



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
      from PIL import Image

      from transformers import ConvNextImageProcessor







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Any=13  ,lowerCamelCase_:     int=32  ,lowerCamelCase_:     List[Any]=3  ,lowerCamelCase_:     int=4  ,lowerCamelCase_:     str=[10, 20, 30, 40]  ,lowerCamelCase_:     Dict=[2, 2, 3, 2]  ,lowerCamelCase_:     int=True  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Optional[Any]=37  ,lowerCamelCase_:     List[Any]="gelu"  ,lowerCamelCase_:     Any=10  ,lowerCamelCase_:     Optional[int]=0.0_2  ,lowerCamelCase_:     int=3  ,lowerCamelCase_:     int=None  ,)	->      List[Any]:
       UpperCAmelCase_     :       Tuple  		=					parent
       UpperCAmelCase_     :       Tuple  		=					batch_size
       UpperCAmelCase_     :       Optional[int]  		=					image_size
       UpperCAmelCase_     :       str  		=					num_channels
       UpperCAmelCase_     :       List[str]  		=					num_stages
       UpperCAmelCase_     :       Dict  		=					hidden_sizes
       UpperCAmelCase_     :       Union[str, Any]  		=					depths
       UpperCAmelCase_     :       Dict  		=					is_training
       UpperCAmelCase_     :       Optional[int]  		=					use_labels
       UpperCAmelCase_     :       str  		=					intermediate_size
       UpperCAmelCase_     :       Any  		=					hidden_act
       UpperCAmelCase_     :       Optional[Any]  		=					type_sequence_label_size
       UpperCAmelCase_     :       Dict  		=					initializer_range
       UpperCAmelCase_     :       List[str]  		=					scope


   def       A__    (    self:     List[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[int]  		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCAmelCase_     :       Union[str, Any]  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       int  		=					ids_tensor([self.batch_size]  ,self.type_sequence_label_size							)

       UpperCAmelCase_     :       Union[str, Any]  		=					self.get_config()

       return config, pixel_values, labels


   def       A__    (    self:     Dict							)	->      Optional[Any]:
       return ConvNextConfig(
           num_channels=self.num_channels  ,hidden_sizes=self.hidden_sizes  ,depths=self.depths  ,num_stages=self.num_stages  ,hidden_act=self.hidden_act  ,is_decoder=lowerCamelCase_  ,initializer_range=self.initializer_range  ,)


   def       A__    (    self:     int  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     str							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					TFConvNextModel(config=lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					model(lowerCamelCase_  ,training=lowerCamelCase_							)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,)


   def       A__    (    self:     Dict  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     Optional[Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       int  		=					self.type_sequence_label_size
       UpperCAmelCase_     :       List[str]  		=					TFConvNextForImageClassification(lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					model(lowerCamelCase_  ,labels=lowerCamelCase_  ,training=lowerCamelCase_							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)							)






   def       A__    (    self:     int							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					config_and_inputs
       UpperCAmelCase_     :       List[str]  		=					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_tf
class       _snake_case				(     __snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : List[Any]       =						(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
   A__      : Optional[Any]       =						(
       {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
       if is_tf_available()
       else {}
   )

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


   def       A__    (    self:     List[Any]							)	->      Tuple:
       UpperCAmelCase_     :       Optional[int]  		=					TFConvNextModelTester(self							)
       UpperCAmelCase_     :       Any  		=					ConfigTester(
           self  ,config_class=lowerCamelCase_  ,has_text_modality=lowerCamelCase_  ,hidden_size=37  ,)


   @unittest.skip(reason="""ConvNext does not use inputs_embeds"""							)
   def       A__    (    self:     Tuple							)	->      str:
       pass


   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""							)							) == 0  ,reason="""TF does not support backprop for grouped convolutions on CPU."""  ,)
   @slow
   def       A__    (    self:     Any							)	->      Any:
       super().test_keras_fit()


   @unittest.skip(reason="""ConvNext does not support input and output embeddings"""							)
   def       A__    (    self:     Tuple							)	->      Union[str, Any]:
       pass


   def       A__    (    self:     Optional[Any]							)	->      List[str]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Tuple  		=					model_class(lowerCamelCase_							)
           UpperCAmelCase_     :       Tuple  		=					inspect.signature(model.call							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCAmelCase_     :       Dict  		=					[*signature.parameters.keys()]

           UpperCAmelCase_     :       Optional[int]  		=					["""pixel_values"""]
           self.assertListEqual(arg_names[:1]  ,lowerCamelCase_							)


   def       A__    (    self:     Optional[Any]							)	->      int:
       UpperCAmelCase_     :       Optional[int]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_							)


