

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Tuple=True	,							lowerCAmelCase			:   List[str]=False	,							lowerCAmelCase			:   Dict=10	,							lowerCAmelCase			:   Dict=3	,							lowerCAmelCase			:   Optional[Any]=32 * 8	,							lowerCAmelCase			:   Any=32 * 8	,							lowerCAmelCase			:   List[Any]=4	,							lowerCAmelCase			:   Any=64	,							)							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						parent
     __lowerCAmelCase  :      str    =						batch_size
     __lowerCAmelCase  :      Union[str, Any]    =						is_training
     __lowerCAmelCase  :      Optional[int]    =						use_auxiliary_loss
     __lowerCAmelCase  :      List[str]    =						num_queries
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      Optional[int]    =						min_size
     __lowerCAmelCase  :      List[str]    =						max_size
     __lowerCAmelCase  :      Optional[int]    =						num_labels
     __lowerCAmelCase  :      Any    =						hidden_dim
     __lowerCAmelCase  :      Dict    =						hidden_dim







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
         lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						torch.ones([self.batch_size, self.min_size, self.max_size]	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						(
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,							device=lowerCAmelCase   ) > 0.5
     ).float()
     __lowerCAmelCase  :      Optional[int]    =						(torch.rand((self.batch_size, self.num_labels)	,							device=lowerCAmelCase   ) > 0.5).long()

     __lowerCAmelCase  :      List[Any]    =						self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						MaskaFormerConfig(
         hidden_size=self.hidden_dim	,							)
     __lowerCAmelCase  :      Optional[int]    =						self.num_queries
     __lowerCAmelCase  :      List[str]    =						self.num_labels

     __lowerCAmelCase  :      Tuple    =						[1, 1, 1, 1]
     __lowerCAmelCase  :      Optional[int]    =						self.num_channels

     __lowerCAmelCase  :      int    =						64
     __lowerCAmelCase  :      Optional[Any]    =						1_28
     __lowerCAmelCase  :      List[Any]    =						self.hidden_dim
     __lowerCAmelCase  :      Any    =						self.hidden_dim
     __lowerCAmelCase  :      str    =						self.hidden_dim
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase  :      Union[str, Any]    =						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						output.encoder_hidden_states
     __lowerCAmelCase  :      Any    =						output.pixel_decoder_hidden_states
     __lowerCAmelCase  :      List[Any]    =						output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(lowerCAmelCase   )	,							len(config.backbone_config.depths   )   )
     self.parent.assertTrue(len(lowerCAmelCase   )	,							len(config.backbone_config.depths   )   )
     self.parent.assertTrue(len(lowerCAmelCase   )	,							config.decoder_layers   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict=False   )							->     Optional[int]:






     """simple docstring"""







     with torch.no_grad():
        __lowerCAmelCase  :      Optional[int]    =						MaskaFormerModel(config=lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()

        __lowerCAmelCase  :      Union[str, Any]    =						model(pixel_values=lowerCAmelCase	,							pixel_mask=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						model(lowerCAmelCase	,							output_hidden_states=lowerCAmelCase   )

     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape	,							(self.batch_size, self.num_queries, self.hidden_dim)	,							)
     # let's ensure the other two hidden state exists
     self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
     self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

     if output_hidden_states:
        self.check_output_hidden_state(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[int]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						MaskaFormerForUniversalSegmentation(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()

     def comm_check_on_output(lowerCAmelCase			:   int   ):
        # let's still check that all the required stuff is there
        self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
        self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
        self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
        # okay, now we need to check the logits shape
        # due to the encoder compression, masks have a //4 spatial size
        self.parent.assertEqual(
            result.masks_queries_logits.shape	,							(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,							)
        # + 1 for null class
        self.parent.assertEqual(
            result.class_queries_logits.shape	,							(self.batch_size, self.num_queries, self.num_labels + 1)   )

     with torch.no_grad():
        __lowerCAmelCase  :      Dict    =						model(pixel_values=lowerCAmelCase	,							pixel_mask=lowerCAmelCase   )
        __lowerCAmelCase  :      int    =						model(lowerCAmelCase   )

        comm_check_on_output(lowerCAmelCase   )

        __lowerCAmelCase  :      Optional[int]    =						model(
            pixel_values=lowerCAmelCase	,							pixel_mask=lowerCAmelCase	,							mask_labels=lowerCAmelCase	,							class_labels=lowerCAmelCase   )

     comm_check_on_output(lowerCAmelCase   )

     self.parent.assertTrue(result.loss is not None   )
     self.parent.assertEqual(result.loss.shape	,							torch.Size([1]   )   )






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  lowerCamelCase   :      Any										={"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

  lowerCamelCase   :      Optional[Any]										=False
  lowerCamelCase   :      Optional[Any]										=False
  lowerCamelCase   :      Dict										=False
  lowerCamelCase   :      Optional[int]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						MaskaFormerModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							has_text_modality=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase	,							**lowerCAmelCase	,							output_hidden_states=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase   )







