"""simple docstring"""






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		,     **_UpperCAmelCase       ):
									super().__init__(**_UpperCAmelCase       )
									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(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									__a	:  Dict         =					{}
									__a	:  List[str]         =					{}
									__a	:  Dict         =					{}
									# preprocess args
									if "points_per_batch" in kwargs:
													__a	:  List[str]         =					kwargs['''points_per_batch''']
									if "points_per_crop" in kwargs:
													__a	:  List[str]         =					kwargs['''points_per_crop''']
									if "crops_n_layers" in kwargs:
													__a	:  Dict         =					kwargs['''crops_n_layers''']
									if "crop_overlap_ratio" in kwargs:
													__a	:  Optional[int]         =					kwargs['''crop_overlap_ratio''']
									if "crop_n_points_downscale_factor" in kwargs:
													__a	:  str         =					kwargs['''crop_n_points_downscale_factor''']
									# postprocess args
									if "pred_iou_thresh" in kwargs:
													__a	:  List[str]         =					kwargs['''pred_iou_thresh''']
									if "stability_score_offset" in kwargs:
													__a	:  str         =					kwargs['''stability_score_offset''']
									if "mask_threshold" in kwargs:
													__a	:  Union[str, Any]         =					kwargs['''mask_threshold''']
									if "stability_score_thresh" in kwargs:
													__a	:  Tuple         =					kwargs['''stability_score_thresh''']
									if "crops_nms_thresh" in kwargs:
													__a	:  Tuple         =					kwargs['''crops_nms_thresh''']
									if "output_rle_mask" in kwargs:
													__a	:  Tuple         =					kwargs['''output_rle_mask''']
									if "output_bboxes_mask" in kwargs:
													__a	:  List[str]         =					kwargs['''output_bboxes_mask''']
									return preprocess_kwargs, forward_params, postprocess_kwargs







					def __call__(  self		,     _UpperCAmelCase		,     *_UpperCAmelCase		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									return super().__call__(_UpperCAmelCase		,     *_UpperCAmelCase		,     num_workers=_UpperCAmelCase		,     batch_size=_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=64		,     _UpperCAmelCase = 0		,     _UpperCAmelCase = 512 / 1500		,     _UpperCAmelCase = 32		,     _UpperCAmelCase = 1		,     ):
									__a	:  Any         =					load_image(_UpperCAmelCase       )
									__a	:  Dict         =					self.image_processor.size['''longest_edge''']
									__a	:  Tuple         =					self.image_processor.generate_crop_boxes(
									    _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       )
									__a	:  Optional[Any]         =					self.image_processor(images=_UpperCAmelCase		,     return_tensors='''pt'''       )

									with self.device_placement():
													if self.framework == "pt":
																	__a	:  Tuple         =					self.get_inference_context()
																	with inference_context():
																					__a	:  Optional[int]         =					self._ensure_tensor_on_device(_UpperCAmelCase		,     device=self.device       )
																					__a	:  Tuple         =					self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''       )       )
																					__a	:  Any         =					image_embeddings

									__a	:  List[Any]         =					grid_points.shape[1]
									__a	:  List[str]         =					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		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
													__a	:  Dict         =					grid_points[:, i : i + points_per_batch, :, :]
													__a	:  Any         =					input_labels[:, i : i + points_per_batch]
													__a	:  Tuple         =					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 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=0.8_8		,     _UpperCAmelCase=0.9_5		,     _UpperCAmelCase=0		,     _UpperCAmelCase=1		,     ):
									__a	:  Dict         =					model_inputs.pop('''input_boxes'''       )
									__a	:  List[Any]         =					model_inputs.pop('''is_last'''       )
									__a	:  int         =					model_inputs.pop('''original_sizes'''       ).tolist()
									__a	:  Dict         =					model_inputs.pop('''reshaped_input_sizes'''       ).tolist()

									__a	:  Tuple         =					self.model(**_UpperCAmelCase       )

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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=False		,     _UpperCAmelCase=False		,     _UpperCAmelCase=0.7		,     ):
									__a	:  List[str]         =					[]
									__a	:  Union[str, Any]         =					[]
									__a	:  str         =					[]
									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	:  Optional[int]         =					torch.cat(_UpperCAmelCase       )
									__a	:  int         =					torch.cat(_UpperCAmelCase       )
									__a	:  Optional[int]         =					self.image_processor.post_process_for_mask_generation(
									    _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       )

									__a	:  List[Any]         =					defaultdict(_UpperCAmelCase       )
									for output in model_outputs:
													for k, v in output.items():
																	extra[k].append(_UpperCAmelCase       )

									__a	:  List[Any]         =					{}
									if output_rle_mask:
													__a	:  Tuple         =					rle_mask

									if output_bboxes_mask:
													__a	:  List[Any]         =					bounding_boxes

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






"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
		from PIL import Image




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
					    '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
					    '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
					)
					__lowerCAmelCase					=					'''CIDAS/clipseg-rd64-refined'''
					__lowerCAmelCase					=					'''image_segmenter'''
					__lowerCAmelCase					=					CLIPSegForImageSegmentation

					__lowerCAmelCase					=					['''image''', '''text''']
					__lowerCAmelCase					=					['''image''']







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									requires_backends(self		,     ['''vision''']       )
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					outputs.cpu().detach().numpy()
									__a	:  int         =					0
									__a	:  Optional[int]         =					1
									return Image.fromarray((array * 255).astype(np.uinta       )       )