





'''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

__UpperCamelCase					    =	logging.get_logger(__name__)




@add_end_docstrings(__lowercase		)
class  _A (					__lowercase		):

		def __init__(   self					:   Tuple  ,      **__magic_name__					:   Union[str, Any]			)					->    List[Any]:


					"""simple docstring"""

					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 	lowercase__     (   self					:   Dict  ,      **__magic_name__					:   Optional[int]			)					->    Optional[int]:


					"""simple docstring"""

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

		def __call__(   self					:   str  ,      __magic_name__					:   Union[str, Any]  ,      *__magic_name__					:   str  ,      __magic_name__					:   List[Any]=None  ,      __magic_name__					:   List[str]=None  ,      **__magic_name__					:   Optional[Any]			)					->    Dict:


					"""simple docstring"""

					return super().__call__(__UpperCamelCase  ,      *__UpperCamelCase  ,      num_workers=__UpperCamelCase  ,      batch_size=__UpperCamelCase  ,      **__UpperCamelCase			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Tuple=64  ,      __magic_name__					:   int = 0  ,      __magic_name__					:   float = 5_12 / 15_00  ,      __magic_name__					:   Optional[int] = 32  ,      __magic_name__					:   Optional[int] = 1  ,      )					->    List[str]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     load_image(__UpperCamelCase			)
					__snake_case					:      Optional[Any]							      =     self.image_processor.size["""longest_edge"""]
					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor.generate_crop_boxes(
					    __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase			)
					__snake_case					:      Optional[int]							      =     self.image_processor(images=__UpperCamelCase  ,      return_tensors="""pt"""			)

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

					__snake_case					:      int							      =     grid_points.shape[1]
					__snake_case					:      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			):
								__snake_case					:      List[Any]							      =     grid_points[:, i : i + points_per_batch, :, :]
								__snake_case					:      Any							      =     input_labels[:, i : i + points_per_batch]
								__snake_case					:      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 	lowercase__     (   self					:   str  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Optional[int]=0.88  ,      __magic_name__					:   List[str]=0.95  ,      __magic_name__					:   str=0  ,      __magic_name__					:   Any=1  ,      )					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Any							      =     model_inputs.pop("""input_boxes"""			)
					__snake_case					:      List[str]							      =     model_inputs.pop("""is_last"""			)
					__snake_case					:      List[str]							      =     model_inputs.pop("""original_sizes"""			).tolist()
					__snake_case					:      Any							      =     model_inputs.pop("""reshaped_input_sizes"""			).tolist()

					__snake_case					:      Any							      =     self.model(**__UpperCamelCase			)

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					__snake_case					:      str							      =     model_outputs["""pred_masks"""]
					__snake_case					:      Tuple							      =     self.image_processor.post_process_masks(
					    __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      binarize=__UpperCamelCase			)
					__snake_case					:      Optional[int]							      =     model_outputs["""iou_scores"""]
					__snake_case      ,     __snake_case      ,     __snake_case					:      Optional[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 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Union[str, Any]=False  ,      __magic_name__					:   Optional[int]=False  ,      __magic_name__					:   str=0.7  ,      )					->    List[str]:


					"""simple docstring"""

					__snake_case					:      int							      =     []
					__snake_case					:      int							      =     []
					__snake_case					:      Optional[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"""			)			)

					__snake_case					:      Union[str, Any]							      =     torch.cat(__UpperCamelCase			)
					__snake_case					:      Dict							      =     torch.cat(__UpperCamelCase			)
					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      str							      =     self.image_processor.post_process_for_mask_generation(
					    __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase  ,      __UpperCamelCase			)

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

					__snake_case					:      Dict							      =     {}
					if output_rle_mask:
								__snake_case					:      int							      =     rle_mask

					if output_bboxes_mask:
								__snake_case					:      Union[str, Any]							      =     bounding_boxes

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




from typing import TYPE_CHECKING

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


lowercase   =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2TokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2FeatureExtractor''']
		lowercase   =	['''LayoutLMv2ImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv2ForQuestionAnswering''',
		    '''LayoutLMv2ForSequenceClassification''',
		    '''LayoutLMv2ForTokenClassification''',
		    '''LayoutLMv2Layer''',
		    '''LayoutLMv2Model''',
		    '''LayoutLMv2PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





