






import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


__a:     Any   					=					TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


__a:     Any   					=					[]
__a:     List[str]   					=					[]

__a:     Tuple   					=					{'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}

__a:     Union[str, Any]   					=					[
    {
        '''type''': '''header''',
        '''text''': {
            '''type''': '''plain_text''',
            '''text''': F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
            '''emoji''': True,
        },
    }
]

__a:     Optional[int]   					=					0
for log in Path().glob('''*.log'''):
  __a:     Optional[int]   					=					0
  with open(log, '''r''') as f:
    for line in f:
      __a:     str   					=					json.loads(line)
      if line.get('''nodeid''', '''''') != "":
        __a:     Optional[Any]   					=					line['''nodeid''']
        if line.get('''duration''', None) is not None:
          __a:     Union[str, Any]   					=					F"{line['duration']:.4f}"
          if line.get('''outcome''', '''''') == "failed":
            section_num_failed += 1
            failed.append([test, duration, log.name.split('''_''')[0]])
            total_num_failed += 1
  group_info.append([str(log), section_num_failed, failed])
  __a:     Union[str, Any]   					=					[]
  log.unlink()

__a:     Optional[Any]   					=					''''''
__a:     Union[str, Any]   					=					[]
if total_num_failed > 0:
  for name, num_failed, failed_tests in group_info:
    if num_failed > 0:
      if num_failed == 1:
        message += F"*{name[1:]}: {num_failed} failed test*\n"
      else:
        message += F"*{name[1:]}: {num_failed} failed tests*\n"
      __a:     Union[str, Any]   					=					[]
      __a:     Optional[int]   					=					{}
      for test in failed_tests:
        __a:     Union[str, Any]   					=					test[0].split('''::''')
        __a:     Any   					=					data[0].split('''/''')[-1]
        if data[0] not in filesafailed:
          __a:     Optional[Any]   					=					[data[1:]]
        else:
          filesafailed[data[0]] += [data[1:]]
        failed_table.append(data)

      __a:     Dict   					=					[test[0] for test in failed_table]
      __a:     int   					=					list(set(files))
      # Count number of instances in failed_tests
      __a:     Optional[int]   					=					[]
      for file in individual_files:
        table.append([file, len(filesafailed[file])])

      __a:     int   					=					tabulate(
          table,
          headers=['''Test Location''', '''Num Failed'''],
          tablefmt=hf_table_format,
          stralign='''right''',
      )
      message += F"\n```\n{failed_table}\n```"
      all_filesafailed.append(filesafailed)
  if len(message) > 3000:
    __a:     Optional[Any]   					=					'''Too many failed tests, please see the full report in the Action results.'''
    __a:     Any   					=					len(err) + 10
    __a:     str   					=					message[: 3000 - offset] + F"\n...\n```\n{err}"
  print(F"### {message}")
else:
  __a:     str   					=					'''No failed tests! 🤗'''
  print(F"## {message}")
  payload.append(no_error_payload)

if os.environ.get('''TEST_TYPE''', '''''') != "":
  from slack_sdk import WebClient

  __a:     Any   					=					WebClient(token=os.environ['''SLACK_API_TOKEN'''])
  if message != "No failed tests! 🤗":
    __a:     List[str]   					=					{
        '''type''': '''section''',
        '''text''': {
            '''type''': '''mrkdwn''',
            '''text''': message,
        },
    }
    payload.append(md_report)
    __a:     Union[str, Any]   					=					{
        '''type''': '''section''',
        '''text''': {
            '''type''': '''mrkdwn''',
            '''text''': '''*For more details:*''',
        },
        '''accessory''': {
            '''type''': '''button''',
            '''text''': {
                '''type''': '''plain_text''',
                '''text''': '''Check Action results''',
                '''emoji''': True,
            },
            '''url''': F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
        },
    }
    payload.append(action_button)
    __a:     str   					=					{
        '''type''': '''context''',
        '''elements''': [
            {
                '''type''': '''plain_text''',
                '''text''': F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
            }
        ],
    }
    payload.append(date_report)
  __a:     str   					=					client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
  __a:     Optional[Any]   					=					response.data['''ts''']
  for failed_file in all_filesafailed:
    for test_location, test_failures in failed_file.items():
      # Keep only the first instance of the test name
      __a:     int   					=					''''''
      for i, row in enumerate(test_failures):
        if row[0] != test_class:
          __a:     List[str]   					=					row[0]
        else:
          __a:     Optional[Any]   					=					''''''

