


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 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=13     ,							__UpperCamelCase=30     ,							__UpperCamelCase=2     ,							__UpperCamelCase=3     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=32     ,							__UpperCamelCase=5     ,							__UpperCamelCase=4     ,							__UpperCamelCase=37     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=10     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=None     ,							)       ->    Optional[int]:
          _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  a_					(			self		)       ->    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  a_					(			self		)       ->    Union[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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Any:
          _a						=		ViTMSNModel(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(__UpperCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Optional[int]:
          _a						=		self.type_sequence_label_size
          _a						=		ViTMSNForImageClassification(__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(__UpperCamelCase     ,							labels=__UpperCamelCase		)
          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(__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()

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






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		self.prepare_config_and_inputs()
          _a			,			_a			,			_a						=		config_and_inputs
          _a						=		{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     UpperCAmelCase 					=				(
         {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False






     def  a_					(			self		)       ->    int:
          _a						=		ViTMSNModelTester(self		)
          _a						=		ConfigTester(self     ,							config_class=__UpperCamelCase     ,							has_text_modality=__UpperCamelCase     ,							hidden_size=37		)






     def  a_					(			self		)       ->    str:
          self.config_tester.run_common_tests()






     @unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
     def  a_					(			self		)       ->    int:
          pass






     def  a_					(			self		)       ->    Tuple:
          _a			,			_a						=		self.model_tester.prepare_config_and_inputs_for_common()

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






     def  a_					(			self		)       ->    str:
          _a			,			_a						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _a						=		model_class(__UpperCamelCase		)
               _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]     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)






     @slow
     def  a_					(			self		)       ->    Tuple:




          for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a						=		ViTMSNModel.from_pretrained(__UpperCamelCase		)
               self.assertIsNotNone(__UpperCamelCase		)







def __UpperCamelCase   (			)      ->     Dict:







     '''simple docstring'''






     _a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image






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






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






     @slow
     def  a_					(			self		)       ->    Optional[Any]:
          torch.manual_seed(2		)
          _a						=		ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(__UpperCamelCase		)

          _a						=		self.default_image_processor
          _a						=		prepare_img()
          _a						=		image_processor(images=__UpperCamelCase     ,							return_tensors="pt"		).to(__UpperCamelCase		)

          # forward pass
          with torch.no_grad():
               _a						=		model(**__UpperCamelCase		)

          # verify the logits
          _a						=		torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape     ,							__UpperCamelCase		)

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

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





'''simple docstring'''

from __future__ import annotations

import requests






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     dict:







     '''simple docstring'''






     _a						=		F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
     return requests.get(__lowerCamelCase				).json()






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     list[dict]:







     '''simple docstring'''






     _a						=		"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _a						=		requests.get(__lowerCamelCase				).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase				) for story_id in story_ids]






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     str:







     '''simple docstring'''






     _a						=		hackernews_top_stories(__lowerCamelCase				)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase				) for story in stories				)


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())

