


"""simple docstring"""


import pickle

import numpy as np
from matplotlib import pyplot as plt

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__=0.2 ,     lowerCamelCase__=0.2						)      ->							Tuple:
    lowercase__       :       Union[str, Any]				=						bp_numa
    lowercase__       :       List[Any]				=						bp_numa
    lowercase__       :       str				=						bp_numa
    lowercase__       :       Union[str, Any]				=						conva_get[:2]
    lowercase__       :       Optional[int]				=						conva_get[2]
    lowercase__       :       str				=						size_pa
    lowercase__       :       Dict				=						rate_w
    lowercase__       :       Any				=						rate_t
    lowercase__       :       Dict				=						[
        np.mat(-1 * np.random.rand(self.conva[0] ,     self.conva[0]						) + 0.5						)
        for i in range(self.conva[1]						)
    ]
    lowercase__       :       int				=						np.mat(-1 * np.random.rand(self.num_bpa ,     self.num_bpa						) + 0.5						)
    lowercase__       :       str				=						np.mat(-1 * np.random.rand(self.num_bpa ,     self.num_bpa						) + 0.5						)
    lowercase__       :       List[Any]				=						-2 * np.random.rand(self.conva[1]						) + 1
    lowercase__       :       Optional[int]				=						-2 * np.random.rand(self.num_bpa						) + 1
    lowercase__       :       Union[str, Any]				=						-2 * np.random.rand(self.num_bpa						) + 1


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    # save model dict with pickle
    lowercase__       :       int				=						{
        """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(lowerCamelCase__ ,     """wb"""						) as f:
     pickle.dump(lowerCamelCase__ ,     lowerCamelCase__						)

    print(F'''Model saved： {save_path}'''						)


   @classmethod
   def 					UpperCAmelCase__(						cls ,     lowerCamelCase__						)      ->							List[str]:
    # read saved model
    with open(lowerCamelCase__ ,     """rb"""						) as f:
     lowercase__       :       List[str]				=						pickle.load(lowerCamelCase__						)  # noqa: S301

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    return 1 / (1 + np.exp(-1 * x						))


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Optional[Any]:
    return round(lowerCamelCase__ ,     3						)


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

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__="average_pool"						)      ->							List[Any]:
    # pooling process
    lowercase__       :       List[Any]				=						len(featuremaps[0]						)
    lowercase__       :       Optional[Any]				=						int(size_map / size_pooling						)
    lowercase__       :       List[str]				=						[]
    for i_map in range(len(lowerCamelCase__						)						):
     lowercase__       :       Optional[Any]				=						featuremaps[i_map]
     lowercase__       :       List[Any]				=						[]
     for i_focus in range(0 ,     lowerCamelCase__ ,     lowerCamelCase__						):
      for j_focus in range(0 ,     lowerCamelCase__ ,     lowerCamelCase__						):
       lowercase__       :       Dict				=						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(lowerCamelCase__						)						)
       elif pooling_type == "max_pooling":
        # max pooling
        map_pooled.append(np.max(lowerCamelCase__						)						)
     lowercase__       :       Dict				=						np.asmatrix(lowerCamelCase__						).reshape(lowerCamelCase__ ,     lowerCamelCase__						)
     featuremap_pooled.append(lowerCamelCase__						)
    return featuremap_pooled


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    # expanding three dimension data to one dimension list
    lowercase__       :       Optional[Any]				=						[]
    for i in range(len(lowerCamelCase__						)						):
     lowercase__       :       Tuple				=						np.shape(data[i]						)
     lowercase__       :       List[str]				=						data[i].reshape(1 ,     shapes[0] * shapes[1]						)
     lowercase__       :       List[Any]				=						data_listed.getA().tolist()[0]
     data_expanded.extend(lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						np.asarray(lowerCamelCase__						)
    return data_expanded


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Optional[int]:
    # expanding matrix to one dimension list
    lowercase__       :       str				=						np.asarray(lowerCamelCase__						)
    lowercase__       :       Tuple				=						np.shape(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						data_mat.reshape(1 ,     shapes[0] * shapes[1]						)
    return data_expanded


