'''simple docstring'''

import requests



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      {'''Content-Type''': '''application/json'''}
   lowerCAmelCase_			=      requests.post(__snake_case	,   json={'''text''': message_body}	,   headers=__snake_case)
   if response.status_code != 200:
      lowerCAmelCase_			=      (
          '''Request to slack returned an error '''
          F'''{response.status_code}, the response is:\n{response.text}'''
      )
      raise ValueError(__snake_case)


if __name__ == "__main__":
 # Set the slack url to the one provided by Slack when you create the webhook at
 # https://my.slack.com/services/new/incoming-webhook/
 send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')






'''simple docstring'''

import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=13    ,     _lowerCamelCase=32    ,     _lowerCamelCase=2    ,     _lowerCamelCase=3    ,     _lowerCamelCase=16    ,     _lowerCamelCase=[32, 64, 128]    ,     _lowerCamelCase=[1, 2, 1]    ,     _lowerCamelCase=[2, 2, 4]    ,     _lowerCamelCase=2    ,     _lowerCamelCase=2.0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=1E-5    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase=10    ,     _lowerCamelCase=8    ,     _lowerCamelCase=["stage1", "stage2"]    ,     _lowerCamelCase=[1, 2]    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      patch_size
     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      embed_dim
     lowerCAmelCase_			=      hidden_sizes
     lowerCAmelCase_			=      depths
     lowerCAmelCase_			=      num_heads
     lowerCAmelCase_			=      window_size
     lowerCAmelCase_			=      mlp_ratio
     lowerCAmelCase_			=      qkv_bias
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      drop_path_rate
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      use_absolute_embeddings
     lowerCAmelCase_			=      patch_norm
     lowerCAmelCase_			=      layer_norm_eps
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      scope
     lowerCAmelCase_			=      use_labels
     lowerCAmelCase_			=      type_sequence_label_size
     lowerCAmelCase_			=      encoder_stride
     lowerCAmelCase_			=      out_features
     lowerCAmelCase_			=      out_indices





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     lowerCAmelCase_			=      self.get_config()

     return config, pixel_values, labels





  def 						UpperCAmelCase_	(	self ):
     return FocalNetConfig(
         image_size=self.image_size    ,     patch_size=self.patch_size    ,     num_channels=self.num_channels    ,     embed_dim=self.embed_dim    ,     hidden_sizes=self.hidden_sizes    ,     depths=self.depths    ,     num_heads=self.num_heads    ,     window_size=self.window_size    ,     mlp_ratio=self.mlp_ratio    ,     qkv_bias=self.qkv_bias    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     drop_path_rate=self.drop_path_rate    ,     hidden_act=self.hidden_act    ,     use_absolute_embeddings=self.use_absolute_embeddings    ,     path_norm=self.patch_norm    ,     layer_norm_eps=self.layer_norm_eps    ,     initializer_range=self.initializer_range    ,     encoder_stride=self.encoder_stride    ,     out_features=self.out_features    ,     out_indices=self.out_indices    ,     )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      FocalNetModel(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )

     lowerCAmelCase_			=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
     lowerCAmelCase_			=      int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

     self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, expected_seq_len, expected_dim) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      FocalNetBackbone(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps )    ,     len(config.out_features ) )
     self.parent.assertListEqual(list(result.feature_maps[0].shape )    ,     [self.batch_size, self.image_size, 8, 8] )

     # 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
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      FocalNetBackbone(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps )    ,     1 )
     self.parent.assertListEqual(list(result.feature_maps[0].shape )    ,     [self.batch_size, self.image_size * 2, 4, 4] )

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





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      FocalNetForMaskedImageModeling(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )
     self.parent.assertEqual(
         result.reconstruction.shape    ,     (self.batch_size, self.num_channels, self.image_size, self.image_size) )

     # test greyscale images
     lowerCAmelCase_			=      1
     lowerCAmelCase_			=      FocalNetForMaskedImageModeling(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

     lowerCAmelCase_			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowerCAmelCase_			=      model(_lowerCamelCase )
     self.parent.assertEqual(result.reconstruction.shape    ,     (self.batch_size, 1, self.image_size, self.image_size) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.type_sequence_label_size
     lowerCAmelCase_			=      FocalNetForImageClassification(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase    ,     labels=_lowerCamelCase )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     lowerCAmelCase_			=      1
     lowerCAmelCase_			=      FocalNetForImageClassification(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

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







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.prepare_config_and_inputs()

