


'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__    					=      {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


UpperCamelCase__    					=      {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}




class 			lowerCamelCase_   (	_UpperCAmelCase      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     SqueezeBertTokenizer






      def __init__(					self				:    Dict						,						_A				:    int=None						,						_A				:    List[Any]=None						,						_A				:    Optional[Any]=True						,						_A				:    int="[UNK]"						,						_A				:    str="[SEP]"						,						_A				:    List[str]="[PAD]"						,						_A				:    List[Any]="[CLS]"						,						_A				:    Optional[int]="[MASK]"						,						_A				:    int=True						,						_A				:    Any=None						,						**_A				:    Optional[int]						,						):


          '''simple docstring'''


          super().__init__(
              _UpperCAmelCase						,						tokenizer_file=_UpperCAmelCase						,						do_lower_case=_UpperCAmelCase						,						unk_token=_UpperCAmelCase						,						sep_token=_UpperCAmelCase						,						pad_token=_UpperCAmelCase						,						cls_token=_UpperCAmelCase						,						mask_token=_UpperCAmelCase						,						tokenize_chinese_chars=_UpperCAmelCase						,						strip_accents=_UpperCAmelCase						,						**_UpperCAmelCase						,						)

          UpperCAmelCase__			:		int													=				json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get('''lowercase'''						,						_UpperCAmelCase ) != do_lower_case
              or normalizer_state.get('''strip_accents'''						,						_UpperCAmelCase ) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''						,						_UpperCAmelCase ) != tokenize_chinese_chars
          ):
              UpperCAmelCase__			:		str													=				getattr(_UpperCAmelCase						,						normalizer_state.pop('''type''' ) )
              UpperCAmelCase__			:		List[str]													=				do_lower_case
              UpperCAmelCase__			:		Optional[Any]													=				strip_accents
              UpperCAmelCase__			:		List[Any]													=				tokenize_chinese_chars
              UpperCAmelCase__			:		Any													=				normalizer_class(**_UpperCAmelCase )

          UpperCAmelCase__			:		Dict													=				do_lower_case






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    Dict						,						_A				:    Dict=None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str						,						_A				:    Optional[Any] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				[self.sep_token_id]
          UpperCAmelCase__			:		int													=				[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 ) * [0] + len(token_ids_a + sep ) * [1]






      def lowercase_							(					self				:    Tuple						,						_A				:    Tuple						,						_A				:    List[Any] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self._tokenizer.model.save(_UpperCAmelCase						,						name=_UpperCAmelCase )
          return tuple(_UpperCAmelCase )

"""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

__A			   = logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase  )
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			**_UpperCAmelCase							):
       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 _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      {}
       lowercase__:   List[Any]					=      {}
       lowercase__:   Dict					=      {}
       # preprocess args
       if "points_per_batch" in kwargs:
          lowercase__:   Dict					=      kwargs['''points_per_batch''']
       if "points_per_crop" in kwargs:
          lowercase__:   Any					=      kwargs['''points_per_crop''']
       if "crops_n_layers" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crops_n_layers''']
       if "crop_overlap_ratio" in kwargs:
          lowercase__:   Optional[Any]					=      kwargs['''crop_overlap_ratio''']
       if "crop_n_points_downscale_factor" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crop_n_points_downscale_factor''']
       # postprocess args
       if "pred_iou_thresh" in kwargs:
          lowercase__:   Any					=      kwargs['''pred_iou_thresh''']
       if "stability_score_offset" in kwargs:
          lowercase__:   Tuple					=      kwargs['''stability_score_offset''']
       if "mask_threshold" in kwargs:
          lowercase__:   List[str]					=      kwargs['''mask_threshold''']
       if "stability_score_thresh" in kwargs:
          lowercase__:   str					=      kwargs['''stability_score_thresh''']
       if "crops_nms_thresh" in kwargs:
          lowercase__:   List[str]					=      kwargs['''crops_nms_thresh''']
       if "output_rle_mask" in kwargs:
          lowercase__:   Dict					=      kwargs['''output_rle_mask''']
       if "output_bboxes_mask" in kwargs:
          lowercase__:   int					=      kwargs['''output_bboxes_mask''']
       return preprocess_kwargs, forward_params, postprocess_kwargs






    def __call__(   self    ,			_UpperCAmelCase    ,			*_UpperCAmelCase    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase							):
       return super().__call__(_UpperCAmelCase    ,			*_UpperCAmelCase    ,			num_workers=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=64    ,			_UpperCAmelCase = 0    ,			_UpperCAmelCase = 512 / 1500    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 1    ,			):
       lowercase__:   Union[str, Any]					=      load_image(_UpperCAmelCase							)
       lowercase__:   Dict					=      self.image_processor.size['''longest_edge''']
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      self.image_processor.generate_crop_boxes(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      self.image_processor(images=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       with self.device_placement():
          if self.framework == "pt":
             lowercase__:   Tuple					=      self.get_inference_context()
             with inference_context():
                lowercase__:   Optional[Any]					=      self._ensure_tensor_on_device(_UpperCAmelCase    ,			device=self.device							)
                lowercase__:   Any					=      self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''							)							)
                lowercase__:   Tuple					=      image_embeddings

       lowercase__:   Optional[Any]					=      grid_points.shape[1]
       lowercase__:   Tuple					=      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							):
          lowercase__:   Dict					=      grid_points[:, i : i + points_per_batch, :, :]
          lowercase__:   int					=      input_labels[:, i : i + points_per_batch]
          lowercase__:   Any					=      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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=0.88    ,			_UpperCAmelCase=0.95    ,			_UpperCAmelCase=0    ,			_UpperCAmelCase=1    ,			):
       lowercase__:   List[Any]					=      model_inputs.pop('''input_boxes'''							)
       lowercase__:   List[Any]					=      model_inputs.pop('''is_last'''							)
       lowercase__:   Any					=      model_inputs.pop('''original_sizes'''							).tolist()
       lowercase__:   Union[str, Any]					=      model_inputs.pop('''reshaped_input_sizes'''							).tolist()

       lowercase__:   List[Any]					=      self.model(**_UpperCAmelCase							)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       lowercase__:   int					=      model_outputs['''pred_masks''']
       lowercase__:   str					=      self.image_processor.post_process_masks(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			binarize=_UpperCAmelCase							)
       lowercase__:   str					=      model_outputs['''iou_scores''']
       lowercase__, lowercase__, lowercase__:   Optional[int]					=      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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=0.7    ,			):
       lowercase__:   int					=      []
       lowercase__:   str					=      []
       lowercase__:   List[Any]					=      []
       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'''							)							)

       lowercase__:   Any					=      torch.cat(_UpperCAmelCase							)
       lowercase__:   Dict					=      torch.cat(_UpperCAmelCase							)
       lowercase__, lowercase__, lowercase__, lowercase__:   Any					=      self.image_processor.post_process_for_mask_generation(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      defaultdict(_UpperCAmelCase							)
       for output in model_outputs:
          for k, v in output.items():
             extra[k].append(_UpperCAmelCase							)

       lowercase__:   Any					=      {}
       if output_rle_mask:
          lowercase__:   Optional[Any]					=      rle_mask

       if output_bboxes_mask:
          lowercase__:   Optional[int]					=      bounding_boxes

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