



'''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_			:
    """simple docstring"""







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




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Dict       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          snake_case__    :       Union[str, Any]       		= None
          if self.use_labels:
                snake_case__    :       int       		= ids_tensor([self.batch_size]					,	self.type_sequence_label_size		)

          snake_case__    :       Dict       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	int		):
          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 lowerCamelCase			(       self   :	Any					,	snake_case_   :	str					,	snake_case_   :	Dict					,	snake_case_   :	Any		):
          snake_case__    :       int       		= FocalNetModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Dict       		= model(snake_case_		)

          snake_case__    :       Union[str, Any]       		= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
          snake_case__    :       int       		= 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 lowerCamelCase			(       self   :	str					,	snake_case_   :	int					,	snake_case_   :	int					,	snake_case_   :	List[Any]		):
          snake_case__    :       List[str]       		= FocalNetBackbone(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_		)

          # 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
          snake_case__    :       Union[str, Any]       		= None
          snake_case__    :       Any       		= FocalNetBackbone(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       int       		= model(snake_case_		)

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

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




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Optional[int]					,	snake_case_   :	List[Any]					,	snake_case_   :	str		):
          snake_case__    :       Optional[Any]       		= FocalNetForMaskedImageModeling(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_		)
          self.parent.assertEqual(
              result.reconstruction.shape					,	(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

          # test greyscale images
          snake_case__    :       Tuple       		= 1
          snake_case__    :       Tuple       		= FocalNetForMaskedImageModeling(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       Optional[int]       		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          snake_case__    :       List[str]       		= model(snake_case_		)
          self.parent.assertEqual(result.reconstruction.shape					,	(self.batch_size, 1, self.image_size, self.image_size)		)




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	List[str]					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Optional[int]       		= self.type_sequence_label_size
          snake_case__    :       Optional[int]       		= FocalNetForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)		)

          # test greyscale images
          snake_case__    :       List[Any]       		= 1
          snake_case__    :       Any       		= FocalNetForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       int       		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          snake_case__    :       List[Any]       		= model(snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       List[str]       		= self.prepare_config_and_inputs()

          snake_case__		,							snake_case__		,							snake_case__    :       Union[str, Any]       		= config_and_inputs
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    lowercase   =						(
        {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    lowercase   =						False

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       Union[str, Any]       		= FocalNetModelTester(self		)
          snake_case__    :       Optional[Any]       		= ConfigTester(self					,	config_class=snake_case_					,	embed_dim=37					,	has_text_modality=snake_case_		)




    def lowerCamelCase			(       self   :	int		):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()




    def lowerCamelCase			(       self   :	Optional[Any]		):
          return




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Optional[Any]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @unittest.skip(reason="""FocalNet does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	int		):
          pass




    @unittest.skip(reason="""FocalNet does not use feedforward chunking"""		)
    def lowerCamelCase			(       self   :	str		):
          pass




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__		,							snake_case__    :       Union[str, Any]       		= self.model_tester.prepare_config_and_inputs_for_common()

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




    def lowerCamelCase			(       self   :	Any		):
          snake_case__		,							snake_case__    :       int       		= self.model_tester.prepare_config_and_inputs_for_common()

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

                snake_case__    :       Optional[Any]       		= ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]					,	snake_case_		)




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	List[str]					,	snake_case_   :	Dict					,	snake_case_   :	Dict					,	snake_case_   :	Any		):
          snake_case__    :       List[str]       		= model_class(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          with torch.no_grad():
                snake_case__    :       Any       		= model(**self._prepare_for_class(snake_case_					,	snake_case_		)		)

          snake_case__    :       Dict       		= outputs.hidden_states

          snake_case__    :       Dict       		= getattr(
              self.model_tester					,	"""expected_num_hidden_layers"""					,	len(self.model_tester.depths		) + 1		)
          self.assertEqual(len(snake_case_		)					,	snake_case_		)

          # FocalNet has a different seq_length
          snake_case__    :       Union[str, Any]       		= (
              config.patch_size
              if isinstance(config.patch_size					,	collections.abc.Iterable		)
              else (config.patch_size, config.patch_size)
          )

          snake_case__    :       Dict       		= (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]					,	)

          snake_case__    :       List[str]       		= outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case_		)					,	snake_case_		)

          snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       Optional[int]       		= reshaped_hidden_states[0].shape
          snake_case__    :       Optional[Any]       		= (
              reshaped_hidden_states[0].view(snake_case_					,	snake_case_					,	height * width		).permute(0					,	2					,	1		)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]		)					,	[num_patches, self.model_tester.embed_dim]					,	)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__		,							snake_case__    :       Optional[Any]       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       List[Any]       		= (
              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]:
                snake_case__    :       Tuple       		= True
                self.check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

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

                self.check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__		,							snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__    :       Optional[Any]       		= 3

          snake_case__    :       Union[str, Any]       		= (
              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)
          )
          snake_case__    :       int       		= (
              config.patch_size
              if isinstance(config.patch_size					,	collections.abc.Iterable		)
              else (config.patch_size, config.patch_size)
          )

          snake_case__    :       List[str]       		= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          snake_case__    :       Dict       		= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes[:-1]:
                snake_case__    :       Dict       		= True
                self.check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_					,	(padded_height, padded_width)		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case__    :       Dict       		= True
                self.check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_					,	(padded_height, padded_width)		)




    @slow
    def lowerCamelCase			(       self   :	Any		):
          for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       List[str]       		= FocalNetModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__		,							snake_case__    :       Optional[Any]       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       Any       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Dict       		= model_class(config=snake_case_		)
                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      ):
    """simple docstring"""







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




    @slow
    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Dict       		= FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""		).to(snake_case_		)
          snake_case__    :       Dict       		= self.default_image_processor

          snake_case__    :       str       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
          snake_case__    :       Optional[Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Tuple       		= model(**snake_case_		)

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







@require_torch
class        UpperCAmelCase_			(			_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(FocalNetBackbone,) if is_torch_available() else ()
    lowercase   =						FocalNetConfig

    lowercase   =						False




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Tuple       		= FocalNetModelTester(self		)





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
