


'''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

_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)



@add_end_docstrings(a							)
class  lowerCAmelCase				(				a							):



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



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



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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=64 ,					snake_case__ = 0 ,					snake_case__ = 512 / 1500 ,					snake_case__ = 32 ,					snake_case__ = 1 ,					):
		lowerCAmelCase					:    List[Any]      							= load_image(snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= self.image_processor.size['longest_edge']
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Tuple      							= self.image_processor.generate_crop_boxes(
		    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Any      							= self.image_processor(images=snake_case__ ,					return_tensors='pt'    )

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

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

		lowerCAmelCase					:    List[Any]      							= self.model(**snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=False ,					snake_case__=False ,					snake_case__=0.7 ,					):
		lowerCAmelCase					:    int      							= []
		lowerCAmelCase					:    int      							= []
		lowerCAmelCase					:    int      							= []
		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'    )    )

		lowerCAmelCase					:    Union[str, Any]      							= torch.cat(snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= torch.cat(snake_case__    )
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= self.image_processor.post_process_for_mask_generation(
		    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )

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

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

		if output_bboxes_mask:
			lowerCAmelCase					:    Optional[int]      							= bounding_boxes

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




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :		Tuple		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					int			      = """xmod"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					snake_case__=False ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=True ,					snake_case__=("en_XX",) ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

		lowerCAmelCase					:    Dict      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    List[Any]      							= initializer_range
		lowerCAmelCase					:    Any      							= layer_norm_eps
		lowerCAmelCase					:    Dict      							= position_embedding_type
		lowerCAmelCase					:    Optional[Any]      							= use_cache
		lowerCAmelCase					:    Union[str, Any]      							= classifier_dropout
		lowerCAmelCase					:    int      							= pre_norm
		lowerCAmelCase					:    Optional[Any]      							= adapter_reduction_factor
		lowerCAmelCase					:    Any      							= adapter_layer_norm
		lowerCAmelCase					:    Dict      							= adapter_reuse_layer_norm
		lowerCAmelCase					:    Any      							= ln_before_adapter
		lowerCAmelCase					:    Optional[Any]      							= list(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= default_language



class  lowerCAmelCase				(				a							):



	@property
	def 				lowercase     (					self    ):
		if self.task == "multiple-choice":
			lowerCAmelCase					:    List[Any]      							= {0: 'batch', 1: 'choice', 2: 'sequence'}
		else:
			lowerCAmelCase					:    Optional[int]      							= {0: 'batch', 1: 'sequence'}
		return OrderedDict(
		    [
		        ('input_ids', dynamic_axis),
		        ('attention_mask', dynamic_axis),
		    ]    )
