






'''simple docstring'''
import pickle

import numpy as np
from matplotlib import pyplot as plt


class 							_UpperCAmelCase    :




    def __init__(      self	:							Tuple	,  a	:							Any	,  a	:							int	,  a	:							List[str]	,  a	:							List[str]	,  a	:							int	,  a	:							Union[str, Any]=0.2	,  a	:							Union[str, Any]=0.2      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  bp_numa
       lowercase_						:     Optional[Any]							     =  bp_numa
       lowercase_						:     Optional[int]							     =  bp_numa
       lowercase_						:     Dict							     =  conva_get[:2]
       lowercase_						:     List[Any]							     =  conva_get[2]
       lowercase_						:     Tuple							     =  size_pa
       lowercase_						:     List[str]							     =  rate_w
       lowercase_						:     List[Any]							     =  rate_t
       lowercase_						:     Union[str, Any]							     =  [
           np.mat(-1 * np.random.rand(self.conva[0]	,  self.conva[0]      ) + 0.5      )
           for i in range(self.conva[1]      )
       ]
       lowercase_						:     Union[str, Any]							     =  np.mat(-1 * np.random.rand(self.num_bpa	,  self.num_bpa      ) + 0.5      )
       lowercase_						:     Optional[Any]							     =  np.mat(-1 * np.random.rand(self.num_bpa	,  self.num_bpa      ) + 0.5      )
       lowercase_						:     int							     =  -2 * np.random.rand(self.conva[1]      ) + 1
       lowercase_						:     Optional[Any]							     =  -2 * np.random.rand(self.num_bpa      ) + 1
       lowercase_						:     List[Any]							     =  -2 * np.random.rand(self.num_bpa      ) + 1




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  {
           "num_bp1": self.num_bpa,
           "num_bp2": self.num_bpa,
           "num_bp3": self.num_bpa,
           "conv1": self.conva,
           "step_conv1": self.step_conva,
           "size_pooling1": self.size_poolinga,
           "rate_weight": self.rate_weight,
           "rate_thre": self.rate_thre,
           "w_conv1": self.w_conva,
           "wkj": self.wkj,
           "vji": self.vji,
           "thre_conv1": self.thre_conva,
           "thre_bp2": self.thre_bpa,
           "thre_bp3": self.thre_bpa,
       }
       with open(a	,  "wb"      ) as f:
          pickle.dump(a	,  a      )

       print(f"""Model saved： {save_path}"""      )




    @classmethod
    def 					lowerCAmelCase__					(      cls	:							Optional[Any]	,  a	:							List[str]      ):






       '''simple docstring'''






       with open(a	,  "rb"      ) as f:
          lowercase_						:     str							     =  pickle.load(a      )  # noqa: S301

       lowercase_						:     str							     =  model_dic.get("conv1"      )
       conv_get.append(model_dic.get("step_conv1"      )      )
       lowercase_						:     List[str]							     =  model_dic.get("size_pooling1"      )
       lowercase_						:     Tuple							     =  model_dic.get("num_bp1"      )
       lowercase_						:     List[str]							     =  model_dic.get("num_bp2"      )
       lowercase_						:     Optional[Any]							     =  model_dic.get("num_bp3"      )
       lowercase_						:     Optional[int]							     =  model_dic.get("rate_weight"      )
       lowercase_						:     Optional[int]							     =  model_dic.get("rate_thre"      )
       # create model instance
       lowercase_						:     Optional[int]							     =  CNN(a	,  a	,  a	,  a	,  a	,  a	,  a      )
       # modify model parameter
       lowercase_						:     Optional[int]							     =  model_dic.get("w_conv1"      )
       lowercase_						:     List[Any]							     =  model_dic.get("wkj"      )
       lowercase_						:     Tuple							     =  model_dic.get("vji"      )
       lowercase_						:     Optional[int]							     =  model_dic.get("thre_conv1"      )
       lowercase_						:     Optional[int]							     =  model_dic.get("thre_bp2"      )
       lowercase_						:     Tuple							     =  model_dic.get("thre_bp3"      )
       return conv_ins




