

import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil




@pytest.fixture
def 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  a__  =tmp_path / '''file.csv'''
  a__  =textwrap.dedent(
      '''\
        header1,header2
        1,2
        10,20
        '''							)
  with open(_A	,      '''w'''							) as f:
    f.write(_A							)
  return str(_A							)




@pytest.fixture
def 			UpperCAmelCase__				(			_A      : List[Any]							):





  '''simple docstring'''







  a__  =tmp_path / '''malformed_file.csv'''
  a__  =textwrap.dedent(
      '''\
        header1,header2
        1,2
        10,20,
        '''							)
  with open(_A	,      '''w'''							) as f:
    f.write(_A							)
  return str(_A							)




@pytest.fixture
def 			UpperCAmelCase__				(			_A      : Optional[Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =tmp_path / '''csv_with_image.csv'''
  a__  =textwrap.dedent(
      F"""\
        image
        {image_file}
        """							)
  with open(_A	,      '''w'''							) as f:
    f.write(_A							)
  return str(_A							)




@pytest.fixture
def 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  a__  =tmp_path / '''csv_with_label.csv'''
  a__  =textwrap.dedent(
      '''\
        label
        good
        bad
        good
        '''							)
  with open(_A	,      '''w'''							) as f:
    f.write(_A							)
  return str(_A							)




@pytest.fixture
def 			UpperCAmelCase__				(			_A      : List[Any]							):





  '''simple docstring'''







  a__  =tmp_path / '''csv_with_int_list.csv'''
  a__  =textwrap.dedent(
      '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''							)
  with open(_A	,      '''w'''							) as f:
    f.write(_A							)
  return str(_A							)




def 			UpperCAmelCase__				(			_A      : Dict	,      _A      : List[Any]	,      _A      : Any							):





  '''simple docstring'''







  a__  =Csv()
  a__  =csv._generate_tables([[csv_file, malformed_csv_file]]							)
  with pytest.raises(_A	,      match='''Error tokenizing data'''							):
    for _ in generator:
      pass
  assert any(
      record.levelname == '''ERROR'''
      and '''Failed to read file''' in record.message
      and os.path.basename(_A							) in record.message
      for record in caplog.records							)




@require_pil
def 			UpperCAmelCase__				(			_A      : Dict							):





  '''simple docstring'''







  with open(_A	,      encoding='''utf-8'''							) as f:
    a__  =f.read().splitlines()[1]
  a__  =Csv(encoding='''utf-8'''	,      features=Features({'''image''': Image()}							)							)
  a__  =csv._generate_tables([[csv_file_with_image]]							)
  a__  =pa.concat_tables([table for _, table in generator]							)
  assert pa_table.schema.field('''image'''							).type == Image()()
  a__  =pa_table.to_pydict()['''image''']
  assert generated_content == [{"path": image_file, "bytes": None}]




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  with open(_A	,      encoding='''utf-8'''							) as f:
    a__  =f.read().splitlines()[1:]
  a__  =Csv(encoding='''utf-8'''	,      features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']							)}							)							)
  a__  =csv._generate_tables([[csv_file_with_label]]							)
  a__  =pa.concat_tables([table for _, table in generator]							)
  assert pa_table.schema.field('''label'''							).type == ClassLabel(names=['''good''', '''bad''']							)()
  a__  =pa_table.to_pydict()['''label''']
  assert generated_content == [ClassLabel(names=['''good''', '''bad''']							).straint(_A							) for label in labels]




def 			UpperCAmelCase__				(			_A      : Tuple							):





  '''simple docstring'''







  a__  =Csv(encoding='''utf-8'''	,      sep=''','''	,      converters={'''int_list''': lambda _A							: [int(_A							) for i in x.split()]}							)
  a__  =csv._generate_tables([[csv_file_with_int_list]]							)
  a__  =pa.concat_tables([table for _, table in generator]							)
  assert pa.types.is_list(pa_table.schema.field('''int_list'''							).type							)
  a__  =pa_table.to_pydict()['''int_list''']
  assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



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}
