




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		:				Any       			=		logging.get_logger(__name__)




@add_end_docstrings(_UpperCamelCase   )
class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     List[str]					,      **__lowerCamelCase				:     List[Any]							)					->     List[str]:
							super().__init__(**__lowerCamelCase							)
							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(__lowerCamelCase							)





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





				def __call__(      self				:     Optional[Any]					,      __lowerCamelCase				:     Tuple					,      *__lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     Optional[Any]=None					,      **__lowerCamelCase				:     List[str]							)					->     List[str]:
							return super().__call__(__lowerCamelCase					,      *__lowerCamelCase					,      num_workers=__lowerCamelCase					,      batch_size=__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     str=64					,      __lowerCamelCase				:     int = 0					,      __lowerCamelCase				:     float = 5_12 / 15_00					,      __lowerCamelCase				:     Optional[int] = 32					,      __lowerCamelCase				:     Optional[int] = 1					,      )					->     str:
							a    =			load_image(__lowerCamelCase							)
							a    =			self.image_processor.size["""longest_edge"""]
							a    =			self.image_processor.generate_crop_boxes(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
							a    =			self.image_processor(images=__lowerCamelCase					,      return_tensors="pt"							)

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

							a    =			grid_points.shape[1]
							a    =			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					,      __lowerCamelCase					,      __lowerCamelCase							):
										a    =			grid_points[:, i : i + points_per_batch, :, :]
										a    =			input_labels[:, i : i + points_per_batch]
										a    =			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 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Dict=0.88					,      __lowerCamelCase				:     List[Any]=0.95					,      __lowerCamelCase				:     List[Any]=0					,      __lowerCamelCase				:     List[str]=1					,      )					->     Optional[int]:
							a    =			model_inputs.pop("input_boxes"							)
							a    =			model_inputs.pop("is_last"							)
							a    =			model_inputs.pop("original_sizes"							).tolist()
							a    =			model_inputs.pop("reshaped_input_sizes"							).tolist()

							a    =			self.model(**__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict=False					,      __lowerCamelCase				:     Tuple=False					,      __lowerCamelCase				:     Tuple=0.7					,      )					->     int:
							a    =			[]
							a    =			[]
							a    =			[]
							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    =			torch.cat(__lowerCamelCase							)
							a    =			torch.cat(__lowerCamelCase							)
							a    =			self.image_processor.post_process_for_mask_generation(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)

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

							a    =			{}
							if output_rle_mask:
										a    =			rle_mask

							if output_bboxes_mask:
										a    =			bounding_boxes

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










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase    :     Any   						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
