
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

__a         =   logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase				)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     Dict			,				**SCREAMING_SNAKE_CASE_    :     Any			)			->	int:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			,				**SCREAMING_SNAKE_CASE_    :     int			)			->	int:
					lowercase_	    =   {}
					lowercase_	    =   {}
					lowercase_	    =   {}
					# preprocess args
					if "points_per_batch" in kwargs:
									lowercase_	    =   kwargs['''points_per_batch''']
					if "points_per_crop" in kwargs:
									lowercase_	    =   kwargs['''points_per_crop''']
					if "crops_n_layers" in kwargs:
									lowercase_	    =   kwargs['''crops_n_layers''']
					if "crop_overlap_ratio" in kwargs:
									lowercase_	    =   kwargs['''crop_overlap_ratio''']
					if "crop_n_points_downscale_factor" in kwargs:
									lowercase_	    =   kwargs['''crop_n_points_downscale_factor''']
					# postprocess args
					if "pred_iou_thresh" in kwargs:
									lowercase_	    =   kwargs['''pred_iou_thresh''']
					if "stability_score_offset" in kwargs:
									lowercase_	    =   kwargs['''stability_score_offset''']
					if "mask_threshold" in kwargs:
									lowercase_	    =   kwargs['''mask_threshold''']
					if "stability_score_thresh" in kwargs:
									lowercase_	    =   kwargs['''stability_score_thresh''']
					if "crops_nms_thresh" in kwargs:
									lowercase_	    =   kwargs['''crops_nms_thresh''']
					if "output_rle_mask" in kwargs:
									lowercase_	    =   kwargs['''output_rle_mask''']
					if "output_bboxes_mask" in kwargs:
									lowercase_	    =   kwargs['''output_bboxes_mask''']
					return preprocess_kwargs, forward_params, postprocess_kwargs
	def __call__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				*SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Any=None			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Tuple:
					return super().__call__(SCREAMING_SNAKE_CASE_			,				*SCREAMING_SNAKE_CASE_			,				num_workers=SCREAMING_SNAKE_CASE_			,				batch_size=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=6_4			,				SCREAMING_SNAKE_CASE_    :     int = 0			,				SCREAMING_SNAKE_CASE_    :     float = 5_1_2 / 1_5_0_0			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = 3_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = 1			,				)			->	Tuple:
					lowercase_	    =   load_image(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.image_processor.size['''longest_edge''']
					lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   self.image_processor.generate_crop_boxes(
					    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.image_processor(images=SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			)

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

					lowercase_	    =   grid_points.shape[1]
					lowercase_	    =   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			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   grid_points[:, i : i + points_per_batch, :, :]
									lowercase_	    =   input_labels[:, i : i + points_per_batch]
									lowercase_	    =   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 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.88			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.95			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1			,				)			->	Optional[int]:
					lowercase_	    =   model_inputs.pop('''input_boxes'''			)
					lowercase_	    =   model_inputs.pop('''is_last'''			)
					lowercase_	    =   model_inputs.pop('''original_sizes'''			).tolist()
					lowercase_	    =   model_inputs.pop('''reshaped_input_sizes'''			).tolist()

					lowercase_	    =   self.model(**SCREAMING_SNAKE_CASE_			)

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




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[str]=False			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     List[str]=0.7			,				)			->	Optional[int]:
					lowercase_	    =   []
					lowercase_	    =   []
					lowercase_	    =   []
					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'''			)			)

					lowercase_	    =   torch.cat(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.cat(SCREAMING_SNAKE_CASE_			)
					lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   self.image_processor.post_process_for_mask_generation(
					    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

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

					lowercase_	    =   {}
					if output_rle_mask:
									lowercase_	    =   rle_mask

					if output_bboxes_mask:
									lowercase_	    =   bounding_boxes

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












"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def 				lowercase__		(					lowerCamelCase       ):
				return EnvironmentCommand()






class _lowerCAmelCase						(    __UpperCAmelCase       ):







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    parser.add_parser('env'							)
										download_parser.set_defaults(func=lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		int       		=    huggingface_hub.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'not installed'
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'NA'
										if is_torch_available():
														import torch

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.__version__
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.is_available()

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'not installed'
										if is_transformers_available():
														import transformers

														_SCREAMING_SNAKE_CASE   :		Tuple       		=    transformers.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'not installed'
										if is_accelerate_available():
														import accelerate

														_SCREAMING_SNAKE_CASE   :		str       		=    accelerate.__version__

										_SCREAMING_SNAKE_CASE   :		str       		=    'not installed'
										if is_xformers_available():
														import xformers

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xformers.__version__

										_SCREAMING_SNAKE_CASE   :		Any       		=    {
										    '`diffusers` version': version,
										    'Platform': platform.platform(),
										    'Python version': platform.python_version(),
										    'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
										    'Huggingface_hub version': hub_version,
										    'Transformers version': transformers_version,
										    'Accelerate version': accelerate_version,
										    'xFormers version': xformers_version,
										    'Using GPU in script?': '<fill in>',
										    'Using distributed or parallel set-up in script?': '<fill in>',
										}

										print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'							)
										print(self.format_dict(lowerCAmelCase_							)							)

										return info







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"