    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							Optional[Any]      ):






       '''simple docstring'''






       return 1 / (1 + np.exp(-1 * x      ))




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       return round(a	,  3      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							Tuple	,  a	:							Optional[int]	,  a	:							Optional[int]	,  a	:							Dict	,  a	:							str      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  convs[0]
       lowercase_						:     int							     =  convs[1]
       lowercase_						:     Union[str, Any]							     =  np.shape(a      )[0]
       # get the data slice of original image data, data_focus
       lowercase_						:     Optional[int]							     =  []
       for i_focus in range(0	,  size_data - size_conv + 1	,  a      ):
          for j_focus in range(0	,  size_data - size_conv + 1	,  a      ):
             lowercase_						:     Union[str, Any]							     =  data[
                 i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
             ]
             data_focus.append(a      )
        # calculate the feature map of every single kernel, and saved as list of matrix
       lowercase_						:     Any							     =  []
       lowercase_						:     Union[str, Any]							     =  int((size_data - size_conv) / conv_step + 1      )
       for i_map in range(a      ):
          lowercase_						:     int							     =  []
          for i_focus in range(len(a      )      ):
             lowercase_						:     List[str]							     =  (
                 np.sum(np.multiply(data_focus[i_focus]	,  w_convs[i_map]      )      )
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(a      )      )
          lowercase_						:     List[str]							     =  np.asmatrix(a      ).reshape(
              a	,  a      )
          data_featuremap.append(a      )

       # expanding the data slice to One dimenssion
       lowercase_						:     Tuple							     =  []
       for each_focus in data_focus:
          focusa_list.extend(self.Expand_Mat(a      )      )
       lowercase_						:     Optional[Any]							     =  np.asarray(a      )
       return focus_list, data_featuremap




    def 					lowerCAmelCase__					(      self	:							List[Any]	,  a	:							Optional[Any]	,  a	:							List[str]	,  a	:							Union[str, Any]="average_pool"      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  len(featuremaps[0]      )
       lowercase_						:     Optional[Any]							     =  int(size_map / size_pooling      )
       lowercase_						:     str							     =  []
       for i_map in range(len(a      )      ):
          lowercase_						:     Any							     =  featuremaps[i_map]
          lowercase_						:     List[str]							     =  []
          for i_focus in range(0	,  a	,  a      ):
             for j_focus in range(0	,  a	,  a      ):
                lowercase_						:     Optional[Any]							     =  feature_map[
                    i_focus : i_focus + size_pooling,
                    j_focus : j_focus + size_pooling,
                ]
                if pooling_type == "average_pool":
                   # average pooling
                   map_pooled.append(np.average(a      )      )
                elif pooling_type == "max_pooling":
                   # max pooling
                   map_pooled.append(np.max(a      )      )
          lowercase_						:     int							     =  np.asmatrix(a      ).reshape(a	,  a      )
          featuremap_pooled.append(a      )
       return featuremap_pooled




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  []
       for i in range(len(a      )      ):
          lowercase_						:     str							     =  np.shape(data[i]      )
          lowercase_						:     Optional[int]							     =  data[i].reshape(1	,  shapes[0] * shapes[1]      )
          lowercase_						:     Optional[int]							     =  data_listed.getA().tolist()[0]
          data_expanded.extend(a      )
       lowercase_						:     Optional[Any]							     =  np.asarray(a      )
       return data_expanded




