'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase_     		= logging.getLogger(__name__)
@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      str       =							field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
		lowercase   :      Optional[str]       =							field(
		    default=__lowercase    ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'})
		lowercase   :      Optional[str]       =							field(
		    default='NER'    ,			metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'})
		lowercase   :      Optional[str]       =							field(
		    default=__lowercase    ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
		lowercase   :      bool       =							field(default=__lowercase    ,			metadata={'help': 'Set this flag to use fast tokenization.'})
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowercase   :      Optional[str]       =							field(
		    default=__lowercase    ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,			)

@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      str       =							field(
		    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'})
		lowercase   :      Optional[str]       =							field(
		    default=__lowercase    ,			metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    ,			)
		lowercase   :      int       =							field(
		    default=1_28    ,			metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }    ,			)
		lowercase   :      bool       =							field(
		    default=__lowercase    ,			metadata={'help': 'Overwrite the cached training and evaluation sets'})



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
						if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
						else:
												_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir      )
						    and os.listdir(training_args.output_dir      )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    ' --overwrite_output_dir to overcome.'      )

						_lowerCAmelCase										=import_module('tasks'      )
						try:
												_lowerCAmelCase										=getattr(_SCREAMING_SNAKE_CASE			,      model_args.task_type      )
												_lowerCAmelCase										=token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
												    F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''      )

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,      datefmt='%m/%d/%Y %H:%M:%S'			,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,      )
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'			,      training_args.local_rank			,      training_args.device			,      training_args.n_gpu			,      bool(training_args.local_rank != -1      )			,      training_args.fpaa			,      )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank      ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('Training/evaluation parameters %s'			,      _SCREAMING_SNAKE_CASE      )

						# Set seed
						set_seed(training_args.seed      )

						# Prepare CONLL-2003 task
						_lowerCAmelCase										=token_classification_task.get_labels(data_args.labels      )
						_lowerCAmelCase										=dict(enumerate(_SCREAMING_SNAKE_CASE      )      )
						_lowerCAmelCase										=len(_SCREAMING_SNAKE_CASE      )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						_lowerCAmelCase										=AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,      num_labels=_SCREAMING_SNAKE_CASE			,      idalabel=_SCREAMING_SNAKE_CASE			,      labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE      )}			,      cache_dir=model_args.cache_dir			,      )
						_lowerCAmelCase										=AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,      cache_dir=model_args.cache_dir			,      use_fast=model_args.use_fast			,      )
						_lowerCAmelCase										=AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path			,      from_tf=bool('.ckpt' in model_args.model_name_or_path      )			,      config=_SCREAMING_SNAKE_CASE			,      cache_dir=model_args.cache_dir			,      )

						# Get datasets
						_lowerCAmelCase										=(
						    TokenClassificationDataset(
						        token_classification_task=_SCREAMING_SNAKE_CASE			,      data_dir=data_args.data_dir			,      tokenizer=_SCREAMING_SNAKE_CASE			,      labels=_SCREAMING_SNAKE_CASE			,      model_type=config.model_type			,      max_seq_length=data_args.max_seq_length			,      overwrite_cache=data_args.overwrite_cache			,      mode=Split.train			,      )
						    if training_args.do_train
						    else None
						)
						_lowerCAmelCase										=(
						    TokenClassificationDataset(
						        token_classification_task=_SCREAMING_SNAKE_CASE			,      data_dir=data_args.data_dir			,      tokenizer=_SCREAMING_SNAKE_CASE			,      labels=_SCREAMING_SNAKE_CASE			,      model_type=config.model_type			,      max_seq_length=data_args.max_seq_length			,      overwrite_cache=data_args.overwrite_cache			,      mode=Split.dev			,      )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(a__			,      a__      ) -> Tuple[List[int], List[int]]:
												_lowerCAmelCase										=np.argmax(_SCREAMING_SNAKE_CASE			,      axis=2      )

												_lowerCAmelCase						,				_lowerCAmelCase										=preds.shape

												_lowerCAmelCase										=[[] for _ in range(_SCREAMING_SNAKE_CASE      )]
												_lowerCAmelCase										=[[] for _ in range(_SCREAMING_SNAKE_CASE      )]

												for i in range(_SCREAMING_SNAKE_CASE      ):
																		for j in range(_SCREAMING_SNAKE_CASE      ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]      )
																														preds_list[i].append(label_map[preds[i][j]]      )

												return preds_list, out_label_list

						def compute_metrics(a__      ) -> Dict:
												_lowerCAmelCase						,				_lowerCAmelCase										=align_predictions(p.predictions			,      p.label_ids      )
												return {
												    "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      ),
												    "precision": precision_score(_SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      ),
												    "recall": recall_score(_SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      ),
												    "f1": fa_score(_SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      ),
												}

						# Data collator
						_lowerCAmelCase										=DataCollatorWithPadding(_SCREAMING_SNAKE_CASE			,      pad_to_multiple_of=8      ) if training_args.fpaa else None

