


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

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


    def        A   (  self      :							Any):
           _A					:     Dict     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

           return config, pixel_values, labels


    def        A   (  self      :							Optional[int]):
           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=SCREAMING_SNAKE_CASE				,							initializer_range=self.initializer_range				,							encoder_stride=self.encoder_stride				,							)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Any):
           _A					:     Optional[int]     = TFDeiTModel(config=SCREAMING_SNAKE_CASE)
           _A					:     Any     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size))


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     int     = TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE)
           _A					:     Any     = 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
           _A					:     List[Any]     = 1
           _A					:     Optional[Any]     = TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE)

           _A					:     int     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _A					:     Optional[int]     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.reconstruction.shape				,							(self.batch_size, 1, self.image_size, self.image_size))


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = self.type_sequence_label_size
           _A					:     int     = TFDeiTForImageClassification(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = 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					:     List[Any]     = 1
           _A					:     List[str]     = TFDeiTForImageClassification(SCREAMING_SNAKE_CASE)

           _A					:     int     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _A					:     Dict     = model(SCREAMING_SNAKE_CASE				,							labels=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size))


    def        A   (  self      :							Any):
           _A					:     Dict     = self.prepare_config_and_inputs()
           _A					:     List[str]     = config_and_inputs
           _A					:     List[Any]     = {'pixel_values': pixel_values}
           return config, inputs_dict




@require_tf
class 			__lowerCamelCase			(	a_    , a_    , 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        A   (  self      :							Optional[int]):
           _A					:     Any     = TFDeiTModelTester(self)
           _A					:     Optional[int]     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE				,							has_text_modality=SCREAMING_SNAKE_CASE				,							hidden_size=37)


    def        A   (  self      :							Optional[Any]):
           self.config_tester.run_common_tests()


    @unittest.skip(reason='DeiT does not use inputs_embeds')
    def        A   (  self      :							List[Any]):
           pass


    def        A   (  self      :							Union[str, Any]):
           _A					:     Union[str, Any]     = self.model_tester.prepare_config_and_inputs_for_common()

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


    def        A   (  self      :							Dict):
           _A					:     Optional[Any]     = self.model_tester.prepare_config_and_inputs_for_common()

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

                  _A					:     List[Any]     = ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           _A					:     int     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


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

           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        A   (  self      :							List[str]):
           for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A					:     Tuple     = TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    ):
       _A					:     str     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image


@require_tf
@require_vision
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @cached_property
    def        A   (  self      :							Tuple):
           return (
               DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
               if is_vision_available()
               else None
           )


    @slow
    def        A   (  self      :							List[Any]):
           _A					:     Any     = TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')

           _A					:     Any     = self.default_image_processor
           _A					:     List[Any]     = prepare_img()
           _A					:     Optional[int]     = image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='tf')

           # forward pass
           _A					:     Dict     = model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A					:     Union[str, Any]     = tf.TensorShape((1, 1000))
           self.assertEqual(outputs.logits.shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = tf.constant([-1.0266, 0.1912, -1.2861])

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










'''simple docstring'''







from __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple=None):
           _A					:     Any     = data
           _A					:     Optional[Any]     = None


    def __repr__(  self      :							List[str]):
           _A					:     List[Any]     = []
           _A					:     Any     = self
           while temp:
                  string_rep.append(F'{temp.data}')
                  _A					:     List[Any]     = temp.next
           return "->".join(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    lowerCamelCase					:      list       ):
       if not elements_list:
              raise Exception('The Elements List is empty'       )

       _A					:     Union[str, Any]     = Node(elements_list[0]       )
       for i in range(1  ,len(lowerCamelCase       )       ):
              _A					:     Dict     = Node(elements_list[i]       )
              _A					:     int     = current.next
       return head






def 				lowerCAmelCase__	(    lowerCamelCase					:      Node       ):
       if head_node is not None and isinstance(lowerCamelCase  ,lowerCamelCase       ):
              print_reverse(head_node.next       )
              print(head_node.data       )






def 				lowerCAmelCase__	(    ):
       from doctest import testmod

       testmod()

       _A					:     List[str]     = make_linked_list([14, 52, 14, 12, 43]       )
       print('Linked List:'       )
       print(lowerCamelCase       )
       print('Elements in Reverse:'       )
       print_reverse(lowerCamelCase       )


if __name__ == "__main__":
   main()






