

"""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 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__=[32, 64, 128]    ,						SCREAMING_SNAKE_CASE__=[1, 2, 1]    ,						SCREAMING_SNAKE_CASE__=[2, 2, 4]    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=2.0    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1e-5    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=["stage1", "stage2"]    ,						SCREAMING_SNAKE_CASE__=[1, 2]    ,						)   ->					Tuple:
         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 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         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 					_lowerCamelCase  (  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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			FocalNetModel(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)

         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			FocalNetBackbone(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_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
         lowerCamelCase__						=			None
         lowerCamelCase__						=			FocalNetBackbone(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			FocalNetForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         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(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.type_sequence_label_size
         lowerCamelCase__						=			FocalNetForImageClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)		)

         # test greyscale images
         lowerCamelCase__						=			1
         lowerCamelCase__						=			FocalNetForImageClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

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





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			self.prepare_config_and_inputs()

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






@require_torch
class 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      __a								=	(
          {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      __a								=	False

      __a								=	False
      __a								=	False
      __a								=	False
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			FocalNetModelTester(self		)
         lowerCamelCase__						=			ConfigTester(self    ,						config_class=SCREAMING_SNAKE_CASE__    ,						embed_dim=37    ,						has_text_modality=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         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		)   ->					Union[str, Any]:
         return





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__		)





      @unittest.skip(reason="FocalNet does not use inputs_embeds"		)
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         pass





      @unittest.skip(reason="FocalNet does not use feedforward chunking"		)
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         pass





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

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





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes[:-1]:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            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]    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					str:
         lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

         with torch.no_grad():
            lowerCamelCase__						=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)		)

         lowerCamelCase__						=			outputs.hidden_states

         lowerCamelCase__						=			getattr(
             self.model_tester    ,						"expected_num_hidden_layers"    ,						len(self.model_tester.depths		) + 1		)
         self.assertEqual(len(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)

         # 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(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			reshaped_hidden_states[0].shape
         lowerCamelCase__						=			(
             reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__    ,						SCREAMING_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		)   ->					Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

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

            self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						(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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						(padded_height, padded_width)		)





      @slow
      def 					_lowerCamelCase  (  self		)   ->					int:
         for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCamelCase__						=			FocalNetModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()

         lowerCamelCase__						=			_config_zero_init(SCREAMING_SNAKE_CASE__		)
         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(config=SCREAMING_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 		_A       (			unittest.TestCase					):





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





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.default_image_processor

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

         # forward pass
         with torch.no_grad():
            lowerCamelCase__						=			model(**SCREAMING_SNAKE_CASE__		)

         # verify the logits
         lowerCamelCase__						=			torch.Size((1, 1000)		)
         self.assertEqual(outputs.logits.shape    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			torch.tensor([0.21_66, -0.43_68, 0.21_91]		).to(SCREAMING_SNAKE_CASE__		)
         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-4		)		)
         self.assertTrue(outputs.logits.argmax(dim=-1		).item()    ,						281		)






@require_torch
class 		_A       (			__a				,       unittest.TestCase					):
      __a								=	(FocalNetBackbone,) if is_torch_available() else ()
      __a								=	FocalNetConfig

      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			FocalNetModelTester(self		)







"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