    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  np.asarray(a      )
       lowercase_						:     Optional[Any]							     =  np.shape(a      )
       lowercase_						:     Tuple							     =  data_mat.reshape(1	,  shapes[0] * shapes[1]      )
       return data_expanded




    def 					lowerCAmelCase__					(      self	:							Optional[Any]	,  a	:							Any	,  a	:							Optional[int]	,  a	:							str	,  a	:							Union[str, Any]	,  a	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  []
       lowercase_						:     Tuple							     =  0
       for i_map in range(a      ):
          lowercase_						:     Optional[int]							     =  np.ones((size_map, size_map)      )
          for i in range(0	,  a	,  a      ):
             for j in range(0	,  a	,  a      ):
                lowercase_						:     List[Any]							     =  pd_pool[
                    i_pool
                ]
                lowercase_						:     Optional[int]							     =  i_pool + 1
          lowercase_						:     Optional[Any]							     =  np.multiply(
              a	,  np.multiply(out_map[i_map]	,  (1 - out_map[i_map])      )      )
          pd_all.append(a      )
       return pd_all




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[int]	,  a	:							Optional[Any]	,  a	:							Optional[int]	,  a	:							Any	,  a	:							Dict	,  a	:							Tuple=bool      ):






       '''simple docstring'''






       print("----------------------Start Training-------------------------"      )
       print((" - - Shape: Train_Data  ", np.shape(a      ))      )
       print((" - - Shape: Teach_Data  ", np.shape(a      ))      )
       lowercase_						:     str							     =  0
       lowercase_						:     List[Any]							     =  []
       lowercase_						:     List[Any]							     =  1_0_0_0_0
       while rp < n_repeat and mse >= error_accuracy:
          lowercase_						:     str							     =  0
          print(f"""-------------Learning Time {rp}--------------"""      )
          for p in range(len(a      )      ):
             # print('------------Learning Image: %d--------------'%p)
             lowercase_						:     List[str]							     =  np.asmatrix(datas_train[p]      )
             lowercase_						:     Tuple							     =  np.asarray(datas_teach[p]      )
             lowercase_     ,		lowercase_						:     List[Any]							     =  self.convolute(
                 a	,  self.conva	,  self.w_conva	,  self.thre_conva	,  conv_step=self.step_conva	,  )
             lowercase_						:     Optional[int]							     =  self.pooling(a	,  self.size_poolinga      )
             lowercase_						:     Optional[int]							     =  np.shape(a      )
             lowercase_						:     Optional[Any]							     =  self._expand(a      )
             lowercase_						:     Tuple							     =  data_bp_input

             lowercase_						:     List[Any]							     =  np.dot(a	,  self.vji.T      ) - self.thre_bpa
             lowercase_						:     str							     =  self.sig(a      )
             lowercase_						:     List[str]							     =  np.dot(a	,  self.wkj.T      ) - self.thre_bpa
             lowercase_						:     Tuple							     =  self.sig(a      )

             # --------------Model Leaning ------------------------
             # calculate error and gradient---------------
             lowercase_						:     Optional[Any]							     =  np.multiply(
                 (data_teach - bp_outa)	,  np.multiply(a	,  (1 - bp_outa)      )      )
             lowercase_						:     Any							     =  np.multiply(
                 np.dot(a	,  self.wkj      )	,  np.multiply(a	,  (1 - bp_outa)      )      )
             lowercase_						:     str							     =  np.dot(a	,  self.vji      )

             lowercase_						:     List[Any]							     =  pd_i_all / (self.size_poolinga * self.size_poolinga)
             lowercase_						:     List[Any]							     =  pd_conva_pooled.T.getA().tolist()
             lowercase_						:     int							     =  self._calculate_gradient_from_pool(
                 a	,  a	,  shape_featuremapa[0]	,  shape_featuremapa[1]	,  self.size_poolinga	,  )
             # weight and threshold learning process---------
             # convolution layer
             for k_conv in range(self.conva[1]      ):
                lowercase_						:     Tuple							     =  self._expand_mat(pd_conva_all[k_conv]      )
                lowercase_						:     List[Any]							     =  self.rate_weight * np.dot(a	,  a      )

                lowercase_						:     Any							     =  self.w_conva[k_conv] + delta_w.reshape(
                    (self.conva[0], self.conva[0])      )

