


'''simple docstring'''





import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE__      					=     "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__      					=     direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE__      					=     transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE__      					=     {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)




def        lowerCamelCase  (				_snake_case  :						Dict     ,_snake_case  :						List[str]     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__		       =						False
    for attribute in attributes:
        for modeling_source in source_strings:
            # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
            if (
                f'''config.{attribute}''' in modeling_source
                or f'''getattr(config, "{attribute}"''' in modeling_source
                or f'''getattr(self.config, "{attribute}"''' in modeling_source
            ):
                lowercase__		       =						True
            # Deal with multi-line cases
            elif (
                re.search(
                    Rf'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''     ,_snake_case     ,)
                is not None
            ):
                lowercase__		       =						True
            # `SequenceSummary` is called with `SequenceSummary(config)`
            elif attribute in [
                "summary_type",
                "summary_use_proj",
                "summary_activation",
                "summary_last_dropout",
                "summary_proj_to_labels",
                "summary_first_dropout",
            ]:
                if "SequenceSummary" in modeling_source:
                    lowercase__		       =						True
            if attribute_used:
                break
        if attribute_used:
            break

    # common and important attributes, even if they do not always appear in the modeling files
    lowercase__		       =						[
        "bos_index",
        "eos_index",
        "pad_index",
        "unk_index",
        "mask_index",
        "image_size",
        "use_cache",
        "out_features",
        "out_indices",
    ]
    lowercase__		       =						["encoder_no_repeat_ngram_size"]

    # Special cases to be allowed
    lowercase__		       =						True
    if not attribute_used:
        lowercase__		       =						False
        for attribute in attributes:
            # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
            if attribute in ["is_encoder_decoder"] and default_value is True:
                lowercase__		       =						True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                lowercase__		       =						True

            # Allow cases without checking the default value in the configuration class
            elif attribute in attributes_to_allow + attributes_used_in_generation:
                lowercase__		       =						True
            elif attribute.endswith("_token_id"       ):
                lowercase__		       =						True

            # configuration class specific cases
            if not case_allowed:
                lowercase__		       =						SPECIAL_CASES_TO_ALLOW.get(config_class.__name__     ,[]       )
                lowercase__		       =						allowed_cases is True or attribute in allowed_cases

    return attribute_used or case_allowed




def        lowerCamelCase  (				_snake_case  :						Dict       ):





    '''simple docstring'''


    lowercase__		       =						dict(inspect.signature(config_class.__init__       ).parameters       )
    lowercase__		       =						[x for x in list(signature.keys()       ) if x not in ["self", "kwargs"]]
    lowercase__		       =						[signature[param].default for param in parameter_names]

    # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
    # as one variant is used, the test should pass
    lowercase__		       =						{}
    if len(config_class.attribute_map       ) > 0:
        lowercase__		       =						{v: k for k, v in config_class.attribute_map.items()}

    # Get the path to modeling source files
    lowercase__		       =						inspect.getsourcefile(_snake_case       )
    lowercase__		       =						os.path.dirname(_snake_case       )
    # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
    lowercase__		       =						[os.path.join(_snake_case     ,_snake_case       ) for fn in os.listdir(_snake_case       ) if fn.startswith("modeling_"       )]

    # Get the source code strings
    lowercase__		       =						[]
    for path in modeling_paths:
        if os.path.isfile(_snake_case       ):
            with open(_snake_case       ) as fp:
                modeling_sources.append(fp.read()       )

    lowercase__		       =						[]
    for config_param, default_value in zip(_snake_case     ,_snake_case       ):
        # `attributes` here is all the variant names for `config_param`
        lowercase__		       =						[config_param]
        # some configuration classes have non-empty `attribute_map`, and both names could be used in the
        # corresponding modeling files. As long as one of them appears, it is fine.
        if config_param in reversed_attribute_map:
            attributes.append(reversed_attribute_map[config_param]       )

        if not check_attribute_being_used(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       ):
            unused_attributes.append(attributes[0]       )

    return sorted(_snake_case       )




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						{}
    for _config_class in list(CONFIG_MAPPING.values()       ):
        # Skip deprecated models
        if "models.deprecated" in _config_class.__module__:
            continue
        # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
        lowercase__		       =						[
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class       )     ,lambda _snake_case       : inspect.isclass(_snake_case       )
                and issubclass(_snake_case     ,_snake_case       )
                and inspect.getmodule(_snake_case       ) == inspect.getmodule(_config_class       )     ,)
        ]
        for config_class in config_classes_in_module:
            lowercase__		       =						check_config_attributes_being_used(_snake_case       )
            if len(_snake_case       ) > 0:
                lowercase__		       =						unused_attributes

    if len(_snake_case       ) > 0:
        lowercase__		       =						"The following configuration classes contain unused attributes in the corresponding modeling files:\n"
        for name, attributes in configs_with_unused_attributes.items():
            error += f'''{name}: {attributes}\n'''

        raise ValueError(_snake_case       )


if __name__ == "__main__":
 check_config_attributes()









'''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 				snake_case :



       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]     ,)  ->							Dict:
           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 							_a      (				self      )  ->							int:
           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 							_a      (				self      )  ->							Any:
           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           lowercase__		       =						FocalNetModel(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )

           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						FocalNetBackbone(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						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
           lowercase__		       =						None
           lowercase__		       =						FocalNetBackbone(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						FocalNetForMaskedImageModeling(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           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(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						FocalNetForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

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

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







       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.prepare_config_and_inputs()

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





@require_torch
class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :List[str]						   =					(
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__  :str						   =					(
           {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__  :str						   =					False

       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Dict						   =					False
       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Union[str, Any]						   =					False



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						FocalNetModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,embed_dim=37     ,has_text_modality=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[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 							_a      (				self      )  ->							List[Any]:
           return



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						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 							_a      (				self      )  ->							str:
           pass



       @unittest.skip(reason="FocalNet does not use feedforward chunking"      )
       def 							_a      (				self      )  ->							Optional[int]:
           pass



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               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]     ,UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						model_class(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

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

           lowercase__		       =						outputs.hidden_states

           lowercase__		       =						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
           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(UpperCAmelCase_      )     ,UpperCAmelCase_      )

           lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						reshaped_hidden_states[0].shape
           lowercase__		       =						(
               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 							_a      (				self      )  ->							str:
           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(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )

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

               self.check_hidden_states_output(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           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(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,(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(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,(padded_height, padded_width)      )



       @slow
       def 							_a      (				self      )  ->							Dict:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__		       =						FocalNetModel.from_pretrained(UpperCAmelCase_      )
               self.assertIsNotNone(UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__		       =						_config_zero_init(UpperCAmelCase_      )
           for model_class in self.all_model_classes:
               lowercase__		       =						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 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Optional[int]:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"      ) if is_vision_available() else None







       @slow
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"      ).to(UpperCAmelCase_      )
           lowercase__		       =						self.default_image_processor

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

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

           # verify the logits
           lowercase__		       =						torch.Size((1, 1_000)      )
           self.assertEqual(outputs.logits.shape     ,UpperCAmelCase_      )
           lowercase__		       =						torch.tensor([0.21_66, -0.43_68, 0.21_91]      ).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 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Tuple						   =					(FocalNetBackbone,) if is_torch_available() else ()
       lowerCAmelCase__  :int						   =					FocalNetConfig

       lowerCAmelCase__  :List[Any]						   =					False



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						FocalNetModelTester(self      )




