


import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):

        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as input_file:
             lowercase_       :Union[str, Any]     =       re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''		)
             lowercase_       :List[Any]     =       input_file.read()
             lowercase_       :Tuple     =       regexp.search(UpperCamelCase_		)

        return match
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):

        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as input_file:
             lowercase_       :str     =       re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''   ,							re.DOTALL		)
             lowercase_       :Union[str, Any]     =       input_file.read()
             # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
             lowercase_       :List[Any]     =       regexp.finditer(UpperCamelCase_		)

        lowercase_       :int     =       [match for match in matches if match is not None and match.group(1		) is not None]
        return matches[0] if matches else None
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       Path('''./datasets'''		)
        lowercase_       :List[str]     =       list(dataset_paths.absolute().glob('''**/*.py'''		)		)

        for dataset in dataset_files:
             if self._no_encoding_on_file_open(str(UpperCamelCase_		)		):
                  raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       Path('''./datasets'''		)
        lowercase_       :Dict     =       list(dataset_paths.absolute().glob('''**/*.py'''		)		)

        for dataset in dataset_files:
             if self._no_print_statements(str(UpperCamelCase_		)		):
                  raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."		)





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		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)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

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

