
"""simple docstring"""
import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
    from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_=13						,    snake_case_=32						,    snake_case_=3						,    snake_case_=4						,    snake_case_=[10, 20, 30, 40]						,    snake_case_=[2, 2, 3, 2]						,    snake_case_=True						,    snake_case_=True						,    snake_case_=37						,    snake_case_="gelu"						,    snake_case_=10						,    snake_case_=0.02						,    snake_case_=["stage2", "stage3", "stage4"]						,    snake_case_=[2, 3, 4]						,    snake_case_=None						,    ):
      _snake_case     :						Dict						=   parent
      _snake_case     :						Tuple						=   batch_size
      _snake_case     :						Optional[int]						=   image_size
      _snake_case     :						Dict						=   num_channels
      _snake_case     :						List[Any]						=   num_stages
      _snake_case     :						str						=   hidden_sizes
      _snake_case     :						Union[str, Any]						=   depths
      _snake_case     :						int						=   is_training
      _snake_case     :						Optional[Any]						=   use_labels
      _snake_case     :						Tuple						=   intermediate_size
      _snake_case     :						Tuple						=   hidden_act
      _snake_case     :						Tuple						=   num_labels
      _snake_case     :						Optional[Any]						=   initializer_range
      _snake_case     :						int						=   out_features
      _snake_case     :						Union[str, Any]						=   out_indices
      _snake_case     :						Union[str, Any]						=   scope
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      _snake_case     :						Optional[int]						=   None
      if self.use_labels:
        _snake_case     :						List[Any]						=   ids_tensor([self.batch_size]						,    self.num_labels     )

      _snake_case     :						Optional[Any]						=   self.get_config()

      return config, pixel_values, labels
    def 					lowerCamelCase__	(    self     ):
      return ConvNextVaConfig(
          num_channels=self.num_channels						,    hidden_sizes=self.hidden_sizes						,    depths=self.depths						,    num_stages=self.num_stages						,    hidden_act=self.hidden_act						,    is_decoder=snake_case_						,    initializer_range=self.initializer_range						,    out_features=self.out_features						,    out_indices=self.out_indices						,    num_labels=self.num_labels						,    )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Union[str, Any]						=   ConvNextVaModel(config=snake_case_     )
      model.to(snake_case_     )
      model.eval()
      _snake_case     :						Optional[Any]						=   model(snake_case_     )
      # 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 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   ConvNextVaForImageClassification(snake_case_     )
      model.to(snake_case_     )
      model.eval()
      _snake_case     :						str						=   model(snake_case_						,    labels=snake_case_     )
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)     )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Tuple						=   ConvNextVaBackbone(config=snake_case_     )
      model.to(snake_case_     )
      model.eval()
      _snake_case     :						Optional[Any]						=   model(snake_case_     )

      # verify hidden states
      self.parent.assertEqual(len(result.feature_maps     )						,    len(config.out_features     )     )
      self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,    [self.batch_size, self.hidden_sizes[1], 4, 4]     )

      # verify channels
      self.parent.assertEqual(len(model.channels     )						,    len(config.out_features     )     )
      self.parent.assertListEqual(model.channels						,    config.hidden_sizes[1:]     )

      # verify backbone works with out_features=None
      _snake_case     :						Any						=   None
      _snake_case     :						str						=   ConvNextVaBackbone(config=snake_case_     )
      model.to(snake_case_     )
      model.eval()
      _snake_case     :						str						=   model(snake_case_     )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps     )						,    1     )
      self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,    [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

      # verify channels
      self.parent.assertEqual(len(model.channels     )						,    1     )
      self.parent.assertListEqual(model.channels						,    [config.hidden_sizes[-1]]     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   self.prepare_config_and_inputs()
      _snake_case     :						str						=   config_and_inputs
      _snake_case     :						Dict						=   {"pixel_values": pixel_values}
      return config, inputs_dict


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[str]						=   self.prepare_config_and_inputs()
      _snake_case     :						Optional[int]						=   config_and_inputs
      _snake_case     :						Union[str, Any]						=   {"pixel_values": pixel_values, "labels": labels}
      return config, inputs_dict






@require_torch
class      _UpperCAmelCase				(					_snake_case		,					_snake_case		,					unittest.TestCase):
    __lowercase							: Dict  =					(
        (
            ConvNextVaModel,
            ConvNextVaForImageClassification,
            ConvNextVaBackbone,
        )
        if is_torch_available()
        else ()
    )
    __lowercase							: str  =					(
        {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
        if is_torch_available()
        else {}
    )

