"""simple docstring"""







import os
import sys
import unittest


_a  :  Union[str, Any]=				os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_a  :  int=				os.path.join("tests", "models", "bert", "test_modeling_bert.py")
_a  :  Tuple=				os.path.join("tests", "models", "blip", "test_modeling_blip.py")

class 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[str])					->			List[Any]:
					__snake_case    :							Optional[Any]   	=				get_test_to_tester_mapping(_A)
					__snake_case    :							str   	=				get_test_to_tester_mapping(_A)

					__snake_case    :							str   	=				{'BertModelTest': 'BertModelTester'}

					__snake_case    :							List[str]   	=				{
					    'BlipModelTest': 'BlipModelTester',
					    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
					    'BlipTextModelTest': 'BlipTextModelTester',
					    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
					    'BlipVQAModelTest': 'BlipVQAModelTester',
					    'BlipVisionModelTest': 'BlipVisionModelTester',
					}

					self.assertEqual(get_test_info.to_json(_A)			,						_A)
					self.assertEqual(get_test_info.to_json(_A)			,						_A)



				def       _lowercase  (self       :   List[Any])					->			str:
					__snake_case    :							List[Any]   	=				get_model_to_test_mapping(_A)
					__snake_case    :							Tuple   	=				get_model_to_test_mapping(_A)

					__snake_case    :							List[Any]   	=				{
					    'BertForMaskedLM': ['BertModelTest'],
					    'BertForMultipleChoice': ['BertModelTest'],
					    'BertForNextSentencePrediction': ['BertModelTest'],
					    'BertForPreTraining': ['BertModelTest'],
					    'BertForQuestionAnswering': ['BertModelTest'],
					    'BertForSequenceClassification': ['BertModelTest'],
					    'BertForTokenClassification': ['BertModelTest'],
					    'BertLMHeadModel': ['BertModelTest'],
					    'BertModel': ['BertModelTest'],
					}

					__snake_case    :							str   	=				{
					    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
					    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
					    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
					    'BlipModel': ['BlipModelTest'],
					    'BlipTextModel': ['BlipTextModelTest'],
					    'BlipVisionModel': ['BlipVisionModelTest'],
					}

					self.assertEqual(get_test_info.to_json(_A)			,						_A)
					self.assertEqual(get_test_info.to_json(_A)			,						_A)






				def       _lowercase  (self       :   Any)					->			Any:
					__snake_case    :							int   	=				get_model_to_tester_mapping(_A)
					__snake_case    :							Dict   	=				get_model_to_tester_mapping(_A)

					__snake_case    :							Optional[int]   	=				{
					    'BertForMaskedLM': ['BertModelTester'],
					    'BertForMultipleChoice': ['BertModelTester'],
					    'BertForNextSentencePrediction': ['BertModelTester'],
					    'BertForPreTraining': ['BertModelTester'],
					    'BertForQuestionAnswering': ['BertModelTester'],
					    'BertForSequenceClassification': ['BertModelTester'],
					    'BertForTokenClassification': ['BertModelTester'],
					    'BertLMHeadModel': ['BertModelTester'],
					    'BertModel': ['BertModelTester'],
					}

					__snake_case    :							Optional[Any]   	=				{
					    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
					    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
					    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
					    'BlipModel': ['BlipModelTester'],
					    'BlipTextModel': ['BlipTextModelTester'],
					    'BlipVisionModel': ['BlipVisionModelTester'],
					}

					self.assertEqual(get_test_info.to_json(_A)			,						_A)
					self.assertEqual(get_test_info.to_json(_A)			,						_A)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





