





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__   :				Any      =   logging.get_logger(__name__)

lowercase__   :				Tuple      =   {
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}


class 			SCREAMING_SNAKE_CASE   (_SCREAMING_SNAKE_CASE							):
     lowerCAmelCase		      =	'''swin2sr'''

     lowerCAmelCase		      =	{
         '''hidden_size''': '''embed_dim''',
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }


     def __init__(						self						,     _UpperCAmelCase=64						,     _UpperCAmelCase=1						,     _UpperCAmelCase=3						,     _UpperCAmelCase=180						,     _UpperCAmelCase=[6, 6, 6, 6, 6, 6]						,     _UpperCAmelCase=[6, 6, 6, 6, 6, 6]						,     _UpperCAmelCase=8						,     _UpperCAmelCase=2.0						,     _UpperCAmelCase=True						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=False						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-5						,     _UpperCAmelCase=2						,     _UpperCAmelCase=1.0						,     _UpperCAmelCase="1conv"						,     _UpperCAmelCase="pixelshuffle"						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)

          __A						:			Optional[Any]								=	image_size
          __A						:			int								=	patch_size
          __A						:			Any								=	num_channels
          __A						:			Tuple								=	embed_dim
          __A						:			Tuple								=	depths
          __A						:			List[Any]								=	len(_UpperCAmelCase)
          __A						:			int								=	num_heads
          __A						:			Optional[Any]								=	window_size
          __A						:			Tuple								=	mlp_ratio
          __A						:			Any								=	qkv_bias
          __A						:			Union[str, Any]								=	hidden_dropout_prob
          __A						:			List[Any]								=	attention_probs_dropout_prob
          __A						:			int								=	drop_path_rate
          __A						:			List[str]								=	hidden_act
          __A						:			List[Any]								=	use_absolute_embeddings
          __A						:			Any								=	layer_norm_eps
          __A						:			Tuple								=	initializer_range
          __A						:			str								=	upscale
          __A						:			Tuple								=	img_range
          __A						:			Optional[int]								=	resi_connection
          __A						:			List[str]								=	upsampler






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''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)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

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