    __lowercase							: Union[str, Any]  =					False
    __lowercase							: str  =					False
    __lowercase							: Tuple  =					False
    __lowercase							: Union[str, Any]  =					False
    __lowercase							: Optional[int]  =					False
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   ConvNextVaModelTester(self     )
      _snake_case     :						List[str]						=   ConfigTester(self						,    config_class=snake_case_						,    has_text_modality=snake_case_						,    hidden_size=37     )
    def 					lowerCamelCase__	(    self     ):
      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 					lowerCamelCase__	(    self     ):
      return
    @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"     )
    def 					lowerCamelCase__	(    self     ):
      pass
    def 					lowerCamelCase__	(    self     ):
      if not self.model_tester.is_training:
        return

      for model_class in self.all_model_classes:
        _snake_case     :						List[Any]						=   self.model_tester.prepare_config_and_inputs_with_labels()
        _snake_case     :						Tuple						=   True

        if model_class.__name__ in [
            *get_values(snake_case_     ),
            *get_values(snake_case_     ),
        ]:
          continue

        _snake_case     :						str						=   model_class(snake_case_     )
        model.to(snake_case_     )
        model.train()
        _snake_case     :						List[Any]						=   self._prepare_for_class(snake_case_						,    snake_case_						,    return_labels=snake_case_     )
        _snake_case     :						List[str]						=   model(**snake_case_     ).loss
        loss.backward()
    def 					lowerCamelCase__	(    self     ):
      if not self.model_tester.is_training:
        return

      for model_class in self.all_model_classes:
        _snake_case     :						str						=   self.model_tester.prepare_config_and_inputs_with_labels()
        _snake_case     :						List[str]						=   False
        _snake_case     :						Optional[int]						=   True

        if (
            model_class.__name__
            in [*get_values(snake_case_     ), *get_values(snake_case_     )]
            or not model_class.supports_gradient_checkpointing
        ):
          continue

        _snake_case     :						Optional[int]						=   model_class(snake_case_     )
        model.to(snake_case_     )
        model.gradient_checkpointing_enable()
        model.train()
        _snake_case     :						Optional[int]						=   self._prepare_for_class(snake_case_						,    snake_case_						,    return_labels=snake_case_     )
        _snake_case     :						List[Any]						=   model(**snake_case_     ).loss
        loss.backward()
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[str]						=   self.model_tester.prepare_config_and_inputs_for_common()

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

        _snake_case     :						Optional[Any]						=   ["pixel_values"]
        self.assertListEqual(arg_names[:1]						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      def check_hidden_states_output(snake_case_						,    snake_case_						,    snake_case_     ):
        _snake_case     :						Optional[Any]						=   model_class(snake_case_     )
        model.to(snake_case_     )
        model.eval()

        with torch.no_grad():
          _snake_case     :						Tuple						=   model(**self._prepare_for_class(snake_case_						,    snake_case_     )     )

        _snake_case     :						Tuple						=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        _snake_case     :						Union[str, Any]						=   self.model_tester.num_stages
        self.assertEqual(len(snake_case_     )						,    expected_num_stages + 1     )

        # ConvNextV2'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]						,    )

      _snake_case     :						Any						=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _snake_case     :						Optional[int]						=   True
        check_hidden_states_output(snake_case_						,    snake_case_						,    snake_case_     )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        _snake_case     :						Optional[Any]						=   True

        check_hidden_states_output(snake_case_						,    snake_case_						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*snake_case_     )


    @slow
    def 					lowerCamelCase__	(    self     ):
      for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _snake_case     :						Dict						=   ConvNextVaModel.from_pretrained(snake_case_     )
        self.assertIsNotNone(snake_case_     )


def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






@require_torch
@require_vision
class      _UpperCAmelCase				(					unittest.TestCase):
    @cached_property
    def 					lowerCamelCase__	(    self     ):
      return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"     ) if is_vision_available() else None


    @slow
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"     ).to(snake_case_     )

      _snake_case     :						Any						=   self.default_image_processor
      _snake_case     :						List[str]						=   prepare_img()
      _snake_case     :						Tuple						=   preprocessor(images=snake_case_						,    return_tensors="pt"     ).to(snake_case_     )

      # forward pass
      with torch.no_grad():
        _snake_case     :						Union[str, Any]						=   model(**snake_case_     )

      # verify the logits
      _snake_case     :						str						=   torch.Size((1, 10_00)     )
      self.assertEqual(outputs.logits.shape						,    snake_case_     )

      _snake_case     :						Any						=   torch.tensor([0.9996, 0.1966, -0.4386]     ).to(snake_case_     )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]						,    snake_case_						,    atol=1E-4     )     )








"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
    _a      :			Dict					=							input("""Enter image url: """).strip()
    print(f'Downloading image from {url} ...')
    _a      :			str					=							BeautifulSoup(requests.get(url).content, """html.parser""")
    # The image URL is in the content field of the first meta tag with property og:image
    _a      :			str					=							soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
    _a      :			Dict					=							requests.get(image_url).content
    _a      :			str					=							f'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
    with open(file_name, """wb""") as fp:
        fp.write(image_data)
    print(f'Done. Image saved to disk as {file_name}.')

