



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 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Dict=8   )    ->			Dict:







			'''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       __lowerCAmelCase   (      A						):



			def __init__(						self       :  Optional[Any]			,		A       :  UNetaDConditionModel			,		A       :  DDPMScheduler			,		A       :  VQModel			,		)       ->   List[str]:



						"""simple docstring"""






						super().__init__()

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



			def    _lowerCamelCase	(						self       :  int			,		A       :  int			,		A       :  Union[str, Any]			,		A       :  Optional[int]			,		A       :  Union[str, Any]			,		A       :  Any			,		A       :  Optional[int])       ->   int:



						"""simple docstring"""






						if latents is None:
									_UpperCAmelCase			=       randn_tensor(A			,		generator=A			,		device=A			,		dtype=A)
						else:
									if latents.shape != shape:
												raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
									_UpperCAmelCase			=       latents.to(A)

						_UpperCAmelCase			=       latents * scheduler.init_noise_sigma
						return latents



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Union[str, Any]=0)       ->   Dict:



						"""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(A			,		A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Optional[int]=0)       ->   Dict:



						"""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=A)
									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(A			,		A			,		prev_module_hook=A)

						# 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       :  Any)       ->   int:



						"""simple docstring"""






						if not hasattr(self.unet			,		'_hf_hook'):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(A			,		'_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(A)
			def __call__(						self       :  List[Any]			,		A       :  Union[torch.FloatTensor, List[torch.FloatTensor]]			,		A       :  Union[torch.FloatTensor, List[torch.FloatTensor]]			,		A       :  torch.FloatTensor			,		A       :  int = 5_12			,		A       :  int = 5_12			,		A       :  int = 1_00			,		A       :  float = 4.0			,		A       :  int = 1			,		A       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		A       :  Optional[torch.FloatTensor] = None			,		A       :  Optional[str] = "pil"			,		A       :  bool = True			,		)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self._execution_device

						_UpperCAmelCase			=       guidance_scale > 1.0

						if isinstance(A			,		A):
									_UpperCAmelCase			=       torch.cat(A			,		dim=0)
						if isinstance(A			,		A):
									_UpperCAmelCase			=       torch.cat(A			,		dim=0)
						if isinstance(A			,		A):
									_UpperCAmelCase			=       torch.cat(A			,		dim=0)

						_UpperCAmelCase			=       image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
									_UpperCAmelCase			=       image_embeds.repeat_interleave(A			,		dim=0)
									_UpperCAmelCase			=       negative_image_embeds.repeat_interleave(A			,		dim=0)
									_UpperCAmelCase			=       hint.repeat_interleave(A			,		dim=0)

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

						_UpperCAmelCase			=       self.movq.config.latent_channels

						_UpperCAmelCase	,					_UpperCAmelCase			=       downscale_height_and_width(A			,		A			,		self.movq_scale_factor)

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

						for i, t in enumerate(self.progress_bar(A)):
									# 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=A			,		timestep=A			,		encoder_hidden_states=A			,		added_cond_kwargs=A			,		return_dict=A			,		)[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(
									    A			,		A			,		A			,		generator=A			,		)[0]
						# post-processing
						_UpperCAmelCase			=       self.movq.decode(A			,		force_not_quantize=A)['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(A)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=A)





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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

						super().__init__(bos_token_id=A			,		eos_token_id=A			,		**A)