						# Initialize our Trainer
						_lowerCAmelCase										=Trainer(
						    model=_SCREAMING_SNAKE_CASE			,      args=_SCREAMING_SNAKE_CASE			,      train_dataset=_SCREAMING_SNAKE_CASE			,      eval_dataset=_SCREAMING_SNAKE_CASE			,      compute_metrics=_SCREAMING_SNAKE_CASE			,      data_collator=_SCREAMING_SNAKE_CASE			,      )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir      )

												# Evaluation
						_lowerCAmelCase										={}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'      )

												_lowerCAmelCase										=trainer.evaluate()

												_lowerCAmelCase										=os.path.join(training_args.output_dir			,      'eval_results.txt'      )
												if trainer.is_world_process_zero():
																		with open(_SCREAMING_SNAKE_CASE			,      'w'      ) as writer:
																								logger.info('***** Eval results *****'      )
																								for key, value in result.items():
																														logger.info('  %s = %s'			,      _SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      )
																														writer.write('%s = %s\n' % (key, value)      )

																		results.update(_SCREAMING_SNAKE_CASE      )

												# Predict
						if training_args.do_predict:
												_lowerCAmelCase										=TokenClassificationDataset(
												    token_classification_task=_SCREAMING_SNAKE_CASE			,      data_dir=data_args.data_dir			,      tokenizer=_SCREAMING_SNAKE_CASE			,      labels=_SCREAMING_SNAKE_CASE			,      model_type=config.model_type			,      max_seq_length=data_args.max_seq_length			,      overwrite_cache=data_args.overwrite_cache			,      mode=Split.test			,      )

												_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=trainer.predict(_SCREAMING_SNAKE_CASE      )
												_lowerCAmelCase						,				_lowerCAmelCase										=align_predictions(_SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      )

												_lowerCAmelCase										=os.path.join(training_args.output_dir			,      'test_results.txt'      )
												if trainer.is_world_process_zero():
																		with open(_SCREAMING_SNAKE_CASE			,      'w'      ) as writer:
																								for key, value in metrics.items():
																														logger.info('  %s = %s'			,      _SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      )
																														writer.write('%s = %s\n' % (key, value)      )

																								# Save predictions
												_lowerCAmelCase										=os.path.join(training_args.output_dir			,      'test_predictions.txt'      )
												if trainer.is_world_process_zero():
																		with open(_SCREAMING_SNAKE_CASE			,      'w'      ) as writer:
																								with open(os.path.join(data_args.data_dir			,      'test.txt'      )			,      'r'      ) as f:
																														token_classification_task.write_predictions_to_file(_SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE			,      _SCREAMING_SNAKE_CASE      )

						return results



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						main()


if __name__ == "__main__":
							main()






'''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


lowercase_     		= {
    '''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				) ->     Optional[Any]:
								_lowerCAmelCase										=TOKEN
								HfFolder.save_token(__A				)






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     List[str]:
								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				) ->     str:
								_lowerCAmelCase										=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				)

								_lowerCAmelCase										=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				)

								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=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				)

								_lowerCAmelCase										=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				)

								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     List[str]:
								CustomConfig.register_for_auto_class()
								_lowerCAmelCase										=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'}				)

								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=GPTaConfig()

								# attempt to modify each of int/float/bool/str config records and verify they were updated
								_lowerCAmelCase										=c.n_embd + 1  # int
								_lowerCAmelCase										=c.resid_pdrop + 1.0  # float
								_lowerCAmelCase										=not c.scale_attn_weights  # bool
								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=PretrainedConfig()
								_lowerCAmelCase										=[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']				)
								_lowerCAmelCase										=[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 		UpperCamelCase__       (   self				) ->     Optional[int]:
								with self.assertRaises(__A				):
														# config is in subfolder, the following should not work without specifying the subfolder
														_lowerCAmelCase										=BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'				)

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

								self.assertIsNotNone(__A				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								# A mock response for an HTTP head request to emulate server down
								_lowerCAmelCase										=mock.Mock()
								_lowerCAmelCase										=500
								_lowerCAmelCase										={}
								_lowerCAmelCase										=HTTPError
								_lowerCAmelCase										={}

								# Download this model to make sure it's in the cache.
								_lowerCAmelCase										=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:
														_lowerCAmelCase										=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				) ->     Optional[int]:
								# This test is for deprecated behavior and can be removed in v5
								_lowerCAmelCase										=BertConfig.from_pretrained(
								    'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=AutoConfig.from_pretrained('bert-base-cased'				)
								_lowerCAmelCase										=['config.4.0.0.json']

								with tempfile.TemporaryDirectory() as tmp_dir:
														configuration.save_pretrained(__A				)
														_lowerCAmelCase										=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
														_lowerCAmelCase										=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
														_lowerCAmelCase										=['config.42.0.0.json']
														_lowerCAmelCase										=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'				)				)
														_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
														self.assertEqual(new_configuration.hidden_size		,						768				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
								_lowerCAmelCase										='hf-internal-testing/test-two-configs'

								import transformers as new_transformers

								_lowerCAmelCase										='v4.0.0'
								_lowerCAmelCase						,				_lowerCAmelCase										=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

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





