






'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=13   ,	UpperCamelCase__=30   ,	UpperCamelCase__=2   ,	UpperCamelCase__=3   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=32   ,	UpperCamelCase__=2   ,	UpperCamelCase__=4   ,	UpperCamelCase__=37   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=10   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=3   ,	UpperCamelCase__=0.6   ,	UpperCamelCase__=None   ,	):






         """simple docstring"""


         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_						     =			mask_ratio
         a_						     =			scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         a_						     =			(image_size // patch_size) ** 2
         a_						     =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)







  def    _a       (			self	):






         """simple docstring"""


         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	):






         """simple docstring"""


         return ViTMAEConfig(
             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   ,	decoder_hidden_size=self.hidden_size   ,	decoder_num_hidden_layers=self.num_hidden_layers   ,	decoder_num_attention_heads=self.num_attention_heads   ,	decoder_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   ,	is_decoder=_lowerCamelCase   ,	initializer_range=self.initializer_range   ,	mask_ratio=self.mask_ratio   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			TFViTMAEModel(config=_lowerCamelCase	)
         a_						     =			model(_lowerCamelCase   ,	training=_lowerCamelCase	)
         self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			TFViTMAEForPreTraining(_lowerCamelCase	)
         a_						     =			model(_lowerCamelCase   ,	training=_lowerCamelCase	)
         # expected sequence length = num_patches
         a_						     =			(self.image_size // self.patch_size) ** 2
         a_						     =			self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, num_patches, expected_num_channels)	)

         # test greyscale images
         a_						     =			1
         a_						     =			TFViTMAEForPreTraining(_lowerCamelCase	)

         a_						     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
         a_						     =			model(_lowerCamelCase   ,	training=_lowerCamelCase	)
         a_						     =			self.patch_size**2
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, num_patches, expected_num_channels)	)







  def    _a       (			self	):






         """simple docstring"""


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





@require_tf
class   __SCREAMING_SNAKE_CASE       (__a							,       __a							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Tuple      			=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
  _a  : Any      			=     {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

  _a  : Optional[Any]      			=     False
  _a  : str      			=     False
  _a  : Tuple      			=     False
  _a  : Dict      			=     False







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			TFViTMAEModelTester(self	)
         a_						     =			ConfigTester(self   ,	config_class=_lowerCamelCase   ,	has_text_modality=_lowerCamelCase   ,	hidden_size=37	)







  def    _a       (			self	):






         """simple docstring"""


         self.config_tester.run_common_tests()







  @unittest.skip(reason='ViTMAE does not use inputs_embeds'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(_lowerCamelCase	)
                self.assertIsInstance(model.get_input_embeddings()   ,	(tf.keras.layers.Layer)	)
                a_						     =			model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(_lowerCamelCase   ,	tf.keras.layers.Layer	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

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

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







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase	)







  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         a_						     =			int((config.image_size // config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

         for model_class in self.all_model_classes:
                a_						     =			model_class(_lowerCamelCase	)
                a_						     =			self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase	)

                a_						     =			model(_lowerCamelCase   ,	noise=_lowerCamelCase	)

                a_						     =			copy.deepcopy(self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase	)	)
                a_						     =			model(**_lowerCamelCase   ,	noise=_lowerCamelCase	)
                a_						     =			outputs_dict[0].numpy()
                a_						     =			outputs_keywords[0].numpy()

                self.assertLess(np.sum(np.abs(output_dict - output_keywords	)	)   ,	1e-6	)







  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         a_						     =			int((config.image_size // config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

         def prepare_numpy_arrays(UpperCamelCase__	):
                a_						     =			{}
                for k, v in inputs_dict.items():
                       if tf.is_tensor(_lowerCamelCase	):
                              a_						     =			v.numpy()
                       else:
                              a_						     =			np.array(_lowerCamelCase	)

                return inputs_np_dict

         for model_class in self.all_model_classes:
                a_						     =			model_class(_lowerCamelCase	)

                a_						     =			self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase	)
                a_						     =			prepare_numpy_arrays(_lowerCamelCase	)

                a_						     =			model(_lowerCamelCase   ,	noise=_lowerCamelCase	)
                a_						     =			model(**_lowerCamelCase   ,	noise=_lowerCamelCase	)
                self.assert_outputs_same(_lowerCamelCase   ,	_lowerCamelCase	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         np.random.seed(2	)

         a_						     =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
         a_						     =			tf.constant(_lowerCamelCase	)

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         a_						     =			tf_noise

         super().check_pt_tf_models(_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase	)