                lowercase_						:     Dict							     =  (
                    self.thre_conva[k_conv]
                    - np.sum(pd_conva_all[k_conv]      ) * self.rate_thre
                )
             # all connected layer
             lowercase_						:     Optional[Any]							     =  self.wkj + pd_k_all.T * bp_outa * self.rate_weight
             lowercase_						:     List[str]							     =  self.vji + pd_j_all.T * bp_outa * self.rate_weight
             lowercase_						:     Optional[int]							     =  self.thre_bpa - pd_k_all * self.rate_thre
             lowercase_						:     str							     =  self.thre_bpa - pd_j_all * self.rate_thre
             # calculate the sum error of all single image
             lowercase_						:     List[str]							     =  np.sum(abs(data_teach - bp_outa      )      )
             error_count += errors
             # print('   ----Teach      ',data_teach)
             # print('   ----BP_output  ',bp_out3)
          lowercase_						:     Dict							     =  rp + 1
          lowercase_						:     str							     =  error_count / patterns
          all_mse.append(a      )

       def draw_error():
          lowercase_						:     Tuple							     =  [error_accuracy for i in range(int(n_repeat * 1.2      )      )]
          plt.plot(a	,  "+-"      )
          plt.plot(a	,  "r--"      )
          plt.xlabel("Learning Times"      )
          plt.ylabel("All_mse"      )
          plt.grid(a	,  alpha=0.5      )
          plt.show()

       print("------------------Training Complished---------------------"      )
       print((" - - Training epoch: ", rp, f"""     - - Mse: {mse:.6f}""")      )
       if draw_e:
          draw_error()
       return mse




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  []
       print("-------------------Start Testing-------------------------"      )
       print((" - - Shape: Test_Data  ", np.shape(a      ))      )
       for p in range(len(a      )      ):
          lowercase_						:     int							     =  np.asmatrix(datas_test[p]      )
          lowercase_     ,		lowercase_						:     Dict							     =  self.convolute(
              a	,  self.conva	,  self.w_conva	,  self.thre_conva	,  conv_step=self.step_conva	,  )
          lowercase_						:     Optional[int]							     =  self.pooling(a	,  self.size_poolinga      )
          lowercase_						:     Any							     =  self._expand(a      )

          lowercase_						:     List[Any]							     =  data_bp_input
          lowercase_						:     Tuple							     =  bp_outa * self.vji.T - self.thre_bpa
          lowercase_						:     int							     =  self.sig(a      )
          lowercase_						:     List[str]							     =  bp_outa * self.wkj.T - self.thre_bpa
          lowercase_						:     Optional[Any]							     =  self.sig(a      )
          produce_out.extend(bp_outa.getA().tolist()      )
       lowercase_						:     Optional[Any]							     =  [list(map(self.do_round	,  a      )      ) for each in produce_out]
       return np.asarray(a      )





    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  np.asmatrix(a      )
       lowercase_     ,		lowercase_						:     List[str]							     =  self.convolute(
           a	,  self.conva	,  self.w_conva	,  self.thre_conva	,  conv_step=self.step_conva	,  )
       lowercase_						:     List[Any]							     =  self.pooling(a	,  self.size_poolinga      )

       return data_conveda, data_pooleda


if __name__ == "__main__":
 pass












'''simple docstring'''
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    :




