






"""simple docstring"""







import pickle

import numpy as np
from matplotlib import pyplot as plt







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





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

												'''simple docstring'''

												a__          =  bp_numa
												a__          =  bp_numa
												a__          =  bp_numa
												a__          =  conva_get[:2]
												a__          =  conva_get[2]
												a__          =  size_pa
												a__          =  rate_w
												a__          =  rate_t
												a__          =  [
												    np.mat(-1 * np.random.rand(self.conva[0]				,		self.conva[0]    ) + 0.5    )
												    for i in range(self.conva[1]    )
												]
												a__          =  np.mat(-1 * np.random.rand(self.num_bpa				,		self.num_bpa    ) + 0.5    )
												a__          =  np.mat(-1 * np.random.rand(self.num_bpa				,		self.num_bpa    ) + 0.5    )
												a__          =  -2 * np.random.rand(self.conva[1]    ) + 1
												a__          =  -2 * np.random.rand(self.num_bpa    ) + 1
												a__          =  -2 * np.random.rand(self.num_bpa    ) + 1




							def  lowercase		(   self:     List[str]				,		__A:     str    ):

												'''simple docstring'''

												a__          =  {
												    '''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  lowercase		(   cls:     Dict				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												with open(__A				,		'''rb'''    ) as f:
																	a__          =  pickle.load(__A    )  # noqa: S301

												a__          =  model_dic.get('''conv1'''    )
												conv_get.append(model_dic.get('''step_conv1'''    )    )
												a__          =  model_dic.get('''size_pooling1'''    )
												a__          =  model_dic.get('''num_bp1'''    )
												a__          =  model_dic.get('''num_bp2'''    )
												a__          =  model_dic.get('''num_bp3'''    )
												a__          =  model_dic.get('''rate_weight'''    )
												a__          =  model_dic.get('''rate_thre'''    )
												# create model instance
												a__          =  CNN(__A				,		__A				,		__A				,		__A				,		__A				,		__A				,		__A    )
												# modify model parameter
												a__          =  model_dic.get('''w_conv1'''    )
												a__          =  model_dic.get('''wkj'''    )
												a__          =  model_dic.get('''vji'''    )
												a__          =  model_dic.get('''thre_conv1'''    )
												a__          =  model_dic.get('''thre_bp2'''    )
												a__          =  model_dic.get('''thre_bp3'''    )
												return conv_ins




							def  lowercase		(   self:     List[Any]				,		__A:     List[str]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Dict				,		__A:     List[Any]    ):

												'''simple docstring'''

												return round(__A				,		3    )




							def  lowercase		(   self:     List[Any]				,		__A:     Optional[int]				,		__A:     List[str]				,		__A:     List[Any]				,		__A:     Union[str, Any]				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  convs[0]
												a__          =  convs[1]
												a__          =  np.shape(__A    )[0]
												# get the data slice of original image data, data_focus
												a__          =  []
												for i_focus in range(0				,		size_data - size_conv + 1				,		__A    ):
																	for j_focus in range(0				,		size_data - size_conv + 1				,		__A    ):
																						a__          =  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
												a__          =  []
												a__          =  int((size_data - size_conv) / conv_step + 1    )
												for i_map in range(__A    ):
																	a__          =  []
																	for i_focus in range(len(__A    )    ):
																						a__          =  (
																						    np.sum(np.multiply(data_focus[i_focus]				,		w_convs[i_map]    )    )
																						    - thre_convs[i_map]
																						)
																						featuremap.append(self.sig(__A    )    )
																	a__          =  np.asmatrix(__A    ).reshape(
																	    __A				,		__A    )
																	data_featuremap.append(__A    )

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




							def  lowercase		(   self:     Any				,		__A:     Union[str, Any]				,		__A:     str				,		__A:     Tuple="average_pool"    ):

												'''simple docstring'''

												a__          =  len(featuremaps[0]    )
												a__          =  int(size_map / size_pooling    )
												a__          =  []
												for i_map in range(len(__A    )    ):
																	a__          =  featuremaps[i_map]
																	a__          =  []
																	for i_focus in range(0				,		__A				,		__A    ):
																						for j_focus in range(0				,		__A				,		__A    ):
																											a__          =  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    )    )
																	a__          =  np.asmatrix(__A    ).reshape(__A				,		__A    )
																	featuremap_pooled.append(__A    )
												return featuremap_pooled




							def  lowercase		(   self:     Tuple				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  []
												for i in range(len(__A    )    ):
																	a__          =  np.shape(data[i]    )
																	a__          =  data[i].reshape(1				,		shapes[0] * shapes[1]    )
																	a__          =  data_listed.getA().tolist()[0]
																	data_expanded.extend(__A    )
												a__          =  np.asarray(__A    )
												return data_expanded




							def  lowercase		(   self:     Optional[int]				,		__A:     Any    ):