      __a:     str   					=					{
          '''type''': '''section''',
          '''text''': {
              '''type''': '''mrkdwn''',
              '''text''': F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
          },
      }

      client.chat_postMessage(
          channel='''#accelerate-ci-daily''',
          thread_ts=ts,
          blocks=[payload],
      )







'''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

UpperCamelCase      					=logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE__    )
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  **__lowerCAmelCase			):
       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 	_UpperCAmelCase							(				self      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: Any            =				{}
       # preprocess args
       if "points_per_batch" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
             UpperCamelCase_					: str            =				kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
             UpperCamelCase_					: Any            =				kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
             UpperCamelCase_					: str            =				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_					: Any            =				kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
             UpperCamelCase_					: List[str]            =				kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
             UpperCamelCase_					: Tuple            =				kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
             UpperCamelCase_					: Optional[Any]            =				kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
             UpperCamelCase_					: Optional[int]            =				kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
             UpperCamelCase_					: List[Any]            =				kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs
 def __call__(				self      ,  __lowerCAmelCase      ,  *__lowerCAmelCase      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase			):
       return super().__call__(__lowerCAmelCase      ,  *__lowerCAmelCase      ,  num_workers=__lowerCAmelCase      ,  batch_size=__lowerCAmelCase      ,  **__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=64      ,  __lowerCAmelCase = 0      ,  __lowerCAmelCase = 5_12 / 15_00      ,  __lowerCAmelCase = 32      ,  __lowerCAmelCase = 1      ,  ):
       UpperCamelCase_					: Tuple            =				load_image(__lowerCAmelCase			)
       UpperCamelCase_					: Tuple            =				self.image_processor.size["""longest_edge"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				self.image_processor.generate_crop_boxes(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: Any            =				self.image_processor(images=__lowerCAmelCase      ,  return_tensors="""pt"""			)

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

       UpperCamelCase_					: Any            =				grid_points.shape[1]
       UpperCamelCase_					: List[str]            =				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_					: Dict            =				grid_points[:, i : i + points_per_batch, :, :]
             UpperCamelCase_					: List[Any]            =				input_labels[:, i : i + points_per_batch]
             UpperCamelCase_					: Dict            =				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      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0.88      ,  __lowerCAmelCase=0.95      ,  __lowerCAmelCase=0      ,  __lowerCAmelCase=1      ,  ):
       UpperCamelCase_					: Union[str, Any]            =				model_inputs.pop("""input_boxes"""			)
       UpperCamelCase_					: Tuple            =				model_inputs.pop("""is_last"""			)
       UpperCamelCase_					: Dict            =				model_inputs.pop("""original_sizes"""			).tolist()
       UpperCamelCase_					: int            =				model_inputs.pop("""reshaped_input_sizes"""			).tolist()

       UpperCamelCase_					: Optional[Any]            =				self.model(**__lowerCAmelCase			)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       UpperCamelCase_					: List[str]            =				model_outputs["""pred_masks"""]
       UpperCamelCase_					: Optional[int]            =				self.image_processor.post_process_masks(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  binarize=__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				model_outputs["""iou_scores"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Optional[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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=0.7      ,  ):
       UpperCamelCase_					: Tuple            =				[]
       UpperCamelCase_					: Optional[Any]            =				[]
       UpperCamelCase_					: Tuple            =				[]
       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_					: Any            =				torch.cat(__lowerCAmelCase			)
       UpperCamelCase_					: Union[str, Any]            =				torch.cat(__lowerCAmelCase			)
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Any            =				self.image_processor.post_process_for_mask_generation(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

       UpperCamelCase_					: List[Any]            =				defaultdict(__lowerCAmelCase			)
       for output in model_outputs:
             for k, v in output.items():
                   extra[k].append(__lowerCAmelCase			)

       UpperCamelCase_					: Optional[Any]            =				{}
       if output_rle_mask:
             UpperCamelCase_					: int            =				rle_mask

       if output_bboxes_mask:
             UpperCamelCase_					: int            =				bounding_boxes

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






