


"""simple docstring"""







import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
           MODEL_MAPPING,
           BeitForImageClassification,
           BeitForMaskedImageModeling,
           BeitForSemanticSegmentation,
           BeitModel,
       )
       from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       import PIL
       from PIL import Image

       from transformers import BeitImageProcessor






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=100			,   __A=13			,   __A=30			,   __A=2			,   __A=3			,   __A=True			,   __A=True			,   __A=32			,   __A=4			,   __A=4			,   __A=37			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=10			,   __A=0.02			,   __A=3			,   __A=None			,   __A=[0, 1, 2, 3]			,   ) ->					Union[str, Any]:
     a					=parent
     a					=100
     a					=batch_size
     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=is_training
     a					=use_labels
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=type_sequence_label_size
     a					=initializer_range
     a					=scope
     a					=out_indices
     a					=num_labels

     # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     a					=(image_size // patch_size) ** 2
     a					=num_patches + 1


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

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

     a					=self.get_config()

     return config, pixel_values, labels, pixel_labels


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     return BeitConfig(
         vocab_size=self.vocab_size			,   image_size=self.image_size			,   patch_size=self.patch_size			,   num_channels=self.num_channels			,   hidden_size=self.hidden_size			,   num_hidden_layers=self.num_hidden_layers			,   num_attention_heads=self.num_attention_heads			,   intermediate_size=self.intermediate_size			,   hidden_act=self.hidden_act			,   hidden_dropout_prob=self.hidden_dropout_prob			,   attention_probs_dropout_prob=self.attention_probs_dropout_prob			,   is_decoder=SCREAMING_SNAKE_CASE_			,   initializer_range=self.initializer_range			,   out_indices=self.out_indices			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=BeitModel(config=SCREAMING_SNAKE_CASE_						)
     model.to(SCREAMING_SNAKE_CASE_						)
     model.eval()
     a					=model(SCREAMING_SNAKE_CASE_						)
     self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					Dict:
     a					=BeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_						)
     model.to(SCREAMING_SNAKE_CASE_						)
     model.eval()
     a					=model(SCREAMING_SNAKE_CASE_						)
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.seq_length - 1, self.vocab_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     a					=self.type_sequence_label_size
     a					=BeitForImageClassification(SCREAMING_SNAKE_CASE_						)
     model.to(SCREAMING_SNAKE_CASE_						)
     model.eval()
     a					=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
     a					=1
     a					=BeitForImageClassification(SCREAMING_SNAKE_CASE_						)
     model.to(SCREAMING_SNAKE_CASE_						)
     model.eval()

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					int:
     a					=self.num_labels
     a					=BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE_						)
     model.to(SCREAMING_SNAKE_CASE_						)
     model.eval()
     a					=model(SCREAMING_SNAKE_CASE_						)
     self.parent.assertEqual(
         result.logits.shape			,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
     a					=model(SCREAMING_SNAKE_CASE_			,   labels=SCREAMING_SNAKE_CASE_						)
     self.parent.assertEqual(
         result.logits.shape			,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=self.prepare_config_and_inputs()
     a					=config_and_inputs
     a					={"""pixel_values""": pixel_values}
     return config, inputs_dict






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






 """simple docstring"""




 __lowerCAmelCase			 =  (
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase			 =  (
     {
         "feature-extraction": BeitModel,
         "image-classification": BeitForImageClassification,
         "image-segmentation": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=BeitModelTester(self						)
     a					=ConfigTester(self			,   config_class=SCREAMING_SNAKE_CASE_			,   has_text_modality=SCREAMING_SNAKE_CASE_			,   hidden_size=37						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     self.config_tester.run_common_tests()


 @unittest.skip(reason='''BEiT does not use inputs_embeds'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     pass


 @require_torch_multi_gpu
 @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     pass


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

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


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

     for model_class in self.all_model_classes:
         a					=model_class(SCREAMING_SNAKE_CASE_						)
         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]			,   SCREAMING_SNAKE_CASE_						)


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


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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     if not self.model_tester.is_training:
         return

     a					=self.model_tester.prepare_config_and_inputs_for_common()
     a					=True

     for model_class in self.all_model_classes:
         # we don't test BeitForMaskedImageModeling
         if model_class in [*get_values(SCREAMING_SNAKE_CASE_						), BeitForMaskedImageModeling]:
             continue

         a					=model_class(SCREAMING_SNAKE_CASE_						)
         model.to(SCREAMING_SNAKE_CASE_						)
         model.train()
         a					=self._prepare_for_class(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_			,   return_labels=SCREAMING_SNAKE_CASE_						)
         a					=model(**SCREAMING_SNAKE_CASE_						).loss
         loss.backward()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     a					=False
     a					=True

     for model_class in self.all_model_classes:
         # we don't test BeitForMaskedImageModeling
         if (
             model_class in [*get_values(SCREAMING_SNAKE_CASE_						), BeitForMaskedImageModeling]
             or not model_class.supports_gradient_checkpointing
         ):
             continue

         a					=model_class(SCREAMING_SNAKE_CASE_						)
         model.gradient_checkpointing_enable()
         model.to(SCREAMING_SNAKE_CASE_						)
         model.train()
         a					=self._prepare_for_class(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_			,   return_labels=SCREAMING_SNAKE_CASE_						)
         a					=model(**SCREAMING_SNAKE_CASE_						).loss
         loss.backward()


