


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 : Dict 			=				logging.get_logger(__name__)



@add_end_docstrings(__UpperCAmelCase)
class    UpperCAmelCase_	(							__UpperCAmelCase):
       '''simple docstring'''






       def __init__(     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__(**lowerCAmelCase_						)
              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(lowerCAmelCase_						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		{}
              UpperCamelCase    :	Dict   	=		{}
              UpperCamelCase    :	Any   	=		{}
              # preprocess args
              if "points_per_batch" in kwargs:
                     UpperCamelCase    :	int   	=		kwargs['''points_per_batch''']
              if "points_per_crop" in kwargs:
                     UpperCamelCase    :	List[Any]   	=		kwargs['''points_per_crop''']
              if "crops_n_layers" in kwargs:
                     UpperCamelCase    :	List[str]   	=		kwargs['''crops_n_layers''']
              if "crop_overlap_ratio" in kwargs:
                     UpperCamelCase    :	Any   	=		kwargs['''crop_overlap_ratio''']
              if "crop_n_points_downscale_factor" in kwargs:
                     UpperCamelCase    :	Tuple   	=		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    :	List[str]   	=		kwargs['''stability_score_offset''']
              if "mask_threshold" in kwargs:
                     UpperCamelCase    :	Any   	=		kwargs['''mask_threshold''']
              if "stability_score_thresh" in kwargs:
                     UpperCamelCase    :	Union[str, Any]   	=		kwargs['''stability_score_thresh''']
              if "crops_nms_thresh" in kwargs:
                     UpperCamelCase    :	Dict   	=		kwargs['''crops_nms_thresh''']
              if "output_rle_mask" in kwargs:
                     UpperCamelCase    :	List[str]   	=		kwargs['''output_rle_mask''']
              if "output_bboxes_mask" in kwargs:
                     UpperCamelCase    :	Any   	=		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						):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=64 ,					__SCREAMING_SNAKE_CASE = 0 ,					__SCREAMING_SNAKE_CASE = 512 / 1_500 ,					__SCREAMING_SNAKE_CASE = 32 ,					__SCREAMING_SNAKE_CASE = 1 ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		load_image(lowerCAmelCase_						)
              UpperCamelCase    :	Dict   	=		self.image_processor.size['''longest_edge''']
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	List[str]   	=		self.image_processor.generate_crop_boxes(
                  lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_						)
              UpperCamelCase    :	str   	=		self.image_processor(images=lowerCAmelCase_ ,					return_tensors='''pt'''						)

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

              UpperCamelCase    :	Optional[Any]   	=		grid_points.shape[1]
              UpperCamelCase    :	List[Any]   	=		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 ,					lowerCAmelCase_ ,					lowerCAmelCase_						):
                     UpperCamelCase    :	Tuple   	=		grid_points[:, i : i + points_per_batch, :, :]
                     UpperCamelCase    :	Optional[int]   	=		input_labels[:, i : i + points_per_batch]
                     UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=0.88 ,					__SCREAMING_SNAKE_CASE=0.95 ,					__SCREAMING_SNAKE_CASE=0 ,					__SCREAMING_SNAKE_CASE=1 ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		model_inputs.pop('''input_boxes'''						)
              UpperCamelCase    :	Dict   	=		model_inputs.pop('''is_last'''						)
              UpperCamelCase    :	Union[str, Any]   	=		model_inputs.pop('''original_sizes'''						).tolist()
              UpperCamelCase    :	Union[str, Any]   	=		model_inputs.pop('''reshaped_input_sizes'''						).tolist()

              UpperCamelCase    :	Union[str, Any]   	=		self.model(**lowerCAmelCase_						)

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=0.7 ,					):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[]
              UpperCamelCase    :	str   	=		[]
              UpperCamelCase    :	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'''						)						)

              UpperCamelCase    :	Union[str, Any]   	=		torch.cat(lowerCAmelCase_						)
              UpperCamelCase    :	Any   	=		torch.cat(lowerCAmelCase_						)
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Tuple   	=		self.image_processor.post_process_for_mask_generation(
                  lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_						)

