercase							)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__lowercase							)



import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[int]       =argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument("""--epochs""", type=int, default=1)
						parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
						parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
						parser.add_argument("""--model_name_or_path""", type=str)
						parser.add_argument("""--learning_rate""", type=str, default=5E-5)
						parser.add_argument("""--do_train""", type=bool, default=True)
						parser.add_argument("""--do_eval""", type=bool, default=True)
						parser.add_argument("""--output_dir""", type=str)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					Optional[Any]       =logging.getLogger(__name__)

						logging.basicConfig(
						    level=logging.getLevelName("""INFO"""),
						    handlers=[logging.StreamHandler(sys.stdout)],
						    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
						)

						# Load model and tokenizer
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						_UpperCAmelCase       ,   _UpperCAmelCase     :					Union[str, Any]       =load_dataset("""imdb""", split=["""train""", """test"""])
						_UpperCAmelCase     :					int       =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						_UpperCAmelCase     :					str       =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =train_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					List[Any]       ={
						    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Optional[Any]       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
						    args.per_device_train_batch_size
						)

						# Preprocess test dataset
						_UpperCAmelCase     :					List[Any]       =test_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					Dict       ={
						    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Any       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
						    args.per_device_eval_batch_size
						)

						# fine optimizer and loss
						_UpperCAmelCase     :					List[Any]       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						_UpperCAmelCase     :					str       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						_UpperCAmelCase     :					Optional[Any]       =[tf.keras.metrics.SparseCategoricalAccuracy()]
						model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =time.time() - start_train_time

						logger.info("""*** Train ***""")
						logger.info(f"""train_runtime = {end_train_time}""")
						for key, value in train_results.history.items():
												logger.info(f"""  {key} = {value}""")