     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      config_and_inputs
     lowerCAmelCase_			=      {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class     __UpperCAmelCase       ( __a						,       __a						,       unittest.TestCase		):
  __A :				Tuple											= (
      (
          FocalNetModel,
          FocalNetForImageClassification,
          FocalNetForMaskedImageModeling,
          FocalNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  __A :				Any											= (
      {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
      if is_torch_available()
      else {}
  )
  __A :				List[str]											= False

  __A :				Optional[Any]											= False
  __A :				Union[str, Any]											= False
  __A :				List[str]											= False
  __A :				Optional[Any]											= False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      FocalNetModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=_lowerCamelCase    ,     embed_dim=37    ,     has_text_modality=_lowerCamelCase )





  def 						UpperCAmelCase_	(	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 						UpperCAmelCase_	(	self ):
     return





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )





  @unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      model_class(_lowerCamelCase )
        self.assertIsInstance(model.get_input_embeddings()    ,     (nn.Module) )
        lowerCAmelCase_			=      model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_lowerCamelCase    ,     nn.Linear ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      model_class(_lowerCamelCase )
        lowerCAmelCase_			=      inspect.signature(model.forward )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCAmelCase_			=      [*signature.parameters.keys()]

        lowerCAmelCase_			=      ['''pixel_values''']
        self.assertListEqual(arg_names[:1]    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      model_class(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

     with torch.no_grad():
        lowerCAmelCase_			=      model(**self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase ) )

     lowerCAmelCase_			=      outputs.hidden_states

     lowerCAmelCase_			=      getattr(
         self.model_tester    ,     '''expected_num_hidden_layers'''    ,     len(self.model_tester.depths ) + 1 )
     self.assertEqual(len(_lowerCamelCase )    ,     _lowerCamelCase )

     # FocalNet has a different seq_length
     lowerCAmelCase_			=      (
         config.patch_size
         if isinstance(config.patch_size    ,     collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

     lowerCAmelCase_			=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:] )    ,     [num_patches, self.model_tester.embed_dim]    ,     )

     lowerCAmelCase_			=      outputs.reshaped_hidden_states
     self.assertEqual(len(_lowerCamelCase )    ,     _lowerCamelCase )

     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      reshaped_hidden_states[0].shape
     lowerCAmelCase_			=      (
         reshaped_hidden_states[0].view(_lowerCamelCase    ,     _lowerCamelCase    ,     height * width ).permute(0    ,     2    ,     1 )
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:] )    ,     [num_patches, self.model_tester.embed_dim]    ,     )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     lowerCAmelCase_			=      (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size    ,     collections.abc.Iterable )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      True
        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

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

        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()
     lowerCAmelCase_			=      3

     lowerCAmelCase_			=      (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size    ,     collections.abc.Iterable )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     lowerCAmelCase_			=      (
         config.patch_size
         if isinstance(config.patch_size    ,     collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

     lowerCAmelCase_			=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     lowerCAmelCase_			=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      True
        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     (padded_height, padded_width) )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowerCAmelCase_			=      True
        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     (padded_height, padded_width) )





  @slow
  def 						UpperCAmelCase_	(	self ):
     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_			=      FocalNetModel.from_pretrained(_lowerCamelCase )
        self.assertIsNotNone(_lowerCamelCase )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     lowerCAmelCase_			=      _config_zero_init(_lowerCamelCase )
     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      model_class(config=_lowerCamelCase )
        for name, param in model.named_parameters():
           if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()    ,     [0.0, 1.0]    ,     msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,     )






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





  @cached_property
  def 						UpperCAmelCase_	(	self ):
     # TODO update organization
     return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None







  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      self.default_image_processor

     lowerCAmelCase_			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     lowerCAmelCase_			=      image_processor(images=_lowerCamelCase    ,     return_tensors='''pt''' ).to(_lowerCamelCase )

     # forward pass
     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )

     # verify the logits
     lowerCAmelCase_			=      torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape    ,     _lowerCamelCase )
     lowerCAmelCase_			=      torch.tensor([0.21_66, -0.43_68, 0.21_91] ).to(_lowerCamelCase )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,     _lowerCamelCase    ,     atol=1E-4 ) )
     self.assertTrue(outputs.logits.argmax(dim=-1 ).item()    ,     281 )






@require_torch
class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				Dict											= (FocalNetBackbone,) if is_torch_available() else ()
  __A :				List[str]											= FocalNetConfig

  __A :				Optional[int]											= False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      FocalNetModelTester(self )





