




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
         TFDeiTModel,
     )
     from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor




class    __lowerCAmelCase       :

       '''simple docstring'''





       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					List[Any]       ,					_lowerCAmelCase:					List[Any]=13       ,					_lowerCAmelCase:					List[Any]=30       ,					_lowerCAmelCase:					Union[str, Any]=2       ,					_lowerCAmelCase:					List[Any]=3       ,					_lowerCAmelCase:					List[Any]=True       ,					_lowerCAmelCase:					Any=True       ,					_lowerCAmelCase:					Dict=32       ,					_lowerCAmelCase:					Optional[Any]=2       ,					_lowerCAmelCase:					Optional[Any]=4       ,					_lowerCAmelCase:					List[str]=37       ,					_lowerCAmelCase:					Union[str, Any]="gelu"       ,					_lowerCAmelCase:					List[Any]=0.1       ,					_lowerCAmelCase:					Union[str, Any]=0.1       ,					_lowerCAmelCase:					Optional[int]=10       ,					_lowerCAmelCase:					int=0.02       ,					_lowerCAmelCase:					List[str]=3       ,					_lowerCAmelCase:					Tuple=None       ,					_lowerCAmelCase:					Any=2       ,					):
         lowercase    :List[Any]      	=						parent
         lowercase    :Optional[Any]      	=						batch_size
         lowercase    :Union[str, Any]      	=						image_size
         lowercase    :int      	=						patch_size
         lowercase    :int      	=						num_channels
         lowercase    :Any      	=						is_training
         lowercase    :List[Any]      	=						use_labels
         lowercase    :str      	=						hidden_size
         lowercase    :Tuple      	=						num_hidden_layers
         lowercase    :Union[str, Any]      	=						num_attention_heads
         lowercase    :str      	=						intermediate_size
         lowercase    :Optional[int]      	=						hidden_act
         lowercase    :Union[str, Any]      	=						hidden_dropout_prob
         lowercase    :Dict      	=						attention_probs_dropout_prob
         lowercase    :int      	=						type_sequence_label_size
         lowercase    :List[Any]      	=						initializer_range
         lowercase    :List[str]      	=						scope
         lowercase    :List[str]      	=						encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         lowercase    :Union[str, Any]      	=						(image_size // patch_size) ** 2
         lowercase    :int      	=						num_patches + 2






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Optional[Any]      	=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

         lowercase    :List[str]      	=						self.get_config()

         return config, pixel_values, labels






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return DeiTConfig(
             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=_A       ,					initializer_range=self.initializer_range       ,					encoder_stride=self.encoder_stride       ,					)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Dict       ,					_lowerCAmelCase:					Dict				):
         lowercase    :Dict      	=						TFDeiTModel(config=_A				)
         lowercase    :Any      	=						model(_A				)
         self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					int				):
         lowercase    :Any      	=						TFDeiTForMaskedImageModeling(config=_A				)
         lowercase    :List[str]      	=						model(_A				)
         self.parent.assertEqual(
             result.reconstruction.shape       ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

         # test greyscale images
         lowercase    :Tuple      	=						1
         lowercase    :Any      	=						TFDeiTForMaskedImageModeling(_A				)

         lowercase    :Optional[Any]      	=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         lowercase    :Union[str, Any]      	=						model(_A				)
         self.parent.assertEqual(result.reconstruction.shape       ,					(self.batch_size, 1, self.image_size, self.image_size)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					Optional[Any]       ,					_lowerCAmelCase:					Any       ,					_lowerCAmelCase:					Tuple				):
         lowercase    :Tuple      	=						self.type_sequence_label_size
         lowercase    :Any      	=						TFDeiTForImageClassification(_A				)
         lowercase    :int      	=						model(_A       ,					labels=_A				)
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size)				)

         # test greyscale images
         lowercase    :Optional[Any]      	=						1
         lowercase    :Dict      	=						TFDeiTForImageClassification(_A				)

         lowercase    :str      	=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         lowercase    :List[Any]      	=						model(_A       ,					labels=_A				)
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Optional[int]      	=						self.prepare_config_and_inputs()
         lowercase	,    lowercase	,    lowercase    :int      	=						config_and_inputs
         lowercase    :List[str]      	=						{"pixel_values": pixel_values}
         return config, inputs_dict







