
"""simple docstring"""


import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor







class  lowerCAmelCase_ :



						'''simple docstring'''


						def __init__(       self   :					Dict     ,A_   :					Any     ,A_   :					int=13     ,A_   :					str=30     ,A_   :					Optional[Any]=2     ,A_   :					List[Any]=3     ,A_   :					Optional[Any]=True     ,A_   :					List[Any]=True     ,A_   :					int=32     ,A_   :					str=5     ,A_   :					str=4     ,A_   :					List[Any]=37     ,A_   :					Dict="gelu"     ,A_   :					str=0.1     ,A_   :					Optional[Any]=0.1     ,A_   :					int=10     ,A_   :					Optional[Any]=0.02     ,A_   :					Union[str, Any]=None     ,)   ->      Optional[Any]:
							A        =      parent
							A        =      batch_size
							A        =      image_size
							A        =      patch_size
							A        =      num_channels
							A        =      is_training
							A        =      use_labels
							A        =      hidden_size
							A        =      num_hidden_layers
							A        =      num_attention_heads
							A        =      intermediate_size
							A        =      hidden_act
							A        =      hidden_dropout_prob
							A        =      attention_probs_dropout_prob
							A        =      type_sequence_label_size
							A        =      initializer_range
							A        =      scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							A        =      (image_size // patch_size) ** 2
							A        =      num_patches + 1


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      List[Any]:
							A        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

							A        =      None
							if self.use_labels:
								A        =      ids_tensor([self.batch_size]     ,self.type_sequence_label_size					)

							A        =      self.get_config()

							return config, pixel_values, labels


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Any:
							return ViTMSNConfig(
							    image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,initializer_range=self.initializer_range     ,)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					Optional[Any]     ,A_   :					List[Any]     ,A_   :					Tuple					)   ->      Any:
							A        =      ViTMSNModel(config=A_					)
							model.to(A_					)
							model.eval()
							A        =      model(A_					)
							self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any     ,A_   :					Any     ,A_   :					Optional[int]     ,A_   :					List[str]					)   ->      str:
							A        =      self.type_sequence_label_size
							A        =      ViTMSNForImageClassification(A_					)
							model.to(A_					)
							model.eval()
							A        =      model(A_     ,labels=A_					)
							print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'					)
							print('Labels: {labels}'					)
							self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)					)

							# test greyscale images
							A        =      1
							A        =      ViTMSNForImageClassification(A_					)
							model.to(A_					)
							model.eval()

							A        =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
							A        =      model(A_					)
							self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Any:
							A        =      self.prepare_config_and_inputs()
							A        =      config_and_inputs
							A        =      {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class  lowerCAmelCase_ (					__snake_case						,    __snake_case						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Dict					=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_lowerCamelCase:			int					=			(
						    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_lowerCamelCase:			List[str]					=			False
						_lowerCamelCase:			List[Any]					=			False
						_lowerCamelCase:			int					=			False
						_lowerCamelCase:			Union[str, Any]					=			False


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      str:
							A        =      ViTMSNModelTester(self					)
							A        =      ConfigTester(self     ,config_class=A_     ,has_text_modality=A_     ,hidden_size=37					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Dict:
							self.config_tester.run_common_tests()


						@unittest.skip(reason='ViTMSN does not use inputs_embeds'					)
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      List[str]:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Tuple:
							A        =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A        =      model_class(A_					)
								self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)					)
								A        =      model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(A_     ,nn.Linear					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Tuple:
							A        =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A        =      model_class(A_					)
								A        =      inspect.signature(model.forward					)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								A        =      [*signature.parameters.keys()]

								A        =      ['pixel_values']
								self.assertListEqual(arg_names[:1]     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[Any]:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Tuple:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_					)




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Optional[Any]:
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A        =      ViTMSNModel.from_pretrained(A_					)
								self.assertIsNotNone(A_					)






def 					_snake_case (						):
	A        =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
	return image







@require_torch
@require_vision
class  lowerCAmelCase_ (					unittest.TestCase			):



						'''simple docstring'''


						@cached_property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Optional[Any]:
							return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'					) if is_vision_available() else None




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      List[str]:
							torch.manual_seed(2					)
							A        =      ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'					).to(A_					)

							A        =      self.default_image_processor
							A        =      prepare_img()
							A        =      image_processor(images=A_     ,return_tensors='pt'					).to(A_					)

							# forward pass
							with torch.no_grad():
								A        =      model(**A_					)

							# verify the logits
							A        =      torch.Size((1, 1000)					)
							self.assertEqual(outputs.logits.shape     ,A_					)

							A        =      torch.tensor([-0.08_03, -0.44_54, -0.23_75]					).to(A_					)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,A_     ,atol=1e-4					)					)


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


snake_case_			  = logging.get_logger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       set()
     lowercase__    :   List[str]      =       []

     def parse_line(SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):
          for line in fp:
               if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[int]      =       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(SCREAMING_SNAKE_CASE_			) > 0:
                         lowercase__    :   Optional[Any]      =       '\n'.join(SCREAMING_SNAKE_CASE_			)
                         # Only keep the warnings specified in `targets`
                         if any(f""": {x}: """ in warning for x in targets			):
                              selected_warnings.add(SCREAMING_SNAKE_CASE_			)
                         buffer.clear()
                    continue
               else:
                    lowercase__    :   Optional[Any]      =       line.strip()
                    buffer.append(SCREAMING_SNAKE_CASE_			)

     if from_gh:
          for filename in os.listdir(SCREAMING_SNAKE_CASE_			):
               lowercase__    :   int      =       os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
               if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(SCREAMING_SNAKE_CASE_			) as fp:
                         parse_line(SCREAMING_SNAKE_CASE_			)
     else:
          try:
               with zipfile.ZipFile(SCREAMING_SNAKE_CASE_			) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(SCREAMING_SNAKE_CASE_			) as fp:
                                   parse_line(SCREAMING_SNAKE_CASE_			)
          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__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       set()

     lowercase__    :   List[str]      =       [os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			) for p in os.listdir(SCREAMING_SNAKE_CASE_			) if (p.endswith('.zip'			) or from_gh)]
     for p in paths:
          selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)			)

     return selected_warnings


if __name__ == "__main__":

       def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



            '''simple docstring'''





            return values.split(','			)

       snake_case_			  = 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.''',
       )

       snake_case_			  = parser.parse_args()

       snake_case_			  = 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
              snake_case_			  = 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
       snake_case_			  = extract_warnings(args.output_dir, args.targets)
       snake_case_			  = 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)