    def __init__(      self	:							int	,  a	:							Dict	,  a	:							Optional[Any]=1_3	,  a	:							str=3_0	,  a	:							Dict=2	,  a	:							List[Any]=3	,  a	:							Optional[int]=True	,  a	:							Tuple=True	,  a	:							List[Any]=3_2	,  a	:							int=2	,  a	:							List[str]=4	,  a	:							Dict=3_7	,  a	:							Optional[Any]="gelu"	,  a	:							List[str]=0.1	,  a	:							Dict=0.1	,  a	:							List[Any]=1_0	,  a	:							Union[str, Any]=0.02	,  a	:							Union[str, Any]=3	,  a	:							int=None	,  a	:							Any=2	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  parent
       lowercase_						:     Union[str, Any]							     =  batch_size
       lowercase_						:     Any							     =  image_size
       lowercase_						:     str							     =  patch_size
       lowercase_						:     int							     =  num_channels
       lowercase_						:     str							     =  is_training
       lowercase_						:     str							     =  use_labels
       lowercase_						:     List[Any]							     =  hidden_size
       lowercase_						:     List[str]							     =  num_hidden_layers
       lowercase_						:     Dict							     =  num_attention_heads
       lowercase_						:     Optional[int]							     =  intermediate_size
       lowercase_						:     str							     =  hidden_act
       lowercase_						:     Union[str, Any]							     =  hidden_dropout_prob
       lowercase_						:     List[str]							     =  attention_probs_dropout_prob
       lowercase_						:     List[str]							     =  type_sequence_label_size
       lowercase_						:     List[Any]							     =  initializer_range
       lowercase_						:     str							     =  scope
       lowercase_						:     Tuple							     =  encoder_stride

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




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       lowercase_						:     List[Any]							     =  None
       if self.use_labels:
          lowercase_						:     int							     =  ids_tensor([self.batch_size]	,  self.type_sequence_label_size      )

       lowercase_						:     Optional[Any]							     =  self.get_config()

       return config, pixel_values, labels




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       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 					lowerCAmelCase__					(      self	:							Any	,  a	:							List[str]	,  a	:							str	,  a	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTModel(config=a      )
       lowercase_						:     int							     =  model(a      )
       self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)      )




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							Optional[int]	,  a	:							List[Any]	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTForMaskedImageModeling(config=a      )
       lowercase_						:     Union[str, Any]							     =  model(a      )
       self.parent.assertEqual(
           result.reconstruction.shape	,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     Tuple							     =  TFDeiTForMaskedImageModeling(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Dict							     =  model(a      )
       self.parent.assertEqual(result.reconstruction.shape	,  (self.batch_size, 1, self.image_size, self.image_size)      )




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[str]	,  a	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.type_sequence_label_size
       lowercase_						:     Union[str, 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_						:     Dict							     =  1
       lowercase_						:     str							     =  TFDeiTForImageClassification(a      )

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





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  self.prepare_config_and_inputs()
       lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  config_and_inputs
       lowercase_						:     Optional[Any]							     =  {"pixel_values": pixel_values}
       return config, inputs_dict


@require_tf
class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Tuple									=     (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase: Tuple									=     (
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    __lowerCamelCase: Union[str, Any]									=     False
    __lowerCamelCase: Any									=     False
    __lowerCamelCase: List[str]									=     False
    __lowerCamelCase: Optional[Any]									=     False




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     int							     =  TFDeiTModelTester(self      )
       lowercase_						:     Any							     =  ConfigTester(self	,  config_class=a	,  has_text_modality=a	,  hidden_size=3_7      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    @unittest.skip(reason="DeiT does not use inputs_embeds"      )
    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Tuple							     =  self.model_tester.prepare_config_and_inputs_for_common()

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




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Union[str, Any]							     =  self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase_						:     Dict							     =  ["pixel_values"]
          self.assertListEqual(arg_names[:1]	,  a      )




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							int	,  a	:							Union[str, Any]	,  a	:							Any=False      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  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 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase_						:     List[Any]							     =  TFDeiTModel.from_pretrained(a      )
          self.assertIsNotNone(a      )




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     str							     =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
   return image





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




    @cached_property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return (
           DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
           if is_vision_available()
           else None
       )





    @slow
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

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

       # forward pass
       lowercase_						:     int							     =  model(**a      )

       # verify the logits
       lowercase_						:     Union[str, Any]							     =  tf.TensorShape((1, 1_0_0_0)      )
       self.assertEqual(outputs.logits.shape	,  a      )

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

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




