

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__								=		logging.get_logger(__name__)







@add_end_docstrings(_lowercase							)
class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		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 		A__					(				self	,       **__snake_case):
			_UpperCamelCase   :		int			=						{}
			_UpperCamelCase   :		Any			=						{}
			_UpperCamelCase   :		Optional[Any]			=						{}
			# preprocess args
			if "points_per_batch" in kwargs:
				_UpperCamelCase   :		Optional[Any]			=						kwargs['points_per_batch']
			if "points_per_crop" in kwargs:
				_UpperCamelCase   :		Optional[Any]			=						kwargs['points_per_crop']
			if "crops_n_layers" in kwargs:
				_UpperCamelCase   :		Any			=						kwargs['crops_n_layers']
			if "crop_overlap_ratio" in kwargs:
				_UpperCamelCase   :		List[str]			=						kwargs['crop_overlap_ratio']
			if "crop_n_points_downscale_factor" in kwargs:
				_UpperCamelCase   :		Optional[Any]			=						kwargs['crop_n_points_downscale_factor']
			# postprocess args
			if "pred_iou_thresh" in kwargs:
				_UpperCamelCase   :		Dict			=						kwargs['pred_iou_thresh']
			if "stability_score_offset" in kwargs:
				_UpperCamelCase   :		Dict			=						kwargs['stability_score_offset']
			if "mask_threshold" in kwargs:
				_UpperCamelCase   :		Optional[int]			=						kwargs['mask_threshold']
			if "stability_score_thresh" in kwargs:
				_UpperCamelCase   :		Optional[int]			=						kwargs['stability_score_thresh']
			if "crops_nms_thresh" in kwargs:
				_UpperCamelCase   :		List[Any]			=						kwargs['crops_nms_thresh']
			if "output_rle_mask" in kwargs:
				_UpperCamelCase   :		List[Any]			=						kwargs['output_rle_mask']
			if "output_bboxes_mask" in kwargs:
				_UpperCamelCase   :		Union[str, Any]			=						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 		A__					(				self	,       __snake_case	,       __snake_case=64	,       __snake_case = 0	,       __snake_case = 5_12 / 15_00	,       __snake_case = 32	,       __snake_case = 1	,       ):
			_UpperCamelCase   :		List[Any]			=						load_image(__snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						self.image_processor.size['longest_edge']
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						self.image_processor.generate_crop_boxes(
			    __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case)
			_UpperCamelCase   :		str			=						self.image_processor(images=__snake_case	,       return_tensors='pt')

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

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

			_UpperCamelCase   :		Optional[int]			=						self.model(**__snake_case)

			# post processing happens here in order to avoid CPU GPU copies of ALL the masks
			_UpperCamelCase   :		Dict			=						model_outputs['pred_masks']
			_UpperCamelCase   :		Union[str, Any]			=						self.image_processor.post_process_masks(
			    __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       binarize=__snake_case)
			_UpperCamelCase   :		List[Any]			=						model_outputs['iou_scores']
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						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 		A__					(				self	,       __snake_case	,       __snake_case=False	,       __snake_case=False	,       __snake_case=0.7	,       ):
			_UpperCamelCase   :		Dict			=						[]
			_UpperCamelCase   :		str			=						[]
			_UpperCamelCase   :		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'))

			_UpperCamelCase   :		Tuple			=						torch.cat(__snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						torch.cat(__snake_case)
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.image_processor.post_process_for_mask_generation(
			    __snake_case	,       __snake_case	,       __snake_case	,       __snake_case)

			_UpperCamelCase   :		Any			=						defaultdict(__snake_case)
			for output in model_outputs:
				for k, v in output.items():
					extra[k].append(__snake_case)

			_UpperCamelCase   :		str			=						{}
			if output_rle_mask:
				_UpperCamelCase   :		List[str]			=						rle_mask

			if output_bboxes_mask:
				_UpperCamelCase   :		Any			=						bounding_boxes

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__								=		{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		["""BlipImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlipModel""",
							    """BlipPreTrainedModel""",
							    """BlipForConditionalGeneration""",
							    """BlipForQuestionAnswering""",
							    """BlipVisionModel""",
							    """BlipTextModel""",
							    """BlipForImageTextRetrieval""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBlipModel""",
							    """TFBlipPreTrainedModel""",
							    """TFBlipForConditionalGeneration""",
							    """TFBlipForQuestionAnswering""",
							    """TFBlipVisionModel""",
							    """TFBlipTextModel""",
							    """TFBlipForImageTextRetrieval""",
							]

if TYPE_CHECKING:
							from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
							from .processing_blip import BlipProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_blip import BlipImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blip import (
														    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlipForConditionalGeneration,
														    BlipForImageTextRetrieval,
														    BlipForQuestionAnswering,
														    BlipModel,
														    BlipPreTrainedModel,
														    BlipTextModel,
														    BlipVisionModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blip import (
														    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBlipForConditionalGeneration,
														    TFBlipForImageTextRetrieval,
														    TFBlipForQuestionAnswering,
														    TFBlipModel,
														    TFBlipPreTrainedModel,
														    TFBlipTextModel,
														    TFBlipVisionModel,
														)

else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