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

              UpperCamelCase    :	Tuple   	=		{}
              if output_rle_mask:
                     UpperCamelCase    :	Optional[Any]   	=		rle_mask

              if output_bboxes_mask:
                     UpperCamelCase    :	int   	=		bounding_boxes

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





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase_	(							_a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					DebertaTokenizer
       __UpperCamelCase      :     Optional[int]							=					True
       __UpperCamelCase      :     Optional[int]							=					DebertaTokenizerFast






       def  _lowercase   (     self						):




              """simple docstring"""




              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase    :	Optional[int]   	=		[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''\u0120''',
                  '''\u0120l''',
                  '''\u0120n''',
                  '''\u0120lo''',
                  '''\u0120low''',
                  '''er''',
                  '''\u0120lowest''',
                  '''\u0120newer''',
                  '''\u0120wider''',
                  '''[UNK]''',
              ]
              UpperCamelCase    :	Tuple   	=		dict(zip(__SCREAMING_SNAKE_CASE ,					range(len(__SCREAMING_SNAKE_CASE						)						)						)						)
              UpperCamelCase    :	Any   	=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
              UpperCamelCase    :	List[Any]   	=		{'''unk_token''': '''[UNK]'''}

              UpperCamelCase    :	Union[str, Any]   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']						)
              UpperCamelCase    :	Optional[int]   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']						)
              with open(self.vocab_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     fp.write(json.dumps(__SCREAMING_SNAKE_CASE						) + '''\n'''						)
              with open(self.merges_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              kwargs.update(self.special_tokens_map						)
              return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		'''lower newer'''
              UpperCamelCase    :	Union[str, Any]   	=		'''lower newer'''
              return input_text, output_text






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.get_tokenizer()
              UpperCamelCase    :	int   	=		'''lower newer'''
              UpperCamelCase    :	Union[str, Any]   	=		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
              UpperCamelCase    :	Tuple   	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE						)
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		tokens + [tokenizer.unk_token]
              UpperCamelCase    :	Optional[int]   	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.get_tokenizer()
              UpperCamelCase    :	Optional[Any]   	=		tokenizer('''Hello''' ,					'''World'''						)
              UpperCamelCase    :	List[str]   	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
              self.assertListEqual(tokd['''token_type_ids'''] ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

              UpperCamelCase    :	Optional[Any]   	=		tokenizer.encode('''sequence builders''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		tokenizer.encode(
                  '''sequence builders''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					add_prefix_space=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		tokenizer.encode(
                  '''sequence builders''' ,					'''multi-sequence build''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					add_prefix_space=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		[self.tokenizer_class]
              if self.test_rust_tokenizer:
                     tokenizer_classes.append(self.rust_tokenizer_class						)

              for tokenizer_class in tokenizer_classes:
                     UpperCamelCase    :	Optional[int]   	=		tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

                     UpperCamelCase    :	str   	=		[
                         '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                         '''ALBERT incorporates two parameter reduction techniques''',
                         '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                         ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                         ''' vocabulary embedding.''',
                     ]

                     UpperCamelCase    :	Union[str, Any]   	=		tokenizer(__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Union[str, Any]   	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						) for seq in encoding['''input_ids''']]

                     # fmt: off
                     UpperCamelCase    :	int   	=		{
                         '''input_ids''': [
                             [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                         ],
                         '''token_type_ids''': [
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                         ],
                         '''attention_mask''': [
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                         ]
                     }
                     # fmt: on

                     UpperCamelCase    :	List[str]   	=		[
                         '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                         '''ALBERT incorporates two parameter reduction techniques''',
                         '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                         ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                         ''' vocabulary embedding.''',
                     ]

                     self.assertDictEqual(encoding.data ,					__SCREAMING_SNAKE_CASE						)

                     for expected, decoded in zip(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