												'''simple docstring'''

												a__          =  np.asarray(__A    )
												a__          =  np.shape(__A    )
												a__          =  data_mat.reshape(1				,		shapes[0] * shapes[1]    )
												return data_expanded




							def  lowercase		(   self:     Optional[int]				,		__A:     Any				,		__A:     Tuple				,		__A:     Tuple				,		__A:     Dict				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  []
												a__          =  0
												for i_map in range(__A    ):
																	a__          =  np.ones((size_map, size_map)    )
																	for i in range(0				,		__A				,		__A    ):
																						for j in range(0				,		__A				,		__A    ):
																											a__          =  pd_pool[
																											    i_pool
																											]
																											a__          =  i_pool + 1
																	a__          =  np.multiply(
																	    __A				,		np.multiply(out_map[i_map]				,		(1 - out_map[i_map])    )    )
																	pd_all.append(__A    )
												return pd_all




							def  lowercase		(   self:     List[Any]				,		__A:     Union[str, Any]				,		__A:     List[str]				,		__A:     Optional[Any]				,		__A:     Any				,		__A:     Optional[Any]				,		__A:     Any=bool    ):

												'''simple docstring'''

												print('''----------------------Start Training-------------------------'''    )
												print((''' - - Shape: Train_Data  ''', np.shape(__A    ))    )
												print((''' - - Shape: Teach_Data  ''', np.shape(__A    ))    )
												a__          =  0
												a__          =  []
												a__          =  10000
												while rp < n_repeat and mse >= error_accuracy:
																	a__          =  0
																	print(F'-------------Learning Time {rp}--------------'    )
																	for p in range(len(__A    )    ):
																						# print('------------Learning Image: %d--------------'%p)
																						a__          =  np.asmatrix(datas_train[p]    )
																						a__          =  np.asarray(datas_teach[p]    )
																						a__       ,a__          =  self.convolute(
																						    __A				,		self.conva				,		self.w_conva				,		self.thre_conva				,		conv_step=self.step_conva				,		)
																						a__          =  self.pooling(__A				,		self.size_poolinga    )
																						a__          =  np.shape(__A    )
																						a__          =  self._expand(__A    )
																						a__          =  data_bp_input

																						a__          =  np.dot(__A				,		self.vji.T    ) - self.thre_bpa
																						a__          =  self.sig(__A    )
																						a__          =  np.dot(__A				,		self.wkj.T    ) - self.thre_bpa
																						a__          =  self.sig(__A    )

																						# --------------Model Leaning ------------------------
																						# calculate error and gradient---------------
																						a__          =  np.multiply(
																						    (data_teach - bp_outa)				,		np.multiply(__A				,		(1 - bp_outa)    )    )
																						a__          =  np.multiply(
																						    np.dot(__A				,		self.wkj    )				,		np.multiply(__A				,		(1 - bp_outa)    )    )
																						a__          =  np.dot(__A				,		self.vji    )

																						a__          =  pd_i_all / (self.size_poolinga * self.size_poolinga)
																						a__          =  pd_conva_pooled.T.getA().tolist()
																						a__          =  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]    ):
																											a__          =  self._expand_mat(pd_conva_all[k_conv]    )
																											a__          =  self.rate_weight * np.dot(__A				,		__A    )

																											a__          =  self.w_conva[k_conv] + delta_w.reshape(
																											    (self.conva[0], self.conva[0])    )

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

												def draw_error():
																	a__          =  [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  lowercase		(   self:     Optional[int]				,		__A:     int    ):

												'''simple docstring'''

												a__          =  []
												print('''-------------------Start Testing-------------------------'''    )
												print((''' - - Shape: Test_Data  ''', np.shape(__A    ))    )
												for p in range(len(__A    )    ):
																	a__          =  np.asmatrix(datas_test[p]    )
																	a__       ,a__          =  self.convolute(
																	    __A				,		self.conva				,		self.w_conva				,		self.thre_conva				,		conv_step=self.step_conva				,		)
																	a__          =  self.pooling(__A				,		self.size_poolinga    )
																	a__          =  self._expand(__A    )

																	a__          =  data_bp_input
																	a__          =  bp_outa * self.vji.T - self.thre_bpa
																	a__          =  self.sig(__A    )
																	a__          =  bp_outa * self.wkj.T - self.thre_bpa
																	a__          =  self.sig(__A    )
																	produce_out.extend(bp_outa.getA().tolist()    )
												a__          =  [list(map(self.do_round				,		__A    )    ) for each in produce_out]
												return np.asarray(__A    )





							def  lowercase		(   self:     List[str]				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  np.asmatrix(__A    )
												a__       ,a__          =  self.convolute(
												    __A				,		self.conva				,		self.w_conva				,		self.thre_conva				,		conv_step=self.step_conva				,		)
												a__          =  self.pooling(__A				,		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


__a       :	Dict             = {
    '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  lowercase		(   cls:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  TOKEN
												HfFolder.save_token(__A    )