  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         a_						     =			{
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__	),)
             for module_member_name in dir(_lowerCamelCase	)
             if module_member_name.endswith('MainLayer'	)
             # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
             and module_member_name[: -len('MainLayer'	)] == model_class.__name__[: -len('Model'	)]
             for module_member in (getattr(_lowerCamelCase   ,	_lowerCamelCase	),)
             if isinstance(_lowerCamelCase   ,	_lowerCamelCase	)
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(_lowerCamelCase   ,	'_keras_serializable'   ,	_lowerCamelCase	)
         }

         a_						     =			int((config.image_size // config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
         a_						     =			tf.convert_to_tensor(_lowerCamelCase	)
         inputs_dict.update({'noise': noise}	)

         for main_layer_class in tf_main_layer_classes:
                a_						     =			main_layer_class(_lowerCamelCase	)

                a_						     =			{
                    name: tf.keras.Input(tensor.shape[1:]   ,	dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
                }

                a_						     =			tf.keras.Model(_lowerCamelCase   ,	outputs=main_layer(_lowerCamelCase	)	)
                a_						     =			model(_lowerCamelCase	)

                with tempfile.TemporaryDirectory() as tmpdirname:
                       a_						     =			os.path.join(_lowerCamelCase   ,	'keras_model.h5'	)
                       model.save(_lowerCamelCase	)
                       a_						     =			tf.keras.models.load_model(
                           _lowerCamelCase   ,	custom_objects={main_layer_class.__name__: main_layer_class}	)
                       assert isinstance(_lowerCamelCase   ,	tf.keras.Model	)
                       a_						     =			model(_lowerCamelCase	)
                       self.assert_outputs_same(_lowerCamelCase   ,	_lowerCamelCase	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         a_						     =			int((config.image_size // config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

         for model_class in self.all_model_classes:
                a_						     =			model_class(_lowerCamelCase	)
                a_						     =			self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase	)
                a_						     =			model(_lowerCamelCase   ,	noise=_lowerCamelCase	)

                if model_class.__name__ == "TFViTMAEModel":
                       a_						     =			outputs.last_hidden_state.numpy()
                       a_						     =			0
                else:
                       a_						     =			outputs.logits.numpy()
                       a_						     =			0

                with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(_lowerCamelCase   ,	saved_model=_lowerCamelCase	)
                       a_						     =			model_class.from_pretrained(_lowerCamelCase	)
                       a_						     =			model(_lowerCamelCase   ,	noise=_lowerCamelCase	)

                       if model_class.__name__ == "TFViTMAEModel":
                              a_						     =			after_outputs['last_hidden_state'].numpy()
                              a_						     =			0
                       else:
                              a_						     =			after_outputs['logits'].numpy()
                              a_						     =			0

                       a_						     =			np.amax(np.abs(out_a - out_a	)	)
                       self.assertLessEqual(_lowerCamelCase   ,	1e-5	)







  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         a_						     =			int((config.image_size // config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

         for model_class in self.all_model_classes:
                a_						     =			model_class(_lowerCamelCase	)
                a_						     =			self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase	)

                a_						     =			model(_lowerCamelCase   ,	noise=_lowerCamelCase	)
                a_						     =			model.get_config()
                # make sure that returned config is jsonifiable, which is required by keras
                json.dumps(_lowerCamelCase	)
                a_						     =			model_class.from_config(model.get_config()	)
                # make sure it also accepts a normal config
                a_						     =			model_class.from_config(model.config	)
                a_						     =			new_model(_lowerCamelCase	)  # Build model
                new_model.set_weights(model.get_weights()	)
                a_						     =			new_model(_lowerCamelCase   ,	noise=_lowerCamelCase	)

                self.assert_outputs_same(_lowerCamelCase   ,	_lowerCamelCase	)







  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'	)
         self.assertIsNotNone(_lowerCamelCase	)





def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image




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


  """simple docstring"""


  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None







  @slow
  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_						     =			TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	)

         a_						     =			self.default_image_processor
         a_						     =			prepare_img()
         a_						     =			image_processor(images=_lowerCamelCase   ,	return_tensors='tf'	)

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         a_						     =			ViTMAEConfig()
         a_						     =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(1, num_patches)	)

         # forward pass
         a_						     =			model(**_lowerCamelCase   ,	noise=_lowerCamelCase	)

         # verify the logits
         a_						     =			tf.convert_to_tensor([1, 196, 768]	)
         self.assertEqual(outputs.logits.shape   ,	_lowerCamelCase	)

         a_						     =			tf.convert_to_tensor(
             [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]	)

         tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,	_lowerCamelCase   ,	atol=1e-4	)





'''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 )