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

     a					=_config_zero_init(SCREAMING_SNAKE_CASE_						)
     for model_class in self.all_model_classes:
         a					=model_class(config=SCREAMING_SNAKE_CASE_						)
         for name, param in model.named_parameters():
             # we skip lambda parameters as these require special initial values
             # determined by config.layer_scale_init_value
             if "lambda" in name:
                 continue
             if 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'''			,   )





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a					=BeitModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)

def _A						(						):







    """simple docstring"""
    a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image






@require_torch
@require_vision
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''						) if is_vision_available() else None


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''						).to(SCREAMING_SNAKE_CASE_						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''						).pixel_values.to(SCREAMING_SNAKE_CASE_						)

     # prepare bool_masked_pos
     a					=torch.ones((1, 196)			,   dtype=torch.bool						).to(SCREAMING_SNAKE_CASE_						)

     # forward pass
     with torch.no_grad():
         a					=model(pixel_values=SCREAMING_SNAKE_CASE_			,   bool_masked_pos=SCREAMING_SNAKE_CASE_						)
     a					=outputs.logits

     # verify the logits
     a					=torch.Size((1, 196, 8192)						)
     self.assertEqual(logits.shape			,   SCREAMING_SNAKE_CASE_						)

     a					=torch.tensor(
         [[-3.2_437, 0.5_072, -13.9174], [-3.2_456, 0.4_948, -13.9401], [-3.2_033, 0.5_121, -13.8550]]						).to(SCREAMING_SNAKE_CASE_						)

     self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]			,   SCREAMING_SNAKE_CASE_			,   atol=1E-2						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''						).to(SCREAMING_SNAKE_CASE_						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''						).to(SCREAMING_SNAKE_CASE_						)

     # forward pass
     with torch.no_grad():
         a					=model(**SCREAMING_SNAKE_CASE_						)
     a					=outputs.logits

     # verify the logits
     a					=torch.Size((1, 1000)						)
     self.assertEqual(logits.shape			,   SCREAMING_SNAKE_CASE_						)

     a					=torch.tensor([-1.2_385, -1.0_987, -1.0_108]						).to(SCREAMING_SNAKE_CASE_						)

     self.assertTrue(torch.allclose(logits[0, :3]			,   SCREAMING_SNAKE_CASE_			,   atol=1E-4						)						)

     a					=281
     self.assertEqual(logits.argmax(-1						).item()			,   SCREAMING_SNAKE_CASE_						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''						).to(
         SCREAMING_SNAKE_CASE_						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''						).to(SCREAMING_SNAKE_CASE_						)

     # forward pass
     with torch.no_grad():
         a					=model(**SCREAMING_SNAKE_CASE_						)
     a					=outputs.logits

     # verify the logits
     a					=torch.Size((1, 2_1841)						)
     self.assertEqual(logits.shape			,   SCREAMING_SNAKE_CASE_						)

     a					=torch.tensor([1.6_881, -0.2_787, 0.5_901]						).to(SCREAMING_SNAKE_CASE_						)

     self.assertTrue(torch.allclose(logits[0, :3]			,   SCREAMING_SNAKE_CASE_			,   atol=1E-4						)						)

     a					=2396
     self.assertEqual(logits.argmax(-1						).item()			,   SCREAMING_SNAKE_CASE_						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''						)
     a					=model.to(SCREAMING_SNAKE_CASE_						)

     a					=BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE_			,   size=640			,   do_center_crop=SCREAMING_SNAKE_CASE_						)

     a					=load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''						)
     a					=Image.open(ds[0]['''file''']						)
     a					=image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''						).to(SCREAMING_SNAKE_CASE_						)

     # forward pass
     with torch.no_grad():
         a					=model(**SCREAMING_SNAKE_CASE_						)
     a					=outputs.logits

     # verify the logits
     a					=torch.Size((1, 150, 160, 160)						)
     self.assertEqual(logits.shape			,   SCREAMING_SNAKE_CASE_						)

     a					=version.parse(PIL.__version__						) < version.parse('''9.0.0'''						)

     if is_pillow_less_than_a:
         a					=torch.tensor(
             [
                 [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                 [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                 [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
             ]			,   device=SCREAMING_SNAKE_CASE_			,   )
     else:
         a					=torch.tensor(
             [
                 [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
                 [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
                 [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
             ]			,   device=SCREAMING_SNAKE_CASE_			,   )

     self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,   SCREAMING_SNAKE_CASE_			,   atol=1E-4						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''						)
     a					=model.to(SCREAMING_SNAKE_CASE_						)

     a					=BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE_			,   size=640			,   do_center_crop=SCREAMING_SNAKE_CASE_						)

     a					=load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''						)
     a					=Image.open(ds[0]['''file''']						)
     a					=image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''						).to(SCREAMING_SNAKE_CASE_						)

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

     a					=outputs.logits.detach().cpu()

     a					=image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_			,   target_sizes=[(500, 300)]						)
     a					=torch.Size((500, 300)						)
     self.assertEqual(segmentation[0].shape			,   SCREAMING_SNAKE_CASE_						)

     a					=image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_						)
     a					=torch.Size((160, 160)						)
     self.assertEqual(segmentation[0].shape			,   SCREAMING_SNAKE_CASE_						)



"""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")