


"""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    :






 """simple docstring"""




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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

     a					=self.get_config()

     return config, pixel_values, labels


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					int:
     a					=FocalNetModel(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A						)

     a					=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					int:
     a					=FocalNetBackbone(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A						)

     # 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
     a					=None
     a					=FocalNetBackbone(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A						)

     # 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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Any:
     a					=FocalNetForMaskedImageModeling(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A						)
     self.parent.assertEqual(
         result.reconstruction.shape			,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

     # test greyscale images
     a					=1
     a					=FocalNetForMaskedImageModeling(__A						)
     model.to(__A						)
     model.eval()

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					str:
     a					=self.type_sequence_label_size
     a					=FocalNetForImageClassification(__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A			,   labels=__A						)
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)						)

     # test greyscale images
     a					=1
     a					=FocalNetForImageClassification(__A						)
     model.to(__A						)
     model.eval()

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





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.prepare_config_and_inputs()

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






@require_torch
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




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

 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=FocalNetModelTester(self						)
     a					=ConfigTester(self			,   config_class=__A			,   embed_dim=37			,   has_text_modality=__A						)


 def 				SCREAMING_SNAKE_CASE							(		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 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     return


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__A						)


 @unittest.skip(reason='''FocalNet does not use inputs_embeds'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     pass


 @unittest.skip(reason='''FocalNet does not use feedforward chunking'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     pass


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

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

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

         a					=['''pixel_values''']
         self.assertListEqual(arg_names[:1]			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=model_class(__A						)
     model.to(__A						)
     model.eval()

     with torch.no_grad():
         a					=model(**self._prepare_for_class(__A			,   __A						)						)

     a					=outputs.hidden_states

     a					=getattr(
         self.model_tester			,   '''expected_num_hidden_layers'''			,   len(self.model_tester.depths						) + 1						)
     self.assertEqual(len(__A						)			,   __A						)

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

     a					=(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]			,   )

     a					=outputs.reshaped_hidden_states
     self.assertEqual(len(__A						)			,   __A						)

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

     a					=(
         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]:
         a					=True
         self.check_hidden_states_output(__A			,   __A			,   __A			,   __A						)

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

         self.check_hidden_states_output(__A			,   __A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()
     a					=3

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

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

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

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


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a					=FocalNetModel.from_pretrained(__A						)
         self.assertIsNotNone(__A						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

     a					=_config_zero_init(__A						)
     for model_class in self.all_model_classes:
         a					=model_class(config=__A						)
         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      ):






 """simple docstring"""




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





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''						).to(__A						)
     a					=self.default_image_processor

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

     # forward pass
     with torch.no_grad():
         a					=model(**__A						)

     # verify the logits
     a					=torch.Size((1, 1000)						)
     self.assertEqual(outputs.logits.shape			,   __A						)
     a					=torch.tensor([0.2_166, -0.4_368, 0.2_191]						).to(__A						)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,   __A			,   atol=1E-4						)						)
     self.assertTrue(outputs.logits.argmax(dim=-1						).item()			,   281						)






@require_torch
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




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

 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=FocalNetModelTester(self						)



"""simple docstring"""







from __future__ import annotations






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A = 0						) ->					Dict:
     a					=key


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''encrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.encrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''decrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.decrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")