

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_       : int         =			logging.get_logger(__name__)



@add_end_docstrings(__lowerCamelCase				)
class     _a						(__lowerCamelCase				):


					'''simple docstring'''






					def __init__( self    ,       **A__			):
						super().__init__(**A__			)
						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(A__			)






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






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






					def   __A			( self    ,       A__    ,       A__=64    ,       A__ = 0    ,       A__ = 512 / 1500    ,       A__ = 32    ,       A__ = 1    ,       ):
						A__							:    Optional[Any]        =		load_image(A__			)
						A__							:    Optional[Any]        =		self.image_processor.size["""longest_edge"""]
						A__							:    Union[str, Any]        =		self.image_processor.generate_crop_boxes(
						    A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			)
						A__							:    str        =		self.image_processor(images=A__    ,       return_tensors="""pt"""			)

						with self.device_placement():
							if self.framework == "pt":
								A__							:    Dict        =		self.get_inference_context()
								with inference_context():
									A__							:    Optional[Any]        =		self._ensure_tensor_on_device(A__    ,       device=self.device			)
									A__							:    str        =		self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""			)			)
									A__							:    Any        =		image_embeddings

						A__							:    Tuple        =		grid_points.shape[1]
						A__							:    Tuple        =		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    ,       A__    ,       A__			):
							A__							:    List[str]        =		grid_points[:, i : i + points_per_batch, :, :]
							A__							:    Optional[Any]        =		input_labels[:, i : i + points_per_batch]
							A__							:    Optional[int]        =		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   __A			( self    ,       A__    ,       A__=0.8_8    ,       A__=0.9_5    ,       A__=0    ,       A__=1    ,       ):
						A__							:    Any        =		model_inputs.pop("""input_boxes"""			)
						A__							:    List[str]        =		model_inputs.pop("""is_last"""			)
						A__							:    int        =		model_inputs.pop("""original_sizes"""			).tolist()
						A__							:    int        =		model_inputs.pop("""reshaped_input_sizes"""			).tolist()

						A__							:    Optional[Any]        =		self.model(**A__			)

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






					def   __A			( self    ,       A__    ,       A__=False    ,       A__=False    ,       A__=0.7    ,       ):
						A__							:    Optional[Any]        =		[]
						A__							:    Dict        =		[]
						A__							:    Optional[Any]        =		[]
						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__							:    List[Any]        =		torch.cat(A__			)
						A__							:    Any        =		torch.cat(A__			)
						A__							:    Optional[int]        =		self.image_processor.post_process_for_mask_generation(
						    A__    ,       A__    ,       A__    ,       A__			)

						A__							:    Union[str, Any]        =		defaultdict(A__			)
						for output in model_outputs:
							for k, v in output.items():
								extra[k].append(A__			)

						A__							:    str        =		{}
						if output_rle_mask:
							A__							:    Union[str, Any]        =		rle_mask

						if output_bboxes_mask:
							A__							:    Optional[Any]        =		bounding_boxes

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










from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						self.base_model(
             input_ids=A							,				attention_mask=A							,				token_type_ids=A							,				position_ids=A							,				head_mask=A							,				inputs_embeds=A							,				encoder_hidden_states=A							,				encoder_attention_mask=A							,				output_attentions=A							,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,				return_dict=A							,				)

         if self.has_pre_transformation:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						self.transformation_pre(A		)

           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)
         else:
           __snake_case:						Optional[int]   =						self.transformation(outputs.last_hidden_state		)
           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)