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Any:
    lowercase__       :       Any				=						[]
    lowercase__       :       int				=						0
    for i_map in range(lowerCamelCase__						):
     lowercase__       :       List[Any]				=						np.ones((size_map, size_map)						)
     for i in range(0 ,     lowerCamelCase__ ,     lowerCamelCase__						):
      for j in range(0 ,     lowerCamelCase__ ,     lowerCamelCase__						):
       lowercase__       :       Dict				=						pd_pool[
           i_pool
       ]
       lowercase__       :       List[Any]				=						i_pool + 1
     lowercase__       :       Union[str, Any]				=						np.multiply(
         lowerCamelCase__ ,     np.multiply(out_map[i_map] ,     (1 - out_map[i_map])						)						)
     pd_all.append(lowerCamelCase__						)
    return pd_all


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__=bool						)      ->							List[Any]:
    # model traning
    print("""----------------------Start Training-------------------------"""						)
    print((""" - - Shape: Train_Data  """, np.shape(lowerCamelCase__						))						)
    print((""" - - Shape: Teach_Data  """, np.shape(lowerCamelCase__						))						)
    lowercase__       :       int				=						0
    lowercase__       :       Any				=						[]
    lowercase__       :       int				=						1_0000
    while rp < n_repeat and mse >= error_accuracy:
     lowercase__       :       Union[str, Any]				=						0
     print(F'''-------------Learning Time {rp}--------------'''						)
     for p in range(len(lowerCamelCase__						)						):
      # print('------------Learning Image: %d--------------'%p)
      lowercase__       :       Optional[Any]				=						np.asmatrix(datas_train[p]						)
      lowercase__       :       Any				=						np.asarray(datas_teach[p]						)
      lowercase__	,       lowercase__       :       Optional[int]				=						self.convolute(
          lowerCamelCase__ ,     self.conva ,     self.w_conva ,     self.thre_conva ,     conv_step=self.step_conva ,     )
      lowercase__       :       Tuple				=						self.pooling(lowerCamelCase__ ,     self.size_poolinga						)
      lowercase__       :       Optional[Any]				=						np.shape(lowerCamelCase__						)
      lowercase__       :       Dict				=						self._expand(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						data_bp_input

      lowercase__       :       Union[str, Any]				=						np.dot(lowerCamelCase__ ,     self.vji.T						) - self.thre_bpa
      lowercase__       :       Tuple				=						self.sig(lowerCamelCase__						)
      lowercase__       :       int				=						np.dot(lowerCamelCase__ ,     self.wkj.T						) - self.thre_bpa
      lowercase__       :       Dict				=						self.sig(lowerCamelCase__						)

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

      lowercase__       :       Optional[int]				=						pd_i_all / (self.size_poolinga * self.size_poolinga)
      lowercase__       :       Optional[Any]				=						pd_conva_pooled.T.getA().tolist()
      lowercase__       :       List[str]				=						self._calculate_gradient_from_pool(
          lowerCamelCase__ ,     lowerCamelCase__ ,     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__       :       int				=						self._expand_mat(pd_conva_all[k_conv]						)
       lowercase__       :       int				=						self.rate_weight * np.dot(lowerCamelCase__ ,     lowerCamelCase__						)

       lowercase__       :       Optional[int]				=						self.w_conva[k_conv] + delta_w.reshape(
           (self.conva[0], self.conva[0])						)

       lowercase__       :       List[str]				=						(
           self.thre_conva[k_conv]
           - np.sum(pd_conva_all[k_conv]						) * self.rate_thre
       )
      # all connected layer
      lowercase__       :       Optional[int]				=						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__       :       Any				=						self.thre_bpa - pd_j_all * self.rate_thre
      # calculate the sum error of all single image
      lowercase__       :       List[Any]				=						np.sum(abs(data_teach - bp_outa						)						)
      error_count += errors
      # print('   ----Teach      ',data_teach)
      # print('   ----BP_output  ',bp_out3)
     lowercase__       :       Dict				=						rp + 1
     lowercase__       :       Any				=						error_count / patterns
     all_mse.append(lowerCamelCase__						)

