






from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import (
     MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
     MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
     MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
     MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
 )


__snake_case							 =  logging.get_logger(__name__)


__snake_case							 =  Dict[str, Any]
__snake_case							 =  List[Prediction]







@add_end_docstrings(UpperCAmelCase_			)
class       UpperCAmelCase_						( UpperCAmelCase_			):



      """simple docstring"""
      def __init__(    self   ,    *SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)    ->				Dict:
          super().__init__(*__UpperCamelCase   ,    **__UpperCamelCase			)

          if self.framework == "tf":
              raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''			)

          requires_backends(self   ,    '''vision'''			)
          self.check_model_type(
              dict(
                  MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                  + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                  + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                  + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Dict:
          UpperCamelCase	:Union[str, Any]     							=       {}
          UpperCamelCase	:Any     							=       {}
          if "subtask" in kwargs:
              UpperCamelCase	:Tuple     							=       kwargs['''subtask''']
              UpperCamelCase	:Dict     							=       kwargs['''subtask''']
          if "threshold" in kwargs:
              UpperCamelCase	:Dict     							=       kwargs['''threshold''']
          if "mask_threshold" in kwargs:
              UpperCamelCase	:List[Any]     							=       kwargs['''mask_threshold''']
          if "overlap_mask_area_threshold" in kwargs:
              UpperCamelCase	:Union[str, Any]     							=       kwargs['''overlap_mask_area_threshold''']

          return preprocess_kwargs, {}, postprocess_kwargs
      def __call__(    self   ,    SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)    ->				str:
          return super().__call__(__UpperCamelCase   ,    **__UpperCamelCase			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=None			)    ->				Optional[int]:
          UpperCamelCase	:int     							=       load_image(__UpperCamelCase			)
          UpperCamelCase	:Tuple     							=       [(image.height, image.width)]
          if self.model.config.__class__.__name__ == "OneFormerConfig":
              if subtask is None:
                  UpperCamelCase	:List[str]     							=       {}
              else:
                  UpperCamelCase	:Any     							=       {'''task_inputs''': [subtask]}
              UpperCamelCase	:Union[str, Any]     							=       self.image_processor(images=[image]   ,    return_tensors='''pt'''   ,    **__UpperCamelCase			)
              UpperCamelCase	:str     							=       self.tokenizer(
                  inputs['''task_inputs''']   ,    padding='''max_length'''   ,    max_length=self.model.config.task_seq_len   ,    return_tensors=self.framework   ,    )['''input_ids''']
          else:
              UpperCamelCase	:Union[str, Any]     							=       self.image_processor(images=[image]   ,    return_tensors='''pt'''			)
          UpperCamelCase	:List[Any]     							=       target_size
          return inputs
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				Optional[int]:
          UpperCamelCase	:Tuple     							=       model_inputs.pop('''target_size'''			)
          UpperCamelCase	:Dict     							=       self.model(**__UpperCamelCase			)
          UpperCamelCase	:Tuple     							=       target_size
          return model_outputs




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=0.9   ,    SCREAMING_SNAKE_CASE_=0.5   ,    SCREAMING_SNAKE_CASE_=0.5			)    ->				str:
          UpperCamelCase	:int     							=       None
          if subtask in {"panoptic", None} and hasattr(self.image_processor   ,    '''post_process_panoptic_segmentation'''			):
              UpperCamelCase	:Optional[Any]     							=       self.image_processor.post_process_panoptic_segmentation
          elif subtask in {"instance", None} and hasattr(self.image_processor   ,    '''post_process_instance_segmentation'''			):
              UpperCamelCase	:Tuple     							=       self.image_processor.post_process_instance_segmentation

          if fn is not None:
              UpperCamelCase	:Optional[int]     							=       fn(
                  __UpperCamelCase   ,    threshold=__UpperCamelCase   ,    mask_threshold=__UpperCamelCase   ,    overlap_mask_area_threshold=__UpperCamelCase   ,    target_sizes=model_outputs['''target_size''']   ,    )[0]

              UpperCamelCase	:Union[str, Any]     							=       []
              UpperCamelCase	:Dict     							=       outputs['''segmentation''']

              for segment in outputs["segments_info"]:
                  UpperCamelCase	:Dict     							=       (segmentation == segment['''id''']) * 255
                  UpperCamelCase	:Any     							=       Image.fromarray(mask.numpy().astype(np.uinta			)   ,    mode='''L'''			)
                  UpperCamelCase	:int     							=       self.model.config.idalabel[segment['''label_id''']]
                  UpperCamelCase	:Any     							=       segment['''score''']
                  annotation.append({'''score''': score, '''label''': label, '''mask''': mask}			)

          elif subtask in {"semantic", None} and hasattr(self.image_processor   ,    '''post_process_semantic_segmentation'''			):
              UpperCamelCase	:List[Any]     							=       self.image_processor.post_process_semantic_segmentation(
                  __UpperCamelCase   ,    target_sizes=model_outputs['''target_size''']			)[0]

              UpperCamelCase	:Tuple     							=       []
              UpperCamelCase	:Optional[Any]     							=       outputs.numpy()
              UpperCamelCase	:List[Any]     							=       np.unique(__UpperCamelCase			)

              for label in labels:
                  UpperCamelCase	:str     							=       (segmentation == label) * 255
                  UpperCamelCase	:Optional[Any]     							=       Image.fromarray(mask.astype(np.uinta			)   ,    mode='''L'''			)
                  UpperCamelCase	:List[Any]     							=       self.model.config.idalabel[label]
                  annotation.append({'''score''': None, '''label''': label, '''mask''': mask}			)
          else:
              raise ValueError(F'''Subtask {subtask} is not supported for model {type(self.model			)}'''			)
          return annotation













from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





