




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       _A				(		UpperCamelCase__     ):






  def __init__(    self						,		**_SCREAMING_SNAKE_CASE			):
        super().__init__(**__A			)
        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(__A			)






  def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
        _UpperCAmelCase      =			{}
        _UpperCAmelCase      =			{}
        _UpperCAmelCase      =			{}
        # preprocess args
        if "points_per_batch" in kwargs:
              _UpperCAmelCase      =			kwargs["""points_per_batch"""]
        if "points_per_crop" in kwargs:
              _UpperCAmelCase      =			kwargs["""points_per_crop"""]
        if "crops_n_layers" in kwargs:
              _UpperCAmelCase      =			kwargs["""crops_n_layers"""]
        if "crop_overlap_ratio" in kwargs:
              _UpperCAmelCase      =			kwargs["""crop_overlap_ratio"""]
        if "crop_n_points_downscale_factor" in kwargs:
              _UpperCAmelCase      =			kwargs["""crop_n_points_downscale_factor"""]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
              _UpperCAmelCase      =			kwargs["""pred_iou_thresh"""]
        if "stability_score_offset" in kwargs:
              _UpperCAmelCase      =			kwargs["""stability_score_offset"""]
        if "mask_threshold" in kwargs:
              _UpperCAmelCase      =			kwargs["""mask_threshold"""]
        if "stability_score_thresh" in kwargs:
              _UpperCAmelCase      =			kwargs["""stability_score_thresh"""]
        if "crops_nms_thresh" in kwargs:
              _UpperCAmelCase      =			kwargs["""crops_nms_thresh"""]
        if "output_rle_mask" in kwargs:
              _UpperCAmelCase      =			kwargs["""output_rle_mask"""]
        if "output_bboxes_mask" in kwargs:
              _UpperCAmelCase      =			kwargs["""output_bboxes_mask"""]
        return preprocess_kwargs, forward_params, postprocess_kwargs






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






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=64						,		_SCREAMING_SNAKE_CASE = 0						,		_SCREAMING_SNAKE_CASE = 512 / 1500						,		_SCREAMING_SNAKE_CASE = 32						,		_SCREAMING_SNAKE_CASE = 1						,		):
        _UpperCAmelCase      =			load_image(__A			)
        _UpperCAmelCase      =			self.image_processor.size["""longest_edge"""]
        _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.generate_crop_boxes(
            __A						,		__A						,		__A						,		__A						,		__A						,		__A			)
        _UpperCAmelCase      =			self.image_processor(images=__A						,		return_tensors="""pt"""			)

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

        _UpperCAmelCase      =			grid_points.shape[1]
        _UpperCAmelCase      =			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						,		__A						,		__A			):
              _UpperCAmelCase      =			grid_points[:, i : i + points_per_batch, :, :]
              _UpperCAmelCase      =			input_labels[:, i : i + points_per_batch]
              _UpperCAmelCase      =			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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0.88						,		_SCREAMING_SNAKE_CASE=0.95						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=1						,		):
        _UpperCAmelCase      =			model_inputs.pop("""input_boxes"""			)
        _UpperCAmelCase      =			model_inputs.pop("""is_last"""			)
        _UpperCAmelCase      =			model_inputs.pop("""original_sizes"""			).tolist()
        _UpperCAmelCase      =			model_inputs.pop("""reshaped_input_sizes"""			).tolist()

        _UpperCAmelCase      =			self.model(**__A			)

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






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=0.7						,		):
        _UpperCAmelCase      =			[]
        _UpperCAmelCase      =			[]
        _UpperCAmelCase      =			[]
        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      =			torch.cat(__A			)
        _UpperCAmelCase      =			torch.cat(__A			)
        _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.post_process_for_mask_generation(
            __A						,		__A						,		__A						,		__A			)

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

        _UpperCAmelCase      =			{}
        if output_rle_mask:
              _UpperCAmelCase      =			rle_mask

        if output_bboxes_mask:
              _UpperCAmelCase      =			bounding_boxes

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





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

a													=			{
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

a													=			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_INIT_CONFIGURATION
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       BertTokenizer






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE="[UNK]"						,		_SCREAMING_SNAKE_CASE="[SEP]"						,		_SCREAMING_SNAKE_CASE="[PAD]"						,		_SCREAMING_SNAKE_CASE="[CLS]"						,		_SCREAMING_SNAKE_CASE="[MASK]"						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(
								    _SCREAMING_SNAKE_CASE						,		tokenizer_file=_SCREAMING_SNAKE_CASE						,		do_lower_case=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		sep_token=_SCREAMING_SNAKE_CASE						,		pad_token=_SCREAMING_SNAKE_CASE						,		cls_token=_SCREAMING_SNAKE_CASE						,		mask_token=_SCREAMING_SNAKE_CASE						,		tokenize_chinese_chars=_SCREAMING_SNAKE_CASE						,		strip_accents=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)

								_UpperCAmelCase      =			json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
								if (
								    normalizer_state.get("""lowercase"""						,		_SCREAMING_SNAKE_CASE			) != do_lower_case
								    or normalizer_state.get("""strip_accents"""						,		_SCREAMING_SNAKE_CASE			) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""						,		_SCREAMING_SNAKE_CASE			) != tokenize_chinese_chars
								):
														_UpperCAmelCase      =			getattr(_SCREAMING_SNAKE_CASE						,		normalizer_state.pop("""type"""			)			)
														_UpperCAmelCase      =			do_lower_case
														_UpperCAmelCase      =			strip_accents
														_UpperCAmelCase      =			tokenize_chinese_chars
														_UpperCAmelCase      =			normalizer_class(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			do_lower_case






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			[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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			[self.sep_token_id]
								_UpperCAmelCase      =			[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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
								return tuple(_SCREAMING_SNAKE_CASE			)