




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 		UpperCamelCase__	:

    '''simple docstring'''



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

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







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[Any]			 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

           lowerCamelCase   :      Optional[int]			 =					None
           if self.use_labels:
                  lowerCamelCase   :      Optional[int]			 =					ids_tensor([self.batch_size]     ,  self.type_sequence_label_size     )

           lowerCamelCase   :      Tuple			 =					self.get_config()

           return config, pixel_values, labels







    def     _lowercase     (    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=UpperCamelCase__     ,  initializer_range=self.initializer_range     ,  encoder_stride=self.encoder_stride     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      List[str]			 =					TFDeiTModel(config=UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					model(UpperCamelCase__     )
           self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					TFDeiTForMaskedImageModeling(config=UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					model(UpperCamelCase__     )
           self.parent.assertEqual(
               result.reconstruction.shape     ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

           # test greyscale images
           lowerCamelCase   :      str			 =					1
           lowerCamelCase   :      Optional[int]			 =					TFDeiTForMaskedImageModeling(UpperCamelCase__     )

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Optional[Any]			 =					self.type_sequence_label_size
           lowerCamelCase   :      Optional[int]			 =					TFDeiTForImageClassification(UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					model(UpperCamelCase__     ,  labels=UpperCamelCase__     )
           self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)     )

           # test greyscale images
           lowerCamelCase   :      List[str]			 =					1
           lowerCamelCase   :      str			 =					TFDeiTForImageClassification(UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
           lowerCamelCase   :      Optional[int]			 =					model(UpperCamelCase__     ,  labels=UpperCamelCase__     )
           self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)     )







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      int			 =					self.prepare_config_and_inputs()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					config_and_inputs
           lowerCamelCase   :      Dict			 =					{"pixel_values": pixel_values}
           return config, inputs_dict




@require_tf
class 		UpperCamelCase__	(lowerCAmelCase__    ,				lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[Any]	     =      (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase_      :	Optional[Any]	     =      (
        {
            """feature-extraction""": TFDeiTModel,
            """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    lowerCamelCase_      :	Dict	     =      False
    lowerCamelCase_      :	Tuple	     =      False
    lowerCamelCase_      :	str	     =      False
    lowerCamelCase_      :	List[Any]	     =      False







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Optional[int]			 =					TFDeiTModelTester(self     )
           lowerCamelCase   :      Any			 =					ConfigTester(self     ,  config_class=UpperCamelCase__     ,  has_text_modality=UpperCamelCase__     ,  hidden_size=37     )







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







    @unittest.skip(reason="DeiT does not use inputs_embeds"     )
    def     _lowercase     (    self     )     ->      int:
           pass







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

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







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      str			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCamelCase   :      str			 =					["pixel_values"]
                  self.assertListEqual(arg_names[:1]     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      int			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Optional[int]			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Union[str, Any]			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=False     )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					super()._prepare_for_class(UpperCamelCase__     ,  UpperCamelCase__     ,  return_labels=UpperCamelCase__     )

           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     _lowercase     (    self     )     ->      Optional[Any]:
           for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCamelCase   :      Any			 =					TFDeiTModel.from_pretrained(UpperCamelCase__     )
                  self.assertIsNotNone(UpperCamelCase__     )




def      A      (      )	-> List[str]:
       lowerCamelCase   :      str			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image




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

    '''simple docstring'''



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







    @slow
    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      int			 =					TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"     )

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

           # forward pass
           lowerCamelCase   :      Optional[int]			 =					model(**UpperCamelCase__     )

           # verify the logits
           lowerCamelCase   :      str			 =					tf.TensorShape((1, 1000)     )
           self.assertEqual(outputs.logits.shape     ,  UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					tf.constant([-1.0266, 0.1912, -1.2861]     )

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



