




'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFResNetForImageClassification, TFResNetModel
   from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class     _a						:
       def __init__(			self		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			str=3     ,       lowercase		:			Any=32     ,       lowercase		:			Optional[int]=3     ,       lowercase		:			str=10     ,       lowercase		:			Tuple=[10, 20, 30, 40]     ,       lowercase		:			int=[1, 1, 2, 1]     ,       lowercase		:			int=True     ,       lowercase		:			List[str]=True     ,       lowercase		:			str="relu"     ,       lowercase		:			Optional[int]=3     ,       lowercase		:			Dict=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	embeddings_size
           UpperCAmelCase     =	hidden_sizes
           UpperCAmelCase     =	depths
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	scope
           UpperCAmelCase     =	len(lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           UpperCAmelCase     =	None
           if self.use_labels:
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.num_labels       )

           UpperCAmelCase     =	self.get_config()

           return config, pixel_values, labels
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           return ResNetConfig(
               num_channels=self.num_channels     ,       embeddings_size=self.embeddings_size     ,       hidden_sizes=self.hidden_sizes     ,       depths=self.depths     ,       hidden_act=self.hidden_act     ,       num_labels=self.num_labels     ,       image_size=self.image_size     ,       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFResNetModel(config=lowercase       )
           UpperCAmelCase     =	model(lowercase       )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,       )
       def 					A   (			self		:			Any     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	TFResNetForImageClassification(lowercase       )
           UpperCAmelCase     =	model(lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )







       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs
           UpperCAmelCase     =	{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_tf
class     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  Tuple            =      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
       __a       :  Dict            =      (
           {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
           if is_tf_available()
           else {}
       )

       __a       :  Optional[int]            =      False
       __a       :  Dict            =      False
       __a       :  List[Any]            =      False
       __a       :  str            =      False
       __a       :  str            =      False
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFResNetModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       has_text_modality=lowercase       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return
       @unittest.skip(reason='''ResNet does not use inputs_embeds'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''ResNet does not support input and output embeddings'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase     =	['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,       lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           def check_hidden_states_output(lowercase		:			Tuple     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Optional[Any]       ):
               UpperCAmelCase     =	model_class(lowercase       )
               UpperCAmelCase     =	model(**self._prepare_for_class(lowercase     ,       lowercase       )       )

               UpperCAmelCase     =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               UpperCAmelCase     =	self.model_tester.num_stages
               self.assertEqual(len(lowercase       )     ,       expected_num_stages + 1       )

               # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]       )     ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,       )

           UpperCAmelCase ,  UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase     =	['''basic''', '''bottleneck''']
           for model_class in self.all_model_classes:
               for layer_type in layers_type:
                   UpperCAmelCase     =	layer_type
                   UpperCAmelCase     =	True
                   check_hidden_states_output(lowercase     ,       lowercase     ,       lowercase       )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   UpperCAmelCase     =	True

                   check_hidden_states_output(lowercase     ,       lowercase     ,       lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase       )







       @slow
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	TFResNetModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )






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







@require_tf
@require_vision
class     _a						(			unittest.TestCase				):
       @cached_property
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
               if is_vision_available()
               else None
           )







       @slow
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

           UpperCAmelCase     =	self.default_image_processor
           UpperCAmelCase     =	prepare_img()
           UpperCAmelCase     =	image_processor(images=lowercase     ,       return_tensors='''tf'''       )

           # forward pass
           UpperCAmelCase     =	model(**lowercase       )

           # verify the logits
           UpperCAmelCase     =	tf.TensorShape((1, 1_000)       )
           self.assertEqual(outputs.logits.shape     ,       lowercase       )

           UpperCAmelCase     =	tf.constant([-11.1069, -9.7877, -8.3777]       )

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     ,       lowercase     ,       atol=1E-4       )       )










'''simple docstring'''







from __future__ import annotations

import requests





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





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    UpperCAmelCase     =	requests.get(_a  ).json()[:max_stories]
    return [get_hackernews_story(_a  ) for story_id in story_ids]





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	hackernews_top_stories(_a  )
    return "\n".join('''* [{title}]({url})'''.format(**_a  ) for story in stories  )


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