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

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Dict:
    # model predict
    lowercase__       :       Optional[int]				=						[]
    print("""-------------------Start Testing-------------------------"""						)
    print((""" - - Shape: Test_Data  """, np.shape(lowerCamelCase__						))						)
    for p in range(len(lowerCamelCase__						)						):
     lowercase__       :       int				=						np.asmatrix(datas_test[p]						)
     lowercase__	,       lowercase__       :       Tuple				=						self.convolute(
         lowerCamelCase__ ,     self.conva ,     self.w_conva ,     self.thre_conva ,     conv_step=self.step_conva ,     )
     lowercase__       :       Tuple				=						self.pooling(lowerCamelCase__ ,     self.size_poolinga						)
     lowercase__       :       int				=						self._expand(lowerCamelCase__						)

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







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Any:
    # return the data of image after convoluting process so we can check it out
    lowercase__       :       Union[str, Any]				=						np.asmatrix(lowerCamelCase__						)
    lowercase__	,       lowercase__       :       Any				=						self.convolute(
        lowerCamelCase__ ,     self.conva ,     self.w_conva ,     self.thre_conva ,     conv_step=self.step_conva ,     )
    lowercase__       :       Tuple				=						self.pooling(lowerCamelCase__ ,     self.size_poolinga						)

    return data_conveda, data_pooleda


if __name__ == "__main__":
   pass



"""simple docstring"""


import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


__snake_case    					=					{
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 128,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}

@is_staging_test
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							Dict:
    lowercase__       :       str				=						TOKEN
    HfFolder.save_token(lowerCamelCase__						)


   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							Optional[Any]:
    try:
     delete_repo(token=cls._token ,     repo_id="""test-config"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""valid_org/test-config-org"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""test-dynamic-config"""						)
    except HTTPError:
     pass


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						BertConfig(
        vocab_size=99 ,     hidden_size=32 ,     num_hidden_layers=5 ,     num_attention_heads=4 ,     intermediate_size=37						)
    config.push_to_hub("""test-config""" ,     use_auth_token=self._token						)

    lowercase__       :       Union[str, Any]				=						BertConfig.from_pretrained(F'''{USER}/test-config'''						)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

        # Reset repo
    delete_repo(token=self._token ,     repo_id="""test-config"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     config.save_pretrained(lowerCamelCase__ ,     repo_id="""test-config""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       Optional[Any]				=						BertConfig.from_pretrained(F'''{USER}/test-config'''						)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       int				=						BertConfig(
        vocab_size=99 ,     hidden_size=32 ,     num_hidden_layers=5 ,     num_attention_heads=4 ,     intermediate_size=37						)
    config.push_to_hub("""valid_org/test-config-org""" ,     use_auth_token=self._token						)

    lowercase__       :       List[str]				=						BertConfig.from_pretrained("""valid_org/test-config-org"""						)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

        # Reset repo
    delete_repo(token=self._token ,     repo_id="""valid_org/test-config-org"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     config.save_pretrained(
         lowerCamelCase__ ,     repo_id="""valid_org/test-config-org""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       Optional[Any]				=						BertConfig.from_pretrained("""valid_org/test-config-org"""						)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    CustomConfig.register_for_auto_class()
    lowercase__       :       int				=						CustomConfig(attribute=42						)

    config.push_to_hub("""test-dynamic-config""" ,     use_auth_token=self._token						)

    # This has added the proper auto_map field to the config
    self.assertDictEqual(config.auto_map ,     {"""AutoConfig""": """custom_configuration.CustomConfig"""}						)

    lowercase__       :       Any				=						AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' ,     trust_remote_code=lowerCamelCase__						)
    # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
    self.assertEqual(new_config.__class__.__name__ ,     """CustomConfig"""						)
    self.assertEqual(new_config.attribute ,     42						)

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						GPTaConfig()

