





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        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       List[Any]    ,  a    :       Any    ,  a    :       Optional[int]=13    ,  a    :       List[str]=32    ,  a    :       int=2    ,  a    :       Optional[Any]=3    ,  a    :       List[str]=16    ,  a    :       Union[str, Any]=[32, 64, 128]    ,  a    :       Tuple=[1, 2, 1]    ,  a    :       Optional[int]=[2, 2, 4]    ,  a    :       Optional[Any]=2    ,  a    :       str=2.0    ,  a    :       Any=True    ,  a    :       List[Any]=0.0    ,  a    :       Tuple=0.0    ,  a    :       str=0.1    ,  a    :       Dict="gelu"    ,  a    :       Tuple=False    ,  a    :       Optional[int]=True    ,  a    :       str=0.02    ,  a    :       int=1E-5    ,  a    :       int=True    ,  a    :       Union[str, Any]=None    ,  a    :       List[Any]=True    ,  a    :       Union[str, Any]=10    ,  a    :       Any=8    ,  a    :       str=["stage1", "stage2"]    ,  a    :       Dict=[1, 2]    ,  )->     str:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						image_size
     lowercase__			    =						patch_size
     lowercase__			    =						num_channels
     lowercase__			    =						embed_dim
     lowercase__			    =						hidden_sizes
     lowercase__			    =						depths
     lowercase__			    =						num_heads
     lowercase__			    =						window_size
     lowercase__			    =						mlp_ratio
     lowercase__			    =						qkv_bias
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						attention_probs_dropout_prob
     lowercase__			    =						drop_path_rate
     lowercase__			    =						hidden_act
     lowercase__			    =						use_absolute_embeddings
     lowercase__			    =						patch_norm
     lowercase__			    =						layer_norm_eps
     lowercase__			    =						initializer_range
     lowercase__			    =						is_training
     lowercase__			    =						scope
     lowercase__			    =						use_labels
     lowercase__			    =						type_sequence_label_size
     lowercase__			    =						encoder_stride
     lowercase__			    =						out_features
     lowercase__			    =						out_indices


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     List[str]:





     """simple docstring"""







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

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

     lowercase__			    =						self.get_config()

     return config, pixel_values, labels


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     str:





     """simple docstring"""







     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  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  a    :       Optional[int]    ,  a    :       int    ,  a    :       int )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						FocalNetModel(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )

     lowercase__			    =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       int    ,  a    :       List[Any]    ,  a    :       Dict )->     Tuple:





     """simple docstring"""







     lowercase__			    =						FocalNetBackbone(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )

     # 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
     lowercase__			    =						None
     lowercase__			    =						FocalNetBackbone(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )

     # 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  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       List[Any]    ,  a    :       Optional[Any]    ,  a    :       int )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						FocalNetForMaskedImageModeling(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )
     self.parent.assertEqual(
         result.reconstruction.shape    ,  (self.batch_size, self.num_channels, self.image_size, self.image_size) )

     # test greyscale images
     lowercase__			    =						1
     lowercase__			    =						FocalNetForMaskedImageModeling(a )
     model.to(a )
     model.eval()

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       Union[str, Any]    ,  a    :       Union[str, Any]    ,  a    :       Dict )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						self.type_sequence_label_size
     lowercase__			    =						FocalNetForImageClassification(a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a    ,  labels=a )
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     lowercase__			    =						1
     lowercase__			    =						FocalNetForImageClassification(a )
     model.to(a )
     model.eval()

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     List[str]:





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()

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






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

   _UpperCamelCase					:	Any				      = False
   _UpperCamelCase					:	Optional[Any]				      = False
   _UpperCamelCase					:	Optional[int]				      = False
   _UpperCamelCase					:	Union[str, Any]				      = False


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						FocalNetModelTester(self )
     lowercase__			    =						ConfigTester(self    ,  config_class=a    ,  embed_dim=37    ,  has_text_modality=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Tuple:





     """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  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Tuple:





     """simple docstring"""







     return


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     List[str]:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*a )


   @unittest.skip(reason='FocalNet does not use inputs_embeds' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Any:





     """simple docstring"""







     pass


   @unittest.skip(reason='FocalNet does not use feedforward chunking' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Tuple:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Any:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Tuple:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

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

       lowercase__			    =						['pixel_values']
       self.assertListEqual(arg_names[:1]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       Optional[Any]    ,  a    :       Any    ,  a    :       int    ,  a    :       List[str] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						model_class(a )
     model.to(a )
     model.eval()

     with torch.no_grad():
       lowercase__			    =						model(**self._prepare_for_class(a    ,  a ) )

     lowercase__			    =						outputs.hidden_states

     lowercase__			    =						getattr(
         self.model_tester    ,  'expected_num_hidden_layers'    ,  len(self.model_tester.depths ) + 1 )
     self.assertEqual(len(a )    ,  a )

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

     lowercase__			    =						(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]    ,  )

     lowercase__			    =						outputs.reshaped_hidden_states
     self.assertEqual(len(a )    ,  a )

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[Any]:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						(
         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]:
       lowercase__			    =						True
       self.check_hidden_states_output(a    ,  a    ,  a    ,  a )

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

       self.check_hidden_states_output(a    ,  a    ,  a    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     str:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__			    =						3

     lowercase__			    =						(
         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)
     )
     lowercase__			    =						(
         config.patch_size
         if isinstance(config.patch_size    ,  collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

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

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

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


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     List[str]:





     """simple docstring"""







     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowercase__			    =						FocalNetModel.from_pretrained(a )
       self.assertIsNotNone(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     str:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						_config_zero_init(a )
     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(config=a )
       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        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   @cached_property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     str:





     """simple docstring"""







     return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny' ) if is_vision_available() else None


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Any:





     """simple docstring"""







     lowercase__			    =						FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny' ).to(a )
     lowercase__			    =						self.default_image_processor

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

     # forward pass
     with torch.no_grad():
       lowercase__			    =						model(**a )

     # verify the logits
     lowercase__			    =						torch.Size((1, 1_000) )
     self.assertEqual(outputs.logits.shape    ,  a )
     lowercase__			    =						torch.tensor([0.2166, -0.4368, 0.2191] ).to(a )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  a    ,  atol=1E-4 ) )
     self.assertTrue(outputs.logits.argmax(dim=-1 ).item()    ,  281 )






@require_torch
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Any				      = (FocalNetBackbone,) if is_torch_available() else ()
   _UpperCamelCase					:	Union[str, Any]				      = FocalNetConfig

   _UpperCamelCase					:	Union[str, Any]				      = False


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     int:





     """simple docstring"""







     lowercase__			    =						FocalNetModelTester(self )








class        SCREAMING_SNAKE_CASE     :  # Public class to implement a graph


   def __init__(					self    :       int    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						row
     lowercase__			    =						col
     lowercase__			    =						graph


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     bool:





     """simple docstring"""







     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     lowercase__			    =						[-1, 0, 1, -1, 1, -1, 0, 1]
     lowercase__			    =						True  # Make those cells visited
     for k in range(8 ):
       if self.is_safe(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a ):
         self.diffs(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     int:  # And finally, count all islands.





     """simple docstring"""







     lowercase__			    =						[[False for j in range(self.COL )] for i in range(self.ROW )]
     lowercase__			    =						0
     for i in range(self.ROW ):
       for j in range(self.COL ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(a    ,  a    ,  a )
           count += 1
     return count