@require_tf
class    __lowerCAmelCase       (							__lowercase      , __lowercase      , unittest.TestCase):

       '''simple docstring'''





       _a 					=						(
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       _a 					=						(
           {
               '''feature-extraction''': TFDeiTModel,
               '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       _a 					=						False
       _a 					=						False
       _a 					=						False
       _a 					=						False






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Optional[Any]      	=						TFDeiTModelTester(self				)
         lowercase    :Union[str, Any]      	=						ConfigTester(self       ,					config_class=_A       ,					has_text_modality=_A       ,					hidden_size=37				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         self.config_tester.run_common_tests()






       @unittest.skip(reason="DeiT does not use inputs_embeds"				)
       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase	,    lowercase    :Tuple      	=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowercase    :Any      	=						model_class(_A				)
           self.assertIsInstance(model.get_input_embeddings()       ,					(tf.keras.layers.Layer)				)
           lowercase    :Union[str, Any]      	=						model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_A       ,					tf.keras.layers.Dense				)				)






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

         for model_class in self.all_model_classes:
           lowercase    :Optional[Any]      	=						model_class(_A				)
           lowercase    :Optional[int]      	=						inspect.signature(model.call				)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowercase    :int      	=						[*signature.parameters.keys()]

           lowercase    :Union[str, Any]      	=						["pixel_values"]
           self.assertListEqual(arg_names[:1]       ,					_A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         lowercase    :List[str]      	=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[Any]      	=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Union[str, Any]      	=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					Union[str, Any]       ,					_lowerCAmelCase:					List[Any]       ,					_lowerCAmelCase:					Any=False				):
         lowercase    :int      	=						super()._prepare_for_class(_A       ,					_A       ,					return_labels=_A				)

         if return_labels:
           if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call				).parameters:
             del inputs_dict["labels"]

         return inputs_dict






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase    :Optional[Any]      	=						TFDeiTModel.from_pretrained(_A				)
           self.assertIsNotNone(_A				)







def       UpperCAmelCase__  (     ):
  lowercase    :Dict      	=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
  return image







@require_tf
@require_vision
class    __lowerCAmelCase       (							unittest.TestCase):

       '''simple docstring'''





       @cached_property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         return (
             DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
             if is_vision_available()
             else None
         )






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :int      	=						TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				)

         lowercase    :Any      	=						self.default_image_processor
         lowercase    :Optional[int]      	=						prepare_img()
         lowercase    :Tuple      	=						image_processor(images=_A       ,					return_tensors="tf"				)

         # forward pass
         lowercase    :Optional[int]      	=						model(**_A				)

         # verify the logits
         lowercase    :List[str]      	=						tf.TensorShape((1, 10_00)				)
         self.assertEqual(outputs.logits.shape       ,					_A				)

         lowercase    :Any      	=						tf.constant([-1.02_66, 0.19_12, -1.28_61]				)

         self.assertTrue(np.allclose(outputs.logits[0, :3]       ,					_A       ,					atol=1e-4				)				)









import string



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Optional[int]      	=						""
  for i in sequence:
    lowercase    :Optional[Any]      	=						ord(lowerCamelCase   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Union[str, Any]      	=						string.ascii_letters
  lowercase    :Dict      	=						string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(lowerCamelCase   )] if c in letters else c for c in sequence   )



def       UpperCAmelCase__  (     ):
  from timeit import timeit

  print("Running performance benchmarks..."   )
  lowercase    :Dict      	=						"from string import printable ; from __main__ import atbash, atbash_slow"
  print(F"> atbash_slow(): {timeit('atbash_slow(printable)',			setup=lowerCamelCase   )} seconds"   )
  print(F">      atbash(): {timeit('atbash(printable)',			setup=lowerCamelCase   )} seconds"   )


if __name__ == "__main__":
     for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
          print(f'''{example} encrypted in atbash: {atbash(example)}''')
     benchmark()