    # attempt to modify each of int/float/bool/str config records and verify they were updated
    lowercase__       :       List[str]				=						c.n_embd + 1  # int
    lowercase__       :       Union[str, Any]				=						c.resid_pdrop + 1.0  # float
    lowercase__       :       int				=						not c.scale_attn_weights  # bool
    lowercase__       :       Optional[int]				=						c.summary_type + """foo"""  # str
    c.update_from_string(
        F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''						)
    self.assertEqual(lowerCamelCase__ ,     c.n_embd ,     """mismatch for key: n_embd"""						)
    self.assertEqual(lowerCamelCase__ ,     c.resid_pdrop ,     """mismatch for key: resid_pdrop"""						)
    self.assertEqual(lowerCamelCase__ ,     c.scale_attn_weights ,     """mismatch for key: scale_attn_weights"""						)
    self.assertEqual(lowerCamelCase__ ,     c.summary_type ,     """mismatch for key: summary_type"""						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       List[str]				=						PretrainedConfig()
    lowercase__       :       Union[str, Any]				=						[key for key in base_config.__dict__ if key not in config_common_kwargs]
    # If this part of the test fails, you have arguments to addin config_common_kwargs above.
    self.assertListEqual(
        lowerCamelCase__ ,     ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""]						)
    lowercase__       :       List[Any]				=						[key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase__ ,     lowerCamelCase__						)]
    if len(lowerCamelCase__						) > 0:
     raise ValueError(
         """The following keys are set with the default values in"""
         """ `test_configuration_common.config_common_kwargs` pick another value for them:"""
         F''' {', '.join(lowerCamelCase__						)}.'''						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    with self.assertRaises(lowerCamelCase__						):
     # config is in subfolder, the following should not work without specifying the subfolder
     lowercase__       :       str				=						BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""						)

    lowercase__       :       Union[str, Any]				=						BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder""" ,     subfolder="""bert"""						)

    self.assertIsNotNone(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    # A mock response for an HTTP head request to emulate server down
    lowercase__       :       Dict				=						mock.Mock()
    lowercase__       :       str				=						500
    lowercase__       :       int				=						{}
    lowercase__       :       Optional[int]				=						HTTPError
    lowercase__       :       Dict				=						{}

    # Download this model to make sure it's in the cache.
    lowercase__       :       Any				=						BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

    # Under the mock environment we get a 500 error when trying to reach the model.
    with mock.patch("""requests.Session.request""" ,     return_value=lowerCamelCase__						) as mock_head:
     lowercase__       :       int				=						BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)
     # This check we did call the fake head request
     mock_head.assert_called()


   def 					UpperCAmelCase__(						self						)      ->							int:
    # This test is for deprecated behavior and can be removed in v5
    lowercase__       :       Optional[Any]				=						BertConfig.from_pretrained(
        """https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"""						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Optional[int]				=						AutoConfig.from_pretrained("""bert-base-cased"""						)
    lowercase__       :       Union[str, Any]				=						["""config.4.0.0.json"""]

    with tempfile.TemporaryDirectory() as tmp_dir:
     configuration.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Optional[Any]				=						2
     json.dump(configuration.to_dict() ,     open(os.path.join(lowerCamelCase__ ,     """config.4.0.0.json"""						) ,     """w"""						)						)

     # This should pick the new configuration file as the version of Transformers is > 4.0.0
     lowercase__       :       Optional[int]				=						AutoConfig.from_pretrained(lowerCamelCase__						)
     self.assertEqual(new_configuration.hidden_size ,     2						)

     # Will need to be adjusted if we reach v42 and this test is still here.
     # Should pick the old configuration file as the version of Transformers is < 4.42.0
     lowercase__       :       Optional[int]				=						["""config.42.0.0.json"""]
     lowercase__       :       Optional[int]				=						768
     configuration.save_pretrained(lowerCamelCase__						)
     shutil.move(os.path.join(lowerCamelCase__ ,     """config.4.0.0.json"""						) ,     os.path.join(lowerCamelCase__ ,     """config.42.0.0.json"""						)						)
     lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__						)
     self.assertEqual(new_configuration.hidden_size ,     768						)







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
    lowercase__       :       Any				=						"""hf-internal-testing/test-two-configs"""

    import transformers as new_transformers

    lowercase__       :       Union[str, Any]				=						"""v4.0.0"""
    lowercase__	,       lowercase__       :       int				=						new_transformers.models.auto.AutoConfig.from_pretrained(
        lowerCamelCase__ ,     return_unused_kwargs=lowerCamelCase__						)
    self.assertEqual(new_configuration.hidden_size ,     2						)
    # This checks `_configuration_file` ia not kept in the kwargs by mistake.
    self.assertDictEqual(lowerCamelCase__ ,     {}						)

    # Testing an older version by monkey-patching the version in the module it's used.
    import transformers as old_transformers

    lowercase__       :       str				=						"""v3.0.0"""
    lowercase__       :       int				=						old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase__						)
    self.assertEqual(old_configuration.hidden_size ,     768						)