

from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

# Initialize new model with config
a_      =     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, is_vision_available


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