  @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     pass







  @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Union[str, Any]:






     """simple docstring"""







     pass







  @unittest.skip(reason="""Mask2Former is not a generative model"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip(reason="""Mask2Former does not use token embeddings"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     pass







  @require_torch_multi_gpu
  @unittest.skip(
      reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     pass







  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     int:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Tuple    =						model_class(lowerCAmelCase   )
        __lowerCAmelCase  :      Union[str, Any]    =						inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase  :      Optional[int]    =						[*signature.parameters.keys()]

        __lowerCAmelCase  :      str    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]	,							lowerCAmelCase   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[str]:






     """simple docstring"""







     for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
        __lowerCAmelCase  :      List[str]    =						MaskaFormerModel.from_pretrained(lowerCAmelCase   )
        self.assertIsNotNone(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						(self.model_tester.min_size,) * 2
     __lowerCAmelCase  :      List[Any]    =						{
         """pixel_values""": torch.randn((2, 3, *size)	,							device=lowerCAmelCase   ),
         """mask_labels""": torch.randn((2, 10, *size)	,							device=lowerCAmelCase   ),
         """class_labels""": torch.zeros(2	,							10	,							device=lowerCAmelCase   ).long(),
     }
     __lowerCAmelCase  :      Dict    =						self.model_tester.get_config()

     __lowerCAmelCase  :      Dict    =						MaskaFormerForUniversalSegmentation(lowerCAmelCase   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						model(**lowerCAmelCase   )
     self.assertTrue(outputs.loss is not None   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase	,							**lowerCAmelCase	,							output_hidden_states=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Any    =						model_class(lowerCAmelCase   ).to(lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[Any]    =						model(**lowerCAmelCase	,							output_attentions=lowerCAmelCase   )
        self.assertTrue(outputs.attentions is not None   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Any:






     """simple docstring"""







     if not self.model_tester.is_training:
        return

     __lowerCAmelCase  :      Optional[int]    =						self.all_model_classes[1]
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()

     __lowerCAmelCase  :      int    =						model_class(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.train()

     __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							mask_labels=lowerCAmelCase	,							class_labels=lowerCAmelCase   ).loss
     loss.backward()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.all_model_classes[1]
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						self.model_tester.prepare_config_and_inputs()
     __lowerCAmelCase  :      Union[str, Any]    =						True
     __lowerCAmelCase  :      Dict    =						True

     __lowerCAmelCase  :      int    =						model_class(lowerCAmelCase   ).to(lowerCAmelCase   )
     model.train()

     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase	,							mask_labels=lowerCAmelCase	,							class_labels=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     __lowerCAmelCase  :      Optional[int]    =						outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

     __lowerCAmelCase  :      Tuple    =						outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     __lowerCAmelCase  :      Dict    =						outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=lowerCAmelCase   )

     self.assertIsNotNone(encoder_hidden_states.grad   )
     self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
     self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
     self.assertIsNotNone(attentions.grad   )






__UpperCAmelCase								=			1e-4





def      snake_case_				()      ->     Optional[int]:
   __lowerCAmelCase  :      str    =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
   return image



@require_vision
@slow
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Any:






     """simple docstring"""







     return "facebook/mask2former-swin-small-coco-instance"







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     str:






     """simple docstring"""







     return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						self.default_image_processor
     __lowerCAmelCase  :      Dict    =						prepare_img()
     __lowerCAmelCase  :      List[Any]    =						image_processor(lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						inputs["""pixel_values"""].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
     # check size
     self.assertEqual(lowerCAmelCase	,							(1, 3, 3_84, 3_84)   )

     with torch.no_grad():
        __lowerCAmelCase  :      List[str]    =						model(**lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						torch.tensor(
         [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]   ).to(lowerCAmelCase   )
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=lowerCAmelCase   )   )

     __lowerCAmelCase  :      Tuple    =						torch.tensor(
         [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]   ).to(lowerCAmelCase   )
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=lowerCAmelCase   )   )

     __lowerCAmelCase  :      str    =						torch.tensor(
         [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]   ).to(lowerCAmelCase   )
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,							lowerCAmelCase	,							atol=lowerCAmelCase   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase   ).eval()
     __lowerCAmelCase  :      List[str]    =						self.default_image_processor
     __lowerCAmelCase  :      Union[str, Any]    =						prepare_img()
     __lowerCAmelCase  :      Union[str, Any]    =						image_processor(lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						inputs["""pixel_values"""].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
     # check size
     self.assertEqual(lowerCAmelCase	,							(1, 3, 3_84, 3_84)   )

