





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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        _UpperCAmelCase           =   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				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   FocalNetModel(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase					)

        _UpperCAmelCase           =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
        _UpperCAmelCase           =   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				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   FocalNetBackbone(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase					)

        # 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
        _UpperCAmelCase           =   None
        _UpperCAmelCase           =   FocalNetBackbone(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase					)

        # 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				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   FocalNetForMaskedImageModeling(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase					)
        self.parent.assertEqual(
            result.reconstruction.shape				,							(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

        # test greyscale images
        _UpperCAmelCase           =   1
        _UpperCAmelCase           =   FocalNetForMaskedImageModeling(_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.type_sequence_label_size
        _UpperCAmelCase           =   FocalNetForImageClassification(_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							labels=_UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size)					)

        # test greyscale images
        _UpperCAmelCase           =   1
        _UpperCAmelCase           =   FocalNetForImageClassification(_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()

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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.prepare_config_and_inputs()

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




@require_torch
class 			__UpperCamelCase    (							A__	, A__	, unittest.TestCase    ):
     __A	:  List[str]							  =   (
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     __A	:  Union[str, Any]							  =   (
         {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     __A	:  int							  =   False

     __A	:  int							  =   False
     __A	:  Dict							  =   False
     __A	:  str							  =   False
     __A	:  Tuple							  =   False





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   FocalNetModelTester(self					)
        _UpperCAmelCase           =   ConfigTester(self				,							config_class=_UpperCamelCase				,							embed_dim=37				,							has_text_modality=_UpperCamelCase					)





     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					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase					)





     @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					):
        _UpperCAmelCase       ,      _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_common()

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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase       ,      _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_common()

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

           _UpperCAmelCase           =   ['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   model_class(_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()

        with torch.no_grad():
           _UpperCAmelCase           =   model(**self._prepare_for_class(_UpperCamelCase				,							_UpperCamelCase					)					)

        _UpperCAmelCase           =   outputs.hidden_states

        _UpperCAmelCase           =   getattr(
            self.model_tester				,							'''expected_num_hidden_layers'''				,							len(self.model_tester.depths					) + 1					)
        self.assertEqual(len(_UpperCamelCase					)				,							_UpperCamelCase					)

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

        _UpperCAmelCase           =   (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]				,							)

        _UpperCAmelCase           =   outputs.reshaped_hidden_states
        self.assertEqual(len(_UpperCamelCase					)				,							_UpperCamelCase					)

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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase       ,      _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase           =   (
            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]:
           _UpperCAmelCase           =   True
           self.check_hidden_states_output(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)

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

           self.check_hidden_states_output(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase       ,      _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase           =   3

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

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

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

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





     @slow
     def 		UpperCamelCase(     self					):
        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase           =   FocalNetModel.from_pretrained(_UpperCamelCase					)
           self.assertIsNotNone(_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase       ,      _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase           =   _config_zero_init(_UpperCamelCase					)
        for model_class in self.all_model_classes:
           _UpperCAmelCase           =   model_class(config=_UpperCamelCase					)
           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					):
        _UpperCAmelCase           =   FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''					).to(_UpperCamelCase					)
        _UpperCAmelCase           =   self.default_image_processor

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

        # forward pass
        with torch.no_grad():
           _UpperCAmelCase           =   model(**_UpperCamelCase					)

        # verify the logits
        _UpperCAmelCase           =   torch.Size((1, 1000)					)
        self.assertEqual(outputs.logits.shape				,							_UpperCamelCase					)
        _UpperCAmelCase           =   torch.tensor([0.2166, -0.4368, 0.2191]					).to(_UpperCamelCase					)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]				,							_UpperCamelCase				,							atol=1e-4					)					)
        self.assertTrue(outputs.logits.argmax(dim=-1					).item()				,							281					)




@require_torch
class 			__UpperCamelCase    (							A__	, unittest.TestCase    ):
     __A	:  Optional[int]							  =   (FocalNetBackbone,) if is_torch_available() else ()
     __A	:  str							  =   FocalNetConfig

     __A	:  Any							  =   False





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   FocalNetModelTester(self					)



'''simple docstring'''
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class 					__lowerCAmelCase   (   unittest.TestCase				,			lowercase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		load_tool("text-classification"	)
													self.tool.setup()
													A_			    =		load_tool("text-classification" ,  remote=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.remote_tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.remote_tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)





