


"""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    :     Any   						=		logging.get_logger(__name__)





@add_end_docstrings(A				)
class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : List[str] ,       **UpperCamelCase    : 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 lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Tuple  ):
												'''simple docstring'''






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






					def __call__(       self    : Optional[Any] ,       UpperCamelCase    : Tuple ,       *UpperCamelCase    : Optional[int] ,       UpperCamelCase    : Any=None ,       UpperCamelCase    : Optional[Any]=None ,       **UpperCamelCase    : List[str]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : Union[str, Any] ,       UpperCamelCase    : str=64 ,       UpperCamelCase    : int = 0 ,       UpperCamelCase    : float = 512 / 1_500 ,       UpperCamelCase    : Optional[int] = 32 ,       UpperCamelCase    : Optional[int] = 1 ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					load_image(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					self.image_processor.size["""longest_edge"""]
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					self.image_processor.generate_crop_boxes(
												    UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					self.image_processor(images=UpperCamelCase ,       return_tensors="""pt"""  )

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

												__UpperCAmelCase   :						Dict      							=					grid_points.shape[1]
												__UpperCAmelCase   :						Dict      							=					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  ):
																			__UpperCAmelCase   :						List[str]      							=					grid_points[:, i : i + points_per_batch, :, :]
																			__UpperCAmelCase   :						Optional[Any]      							=					input_labels[:, i : i + points_per_batch]
																			__UpperCAmelCase   :						Tuple      							=					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 lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : str ,       UpperCamelCase    : Dict=0.88 ,       UpperCamelCase    : List[Any]=0.95 ,       UpperCamelCase    : List[Any]=0 ,       UpperCamelCase    : List[str]=1 ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					model_inputs.pop("""input_boxes"""  )
												__UpperCAmelCase   :						Tuple      							=					model_inputs.pop("""is_last"""  )
												__UpperCAmelCase   :						Optional[Any]      							=					model_inputs.pop("""original_sizes"""  ).tolist()
												__UpperCAmelCase   :						int      							=					model_inputs.pop("""reshaped_input_sizes"""  ).tolist()

												__UpperCAmelCase   :						Optional[int]      							=					self.model(**UpperCamelCase  )

												# post processing happens here in order to avoid CPU GPU copies of ALL the masks
												__UpperCAmelCase   :						Tuple      							=					model_outputs["""pred_masks"""]
												__UpperCAmelCase   :						str      							=					self.image_processor.post_process_masks(
												    UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       binarize=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					model_outputs["""iou_scores"""]
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						List[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 lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : Dict=False ,       UpperCamelCase    : Tuple=False ,       UpperCamelCase    : Tuple=0.7 ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					[]
												__UpperCAmelCase   :						Dict      							=					[]
												__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(UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					torch.cat(UpperCamelCase  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Optional[int]      							=					self.image_processor.post_process_for_mask_generation(
												    UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  )

												__UpperCAmelCase   :						Dict      							=					defaultdict(UpperCamelCase  )
												for output in model_outputs:
																			for k, v in output.items():
																										extra[k].append(UpperCamelCase  )

												__UpperCAmelCase   :						Optional[int]      							=					{}
												if output_rle_mask:
																			__UpperCAmelCase   :						List[str]      							=					rle_mask

												if output_bboxes_mask:
																			__UpperCAmelCase   :						Optional[int]      							=					bounding_boxes

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




"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							UpperCAmelCase    :     Union[str, Any]   						=		None

UpperCAmelCase    :     Union[str, Any]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase    :     List[str]   						=		{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase    :     Tuple   						=		{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

UpperCAmelCase    :     str   						=		'▁'





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							PRETRAINED_VOCAB_FILES_MAP
					__a	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							["""input_ids""", """attention_mask"""]
					__a	       =							BarthezTokenizer






					def __init__(       self    : Optional[int] ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : Union[str, Any]="<s>" ,       UpperCamelCase    : Any="</s>" ,       UpperCamelCase    : Tuple="</s>" ,       UpperCamelCase    : Tuple="<s>" ,       UpperCamelCase    : int="<unk>" ,       UpperCamelCase    : List[str]="<pad>" ,       UpperCamelCase    : int="<mask>" ,       **UpperCamelCase    : Any ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					AddedToken(UpperCamelCase ,       lstrip=UpperCamelCase ,       rstrip=UpperCamelCase  ) if isinstance(UpperCamelCase ,       UpperCamelCase  ) else mask_token

												super().__init__(
												    UpperCamelCase ,       tokenizer_file=UpperCamelCase ,       bos_token=UpperCamelCase ,       eos_token=UpperCamelCase ,       unk_token=UpperCamelCase ,       sep_token=UpperCamelCase ,       cls_token=UpperCamelCase ,       pad_token=UpperCamelCase ,       mask_token=UpperCamelCase ,       **UpperCamelCase ,       )

												__UpperCAmelCase   :						List[Any]      							=					vocab_file
												__UpperCAmelCase   :						Tuple      							=					False if not self.vocab_file else True






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												__UpperCAmelCase   :						List[Any]      							=					[self.cls_token_id]
												__UpperCAmelCase   :						Tuple      							=					[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[self.sep_token_id]
												__UpperCAmelCase   :						Union[str, Any]      							=					[self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep  ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]






					def lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[str] = None  ):
												'''simple docstring'''






												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																			    """tokenizer."""  )

												if not os.path.isdir(UpperCamelCase  ):
																			logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
																			return
												__UpperCAmelCase   :						List[Any]      							=					os.path.join(
												    UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

												if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase  ):
																			copyfile(self.vocab_file ,       UpperCamelCase  )

												return (out_vocab_file,)
