

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class     A__(a_,   unittest.TestCase				):
						"""simple docstring"""






						_A  :							List[str]     =       LDMTextToImagePipeline
						_A  :							int     =       TEXT_TO_IMAGE_PARAMS - {
						    '''negative_prompt''',
						    '''negative_prompt_embeds''',
						    '''cross_attention_kwargs''',
						    '''prompt_embeds''',
						}
						_A  :							Tuple     =       PipelineTesterMixin.required_optional_params - {
						    '''num_images_per_prompt''',
						    '''callback''',
						    '''callback_steps''',
						}
						_A  :							Union[str, Any]     =       TEXT_TO_IMAGE_BATCH_PARAMS
						_A  :							Union[str, Any]     =       False





						def        UpperCamelCase__ (  self  )			->      int:
									torch.manual_seed(0  )
									a_		:		Union[str, Any]      				=      UNetaDConditionModel(
									    block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,	cross_attention_dim=32   ,	)
									a_		:		Dict      				=      DDIMScheduler(
									    beta_start=0.0_0_0_8_5   ,	beta_end=0.0_1_2   ,	beta_schedule="""scaled_linear"""   ,	clip_sample=lowerCamelCase_   ,	set_alpha_to_one=lowerCamelCase_   ,	)
									torch.manual_seed(0  )
									a_		:		List[str]      				=      AutoencoderKL(
									    block_out_channels=(32, 64)   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")   ,	up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")   ,	latent_channels=4   ,	)
									torch.manual_seed(0  )
									a_		:		Optional[int]      				=      CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
									a_		:		Optional[int]      				=      CLIPTextModel(lowerCamelCase_  )
									a_		:		Optional[int]      				=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									a_		:		List[Any]      				=      {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vqvae""": vae,
									    """bert""": text_encoder,
									    """tokenizer""": tokenizer,
									}
									return components





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase=0  )			->      int:

									if str(lowerCamelCase_  ).startswith("""mps"""  ):
												a_		:		Any      				=      torch.manual_seed(lowerCamelCase_  )
									else:
												a_		:		int      				=      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
									a_		:		Optional[Any]      				=      {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs





						def        UpperCamelCase__ (  self  )			->      Any:
									a_		:		str      				=      """cpu"""  # ensure determinism for the device-dependent torch.Generator

									a_		:		int      				=      self.get_dummy_components()
									a_		:		List[str]      				=      LDMTextToImagePipeline(**lowerCamelCase_  )
									pipe.to(lowerCamelCase_  )
									pipe.set_progress_bar_config(disable=lowerCamelCase_  )

									a_		:		int      				=      self.get_dummy_inputs(lowerCamelCase_  )
									a_		:		Any      				=      pipe(**lowerCamelCase_  ).images
									a_		:		List[str]      				=      image[0, -3:, -3:, -1]

									assert image.shape == (1, 16, 16, 3)
									a_		:		Tuple      				=      np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]  )

									assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3







@slow
@require_torch_gpu
class     A__(unittest.TestCase				):
						"""simple docstring"""






						def        UpperCamelCase__ (  self  )			->      int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase=torch.floataa   ,	_lowercase=0  )			->      Optional[int]:
									a_		:		List[Any]      				=      torch.manual_seed(lowerCamelCase_  )
									a_		:		Optional[int]      				=      np.random.RandomState(lowerCamelCase_  ).standard_normal((1, 4, 32, 32)  )
									a_		:		Any      				=      torch.from_numpy(lowerCamelCase_  ).to(device=lowerCamelCase_   ,	dtype=lowerCamelCase_  )
									a_		:		Any      				=      {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs





						def        UpperCamelCase__ (  self  )			->      List[Any]:
									a_		:		Tuple      				=      LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""  ).to(lowerCamelCase_  )
									pipe.set_progress_bar_config(disable=lowerCamelCase_  )

									a_		:		List[Any]      				=      self.get_inputs(lowerCamelCase_  )
									a_		:		Dict      				=      pipe(**lowerCamelCase_  ).images
									a_		:		Optional[int]      				=      image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 256, 256, 3)
									a_		:		List[str]      				=      np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]  )
									a_		:		Any      				=      np.abs(expected_slice - image_slice  ).max()
									assert max_diff < 1e-3







@nightly
@require_torch_gpu
class     A__(unittest.TestCase				):
						"""simple docstring"""






						def        UpperCamelCase__ (  self  )			->      str:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase=torch.floataa   ,	_lowercase=0  )			->      Tuple:
									a_		:		List[Any]      				=      torch.manual_seed(lowerCamelCase_  )
									a_		:		Any      				=      np.random.RandomState(lowerCamelCase_  ).standard_normal((1, 4, 32, 32)  )
									a_		:		List[str]      				=      torch.from_numpy(lowerCamelCase_  ).to(device=lowerCamelCase_   ,	dtype=lowerCamelCase_  )
									a_		:		Any      				=      {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 50,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs





						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									a_		:		Dict      				=      LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""  ).to(lowerCamelCase_  )
									pipe.set_progress_bar_config(disable=lowerCamelCase_  )

									a_		:		Dict      				=      self.get_inputs(lowerCamelCase_  )
									a_		:		Optional[Any]      				=      pipe(**lowerCamelCase_  ).images[0]

									a_		:		Any      				=      load_numpy(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""  )
									a_		:		List[str]      				=      np.abs(expected_image - image  ).max()
									assert max_diff < 1e-3







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}