   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""							)							) == 0  ,reason="""TF does not support backprop for grouped convolutions on CPU."""  ,)
   def       A__    (    self:     Optional[int]							)	->      List[str]:
       super().test_dataset_conversion()


   def       A__    (    self:     List[str]							)	->      Optional[int]:
       def check_hidden_states_output(lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Union[str, Any]							):
           UpperCAmelCase_     :       Optional[Any]  		=					model_class(lowerCamelCase_							)

           UpperCAmelCase_     :       List[str]  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)
           UpperCAmelCase_     :       str  		=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCAmelCase_     :       List[Any]  		=					self.model_tester.num_stages
           self.assertEqual(len(lowerCamelCase_							)  ,expected_num_stages + 1							)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)  ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,)

       UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       int  		=					True
           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCAmelCase_     :       str  		=					True

           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)


   def       A__    (    self:     Union[str, Any]							)	->      Tuple:
       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					self.model_tester.prepare_config_and_inputs_for_common()

       def check_equivalence(lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Dict={}							):
           UpperCAmelCase_     :       List[Any]  		=					model(lowerCamelCase_  ,return_dict=lowerCamelCase_  ,**lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					model(lowerCamelCase_  ,return_dict=lowerCamelCase_  ,**lowerCamelCase_							).to_tuple()

           def recursive_check(lowerCamelCase_:     int  ,lowerCamelCase_:     List[str]							):
               if isinstance(lowerCamelCase_  ,(List, Tuple)							):
                   for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase_  ,lowerCamelCase_							):
                       recursive_check(lowerCamelCase_  ,lowerCamelCase_							)
               elif tuple_object is None:
                   return
               else:
                   self.assertTrue(
                       all(tf.equal(lowerCamelCase_  ,lowerCamelCase_							)							)  ,msg=(
                           """Tuple and dict output are not equal. Difference:"""
                           F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}'''
                       )  ,)

               recursive_check(lowerCamelCase_  ,lowerCamelCase_							)

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Tuple  		=					model_class(lowerCamelCase_							)

           UpperCAmelCase_     :       List[str]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)
           UpperCAmelCase_     :       Union[str, Any]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)
           check_equivalence(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

           UpperCAmelCase_     :       str  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           UpperCAmelCase_     :       Dict  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           check_equivalence(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

           UpperCAmelCase_     :       Optional[int]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)
           UpperCAmelCase_     :       Dict  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)
           check_equivalence(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,{"""output_hidden_states""": True}							)

           UpperCAmelCase_     :       int  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           check_equivalence(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,{"""output_hidden_states""": True}							)


   def       A__    (    self:     str							)	->      str:
       UpperCAmelCase_     :       Any  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_							)






   @slow
   def       A__    (    self:     Any							)	->      List[str]:
       UpperCAmelCase_     :       Optional[int]  		=					TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""							)
       self.assertIsNotNone(lowerCamelCase_							)







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    return image







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






   '''simple docstring'''






   @cached_property
   def       A__    (    self:     Optional[int]							)	->      List[Any]:
       return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""							) if is_vision_available() else None






   @slow
   def       A__    (    self:     Union[str, Any]							)	->      Any:
       UpperCAmelCase_     :       Tuple  		=					TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""							)

       UpperCAmelCase_     :       Tuple  		=					self.default_image_processor
       UpperCAmelCase_     :       Optional[Any]  		=					prepare_img()
       UpperCAmelCase_     :       str  		=					image_processor(images=lowerCamelCase_  ,return_tensors="""tf"""							)

       # forward pass
       UpperCAmelCase_     :       Tuple  		=					model(**lowerCamelCase_							)

       # verify the logits
       UpperCAmelCase_     :       Any  		=					tf.TensorShape((1, 1000)							)
       self.assertEqual(outputs.logits.shape  ,lowerCamelCase_							)

       UpperCAmelCase_     :       List[Any]  		=					tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]							)

       tf.debugging.assert_near(outputs.logits[0, :3]  ,lowerCamelCase_  ,atol=1e-4							)





import itertools
from pathlib import Path







def       lowerCamelCase_  (    _a       :						list[str]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					[tuple(_a    ) for login in logins]

    UpperCAmelCase_     :       str  		=					{char for login in split_logins for char in login}

    for permutation in itertools.permutations(_a    ):
        UpperCAmelCase_     :       str  		=					True
        for login in logins:
            if not (
                permutation.index(login[0]    )
                < permutation.index(login[1]    )
                < permutation.index(login[2]    )
            ):
                UpperCAmelCase_     :       Optional[int]  		=					False
                break

        if satisfied:
            return int("""""".join(_a    )    )

    raise Exception("""Unable to find the secret passcode"""    )







def       lowerCamelCase_  (    _a       :						str = "keylog.txt"    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Path(_a    ).parent.joinpath(_a    ).read_text().splitlines()

    return find_secret_passcode(_a    )


if __name__ == "__main__":
      print(F"{solution() = }")
