



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							KandinskyVaaPriorPipeline
	__A							=							["prompt"]
	__A							=							["prompt", "negative_prompt"]
	__A							=							[
	    "num_images_per_prompt",
	    "generator",
	    "num_inference_steps",
	    "latents",
	    "negative_prompt",
	    "guidance_scale",
	    "output_type",
	    "return_dict",
	]
	__A							=							False




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 32




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 32




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.time_input_dim




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.time_input_dim * 4




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 100




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
						return tokenizer




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)
						a					     =   CLIPTextConfig(
						    bos_token_id=0		,       eos_token_id=2		,       hidden_size=self.text_embedder_hidden_size		,       projection_dim=self.text_embedder_hidden_size		,       intermediate_size=37		,       layer_norm_eps=1E-0_5		,       num_attention_heads=4		,       num_hidden_layers=5		,       pad_token_id=1		,       vocab_size=1000		,       )
						return CLIPTextModelWithProjection(lowerCamelCase_		)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)

						a					     =   {
						    "num_attention_heads": 2,
						    "attention_head_dim": 12,
						    "embedding_dim": self.text_embedder_hidden_size,
						    "num_layers": 1,
						}

						a					     =   PriorTransformer(**lowerCamelCase_		)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						a					     =   nn.Parameter(torch.ones(model.clip_std.shape		)		)
						return model




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)
						a					     =   CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,       image_size=224		,       projection_dim=self.text_embedder_hidden_size		,       intermediate_size=37		,       num_attention_heads=4		,       num_channels=3		,       num_hidden_layers=5		,       patch_size=14		,       )

						a					     =   CLIPVisionModelWithProjection(lowerCamelCase_		)
						return model




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   CLIPImageProcessor(
						    crop_size=224		,       do_center_crop=lowerCamelCase_		,       do_normalize=lowerCamelCase_		,       do_resize=lowerCamelCase_		,       image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,       image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,       resample=3		,       size=224		,       )

						return image_processor




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.dummy_prior
						a					     =   self.dummy_image_encoder
						a					     =   self.dummy_text_encoder
						a					     =   self.dummy_tokenizer
						a					     =   self.dummy_image_processor

						a					     =   UnCLIPScheduler(
						    variance_type="fixed_small_log"		,       prediction_type="sample"		,       num_train_timesteps=1000		,       clip_sample=lowerCamelCase_		,       clip_sample_range=10.0		,       )

						a					     =   {
						    "prior": prior,
						    "image_encoder": image_encoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "scheduler": scheduler,
						    "image_processor": image_processor,
						}

						return components




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0		):
						"""simple docstring"""

						if str(lowerCamelCase_		).startswith("mps"		):
											a					     =   torch.manual_seed(lowerCamelCase_		)
						else:
											a					     =   torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
						a					     =   {
						    "prompt": "horse",
						    "generator": generator,
						    "guidance_scale": 4.0,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}
						return inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "cpu"

						a					     =   self.get_dummy_components()

						a					     =   self.pipeline_class(**lowerCamelCase_		)
						a					     =   pipe.to(lowerCamelCase_		)

						pipe.set_progress_bar_config(disable=lowerCamelCase_		)

						a					     =   pipe(**self.get_dummy_inputs(lowerCamelCase_		)		)
						a					     =   output.image_embeds

						a					     =   pipe(
						    **self.get_dummy_inputs(lowerCamelCase_		)		,       return_dict=lowerCamelCase_		,       )[0]

						a					     =   image[0, -10:]
						a					     =   image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						a					     =   np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]		)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2




	@skip_mps
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   torch_device == "cpu"
						a					     =   True
						a					     =   False

						self._test_inference_batch_single_identical(
						    test_max_difference=lowerCamelCase_		,       relax_max_difference=lowerCamelCase_		,       test_mean_pixel_difference=lowerCamelCase_		,       )




	@skip_mps
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   torch_device == "cpu"
						a					     =   False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=lowerCamelCase_		,       test_mean_pixel_difference=lowerCamelCase_		,       )





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						requires_backends(self		,       "torch"		)

						if self.framework != "pt":
											raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''		)

						self.check_model_type(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   points_per_batch if points_per_batch is not None else n_points

						if points_per_batch <= 0:
											raise ValueError(
											    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
											    "To return all points at once, set points_per_batch to None"		)

						for i in range(0		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   i == n_points - points_per_batch
											yield {
											    "input_points": batched_points,
											    "input_labels": labels,
											    "input_boxes": crop_boxes,
											    "is_last": is_last,
											    **model_inputs,
											}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   self.image_processor.filter_masks(
						    masks[0]		,       iou_scores[0]		,       original_sizes[0]		,       input_boxes[0]		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       )
						return {
						    "masks": masks,
						    "is_last": is_last,
						    "boxes": boxes,
						    "iou_scores": iou_scores,
						}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						for model_output in model_outputs:
											all_scores.append(model_output.pop("iou_scores"		)		)
											all_masks.extend(model_output.pop("masks"		)		)
											all_boxes.append(model_output.pop("boxes"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

						return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
