
"""simple docstring"""






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__SCREAMING_SNAKE_CASE							=     HfArgumentParser(InitializationArguments)
__SCREAMING_SNAKE_CASE							=     parser.parse_args()

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

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

# Initialize new model with config
__SCREAMING_SNAKE_CASE							=     AutoModelForCausalLM.from_config(config)

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


"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MCTCTForCTC',
     'MCTCTModel',
     'MCTCTPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