     with torch.no_grad():
        __lowerCAmelCase  :      int    =						model(**lowerCAmelCase   )
     # masks_queries_logits
     __lowerCAmelCase  :      Optional[Any]    =						outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape	,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
     __lowerCAmelCase  :      Tuple    =						[
         [-8.7839, -9.0056, -8.8121],
         [-7.4104, -7.0313, -6.5401],
         [-6.6105, -6.3427, -6.4675],
     ]
     __lowerCAmelCase  :      Optional[Any]    =						torch.tensor(lowerCAmelCase   ).to(lowerCAmelCase   )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=lowerCAmelCase   )   )
     # class_queries_logits
     __lowerCAmelCase  :      Optional[int]    =						outputs.class_queries_logits
     self.assertEqual(class_queries_logits.shape	,							(1, model.config.num_queries, model.config.num_labels + 1)   )
     __lowerCAmelCase  :      int    =						torch.tensor(
         [
             [1.8324, -8.0835, -4.1922],
             [0.8450, -9.0050, -3.6053],
             [0.3045, -7.7293, -3.0275],
         ]   ).to(lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,							lowerCAmelCase	,							atol=lowerCAmelCase   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase   ).eval()
     __lowerCAmelCase  :      str    =						self.default_image_processor

     __lowerCAmelCase  :      str    =						image_processor(
         [np.zeros((3, 8_00, 13_33)   ), np.zeros((3, 8_00, 13_33)   )]	,							segmentation_maps=[np.zeros((3_84, 3_84)   ).astype(np.floataa   ), np.zeros((3_84, 3_84)   ).astype(np.floataa   )]	,							return_tensors="""pt"""	,							)

     __lowerCAmelCase  :      List[str]    =						inputs["""pixel_values"""].to(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						[el.to(lowerCAmelCase   ) for el in inputs["""mask_labels"""]]
     __lowerCAmelCase  :      Optional[int]    =						[el.to(lowerCAmelCase   ) for el in inputs["""class_labels"""]]

     with torch.no_grad():
        __lowerCAmelCase  :      Any    =						model(**lowerCAmelCase   )

     self.assertTrue(outputs.loss is not None   )





import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

   def parse_line(__A					:						List[Any]					):
      for line in fp:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      str    =						line.decode("""UTF-8"""					)
         if "warnings summary (final)" in line:
            continue
         # This means we are outside the body of a warning
         elif not line.startswith(""" """					):
            # process a single warning and move it to `selected_warnings`.
            if len(__A					) > 0:
               __lowerCAmelCase  :      Tuple    =						"""\n""".join(__A					)
               # Only keep the warnings specified in `targets`
               if any(f''': {x}: ''' in warning for x in targets					):
                  selected_warnings.add(__A					)
               buffer.clear()
            continue
         else:
            __lowerCAmelCase  :      Optional[int]    =						line.strip()
            buffer.append(__A					)

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						os.path.join(__A ,       __A					)
         if not os.path.isdir(__A					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(__A					) as fp:
               parse_line(__A					)
   else:
      try:
         with zipfile.ZipFile(__A					) as z:
            for filename in z.namelist():
               if not os.path.isdir(__A					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(__A					) as fp:
                     parse_line(__A					)
      except Exception:
         logger.warning(
             f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''					)

   return selected_warnings





def      snake_case_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[os.path.join(__A ,       __A					) for p in os.listdir(__A					) if (p.endswith(""".zip"""					) or from_gh)]
   for p in paths:
      selected_warnings.update(extract_warnings_from_single_artifact(__A ,       __A					)					)

   return selected_warnings


if __name__ == "__main__":





   def      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
   parser.add_argument(
       """--output_dir""",
       type=str,
       required=True,
       help="""Where to store the downloaded artifacts and other result files.""",
   )
   parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
   # optional parameters
   parser.add_argument(
       """--targets""",
       default="""DeprecationWarning,UserWarning,FutureWarning""",
       type=list_str,
       help="""Comma-separated list of target warning(s) which we want to extract.""",
   )
   parser.add_argument(
       """--from_gh""",
       action="""store_true""",
       help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
   )

   __UpperCAmelCase								=			parser.parse_args()

   __UpperCAmelCase								=			args.from_gh
   if from_gh:
      # The artifacts have to be downloaded using `actions/download-artifact@v3`
      pass
   else:
      os.makedirs(args.output_dir, exist_ok=True)

      # get download links
      __UpperCAmelCase								=			get_artifacts_links(args.workflow_run_id, token=args.token)
      with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
         json.dump(artifacts, fp, ensure_ascii=False, indent=4)

      # download artifacts
      for idx, (name, url) in enumerate(artifacts.items()):
         print(name)
         print(url)
         print("""=""" * 80)
         download_artifact(name, url, args.output_dir, args.token)
         # Be gentle to GitHub
         time.sleep(1)

    # extract warnings from artifacts
   __UpperCAmelCase								=			extract_warnings(args.output_dir, args.targets)
   __UpperCAmelCase								=			sorted(selected_warnings)
   with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


