

from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCamelCase           =					_symbol_database.Default()


lowerCamelCase           =					_descriptor_pool.Default().AddSerializedFile(
    b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

lowerCamelCase           =					globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
       lowerCamelCase           =					None
       lowerCamelCase           =					b'''H\003'''
       # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
       # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
       # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
       # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
       # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
       lowerCamelCase           =					45
       lowerCamelCase           =					1581
       lowerCamelCase           =					1517
       lowerCamelCase           =					1570
       lowerCamelCase           =					1584
       lowerCamelCase           =					1793
       lowerCamelCase           =					1795
       lowerCamelCase           =					1916
       lowerCamelCase           =					1864
       lowerCamelCase           =					1905
       lowerCamelCase           =					1919
       lowerCamelCase           =					2429
       lowerCamelCase           =					2208
       lowerCamelCase           =					2418
       lowerCamelCase           =					2323
       lowerCamelCase           =					2407
# @@protoc_insertion_point(module_scope)



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(lowerCamelCase__							)
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    **lowercase_       )							->    Optional[int]:






     """simple docstring"""






     super().__init__(**lowercase_       )
     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(lowercase_       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     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,    lowercase_,    *lowercase_,    lowercase_=None,    lowercase_=None,    **lowercase_       )							->    List[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=64,    lowercase_ = 0,    lowercase_ = 512 / 1500,    lowercase_ = 32,    lowercase_ = 1,    )							->    Any:






     """simple docstring"""






     a__  =load_image(lowercase_       )
     a__  =self.image_processor.size['''longest_edge''']
     a__,      a__,      a__,      a__  =self.image_processor.generate_crop_boxes(
         lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )
     a__  =self.image_processor(images=lowercase_,    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(lowercase_,    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,    lowercase_,    lowercase_       ):
       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,    lowercase_,    lowercase_=0.88,    lowercase_=0.95,    lowercase_=0,    lowercase_=1,    )							->    int:






     """simple docstring"""






     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(**lowercase_       )

     # 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(
         lowercase_,    lowercase_,    lowercase_,    lowercase_,    binarize=lowercase_       )
     a__  =model_outputs['''iou_scores''']
     a__,      a__,      a__  =self.image_processor.filter_masks(
         masks[0],    iou_scores[0],    original_sizes[0],    input_boxes[0],    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )
     return {
         "masks": masks,
         "is_last": is_last,
         "boxes": boxes,
         "iou_scores": iou_scores,
     }



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=False,    lowercase_=False,    lowercase_=0.7,    )							->    Any:






     """simple docstring"""






     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(lowercase_       )
     a__  =torch.cat(lowercase_       )
     a__,      a__,      a__,      a__  =self.image_processor.post_process_for_mask_generation(
         lowercase_,    lowercase_,    lowercase_,    lowercase_       )

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

     a__  ={}
     if output_rle_mask:
       a__  =rle_mask

     if output_bboxes_mask:
       a__  =bounding_boxes

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