



import functools
import operator

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


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    "sew-d"
       def __init__(     self,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=768,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=3072,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=256,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=("p2c", "c2p"),     SCREAMING_SNAKE_CASE_="layer_norm",     SCREAMING_SNAKE_CASE_="gelu_python",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=1e-7,     SCREAMING_SNAKE_CASE_=1e-5,     SCREAMING_SNAKE_CASE_="group",     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),     SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),     SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=128,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=0.05,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_="mean",     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=256,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=2,     **SCREAMING_SNAKE_CASE_,     )	->						Tuple:
          super().__init__(**SCREAMING_SNAKE_CASE_,     pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      hidden_size
          UpperCamelCase       :    int		      =      feat_extract_norm
          UpperCamelCase       :    Any		      =      feat_extract_activation
          UpperCamelCase       :    List[str]		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Dict		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      conv_bias
          UpperCamelCase       :    List[Any]		      =      num_conv_pos_embeddings
          UpperCamelCase       :    Tuple		      =      num_conv_pos_embedding_groups
          UpperCamelCase       :    Dict		      =      len(self.conv_dim			)
          UpperCamelCase       :    List[str]		      =      num_hidden_layers
          UpperCamelCase       :    List[str]		      =      intermediate_size
          UpperCamelCase       :    List[Any]		      =      squeeze_factor
          UpperCamelCase       :    List[str]		      =      max_position_embeddings
          UpperCamelCase       :    Optional[Any]		      =      position_buckets
          UpperCamelCase       :    Optional[int]		      =      share_att_key
          UpperCamelCase       :    List[str]		      =      relative_attention
          UpperCamelCase       :    List[str]		      =      norm_rel_ebd
          UpperCamelCase       :    List[Any]		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      hidden_act
          UpperCamelCase       :    int		      =      num_attention_heads
          UpperCamelCase       :    Tuple		      =      hidden_dropout
          UpperCamelCase       :    Optional[int]		      =      attention_dropout
          UpperCamelCase       :    Dict		      =      activation_dropout
          UpperCamelCase       :    Union[str, Any]		      =      feat_proj_dropout
          UpperCamelCase       :    Tuple		      =      final_dropout
          UpperCamelCase       :    Union[str, Any]		      =      layer_norm_eps
          UpperCamelCase       :    Tuple		      =      feature_layer_norm_eps
          UpperCamelCase       :    Dict		      =      initializer_range
          UpperCamelCase       :    Optional[Any]		      =      vocab_size

          if (
              (len(self.conv_stride			) != self.num_feat_extract_layers)
              or (len(self.conv_kernel			) != self.num_feat_extract_layers)
              or (len(self.conv_dim			) != self.num_feat_extract_layers)
          ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect.'
                 'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                 F"""but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)"""
                 F"""= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`."""			)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          UpperCamelCase       :    List[Any]		      =      apply_spec_augment
          UpperCamelCase       :    str		      =      mask_time_prob
          UpperCamelCase       :    Union[str, Any]		      =      mask_time_length
          UpperCamelCase       :    Tuple		      =      mask_time_min_masks
          UpperCamelCase       :    List[Any]		      =      mask_feature_prob
          UpperCamelCase       :    List[Any]		      =      mask_feature_length
          UpperCamelCase       :    Dict		      =      mask_feature_min_masks

          # ctc loss
          UpperCamelCase       :    int		      =      ctc_loss_reduction
          UpperCamelCase       :    Tuple		      =      ctc_zero_infinity

          # sequence classification
          UpperCamelCase       :    str		      =      use_weighted_layer_sum
          UpperCamelCase       :    Optional[Any]		      =      classifier_proj_size






       @property
       def    snake_case_			(     self			)	->						List[str]:
          return functools.reduce(operator.mul,     self.conv_stride,     1			)





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,
)


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

A		: Optional[Any]								=     '''
    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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	int=8       )  ->     Optional[int]:
						_lowercase					       =					height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_lowercase					       =					width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



class    a_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			):
									super().__init__()

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									if latents is None:
															_lowercase					       =					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}"""  )
															_lowercase					       =					latents.to(__UpperCamelCase  )

									_lowercase					       =					latents * scheduler.init_noise_sigma
									return latents






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

									_lowercase					       =					torch.device(f"""cuda:{gpu_id}"""  )

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=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."""  )

									_lowercase					       =					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)

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

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






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def    UpperCamelCase_				(      self  ):
									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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = 512				,			__UpperCamelCase = 512				,			__UpperCamelCase = 100				,			__UpperCamelCase = 4.0				,			__UpperCamelCase = 1				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = "pil"				,			__UpperCamelCase = True				,			):
									_lowercase					       =					self._execution_device

									_lowercase					       =					guidance_scale > 1.0

									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
															_lowercase					       =					image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					negative_image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					hint.repeat_interleave(__UpperCamelCase				,			dim=0  )

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

									_lowercase					       =					self.movq.config.latent_channels

									_lowercase       ,						_lowercase					       =					downscale_height_and_width(__UpperCamelCase				,			__UpperCamelCase				,			self.movq_scale_factor  )

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

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

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

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

									if output_type == "pil":
															_lowercase					       =					self.numpy_to_pil(__UpperCamelCase  )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=__UpperCamelCase  )