							@classmethod
							def  lowercase		(   cls:     Any    ):

												'''simple docstring'''

												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  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  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    )

												a__          =  BertConfig.from_pretrained(F'{USER}/test-config'    )
												for k, v in config.to_dict().items():
																	if k != "transformers_version":
																						self.assertEqual(__A				,		getattr(__A				,		__A    )    )

        # 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(__A				,		repo_id='''test-config'''				,		push_to_hub=__A				,		use_auth_token=self._token    )

												a__          =  BertConfig.from_pretrained(F'{USER}/test-config'    )
												for k, v in config.to_dict().items():
																	if k != "transformers_version":
																						self.assertEqual(__A				,		getattr(__A				,		__A    )    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  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    )

												a__          =  BertConfig.from_pretrained('''valid_org/test-config-org'''    )
												for k, v in config.to_dict().items():
																	if k != "transformers_version":
																						self.assertEqual(__A				,		getattr(__A				,		__A    )    )

        # 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(
																	    __A				,		repo_id='''valid_org/test-config-org'''				,		push_to_hub=__A				,		use_auth_token=self._token    )

												a__          =  BertConfig.from_pretrained('''valid_org/test-config-org'''    )
												for k, v in config.to_dict().items():
																	if k != "transformers_version":
																						self.assertEqual(__A				,		getattr(__A				,		__A    )    )





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												CustomConfig.register_for_auto_class()
												a__          =  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'''}    )

												a__          =  AutoConfig.from_pretrained(F'{USER}/test-dynamic-config'				,		trust_remote_code=__A    )
												# 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  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  GPTaConfig()

												# attempt to modify each of int/float/bool/str config records and verify they were updated
												a__          =  c.n_embd + 1  # int
												a__          =  c.resid_pdrop + 1.0  # float
												a__          =  not c.scale_attn_weights  # bool
												a__          =  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(__A				,		c.n_embd				,		'''mismatch for key: n_embd'''    )
												self.assertEqual(__A				,		c.resid_pdrop				,		'''mismatch for key: resid_pdrop'''    )
												self.assertEqual(__A				,		c.scale_attn_weights				,		'''mismatch for key: scale_attn_weights'''    )
												self.assertEqual(__A				,		c.summary_type				,		'''mismatch for key: summary_type'''    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  PretrainedConfig()
												a__          =  [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(
												    __A				,		['''is_encoder_decoder''', '''_name_or_path''', '''_commit_hash''', '''transformers_version''']    )
												a__          =  [key for key, value in config_common_kwargs.items() if value == getattr(__A				,		__A    )]
												if len(__A    ) > 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(__A    )}.'    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												with self.assertRaises(__A    ):
																	# config is in subfolder, the following should not work without specifying the subfolder
																	a__          =  BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder'''    )

												a__          =  BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder'''				,		subfolder='''bert'''    )

												self.assertIsNotNone(__A    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  mock.Mock()
												a__          =  500
												a__          =  {}
												a__          =  HTTPError
												a__          =  {}

												# Download this model to make sure it's in the cache.
												a__          =  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=__A    ) as mock_head:
																	a__          =  BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )
																	# This check we did call the fake head request
																	mock_head.assert_called()




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  BertConfig.from_pretrained(
												    '''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'''    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  AutoConfig.from_pretrained('''bert-base-cased'''    )
												a__          =  ['''config.4.0.0.json''']

												with tempfile.TemporaryDirectory() as tmp_dir:
																	configuration.save_pretrained(__A    )
																	a__          =  2
																	json.dump(configuration.to_dict()				,		open(os.path.join(__A				,		'''config.4.0.0.json'''    )				,		'''w'''    )    )

																	# This should pick the new configuration file as the version of Transformers is > 4.0.0
																	a__          =  AutoConfig.from_pretrained(__A    )
																	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
																	a__          =  ['''config.42.0.0.json''']
																	a__          =  768
																	configuration.save_pretrained(__A    )
																	shutil.move(os.path.join(__A				,		'''config.4.0.0.json'''    )				,		os.path.join(__A				,		'''config.42.0.0.json'''    )    )
																	a__          =  AutoConfig.from_pretrained(__A    )
																	self.assertEqual(new_configuration.hidden_size				,		768    )





							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  '''hf-internal-testing/test-two-configs'''

												import transformers as new_transformers

												a__          =  '''v4.0.0'''
												a__       ,a__          =  new_transformers.models.auto.AutoConfig.from_pretrained(
												    __A				,		return_unused_kwargs=__A    )
												self.assertEqual(new_configuration.hidden_size				,		2    )
												# This checks `_configuration_file` ia not kept in the kwargs by mistake.
												self.assertDictEqual(__A				,		{}    )

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

												a__          =  '''v3.0.0'''
												a__          =  old_transformers.models.auto.AutoConfig.from_pretrained(__A    )
												self.assertEqual(old_configuration.hidden_size				,		768    )


