

"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_A						=     logging.get_logger(__name__)

_A						=     {
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
    import detectrona

class 			lowerCamelCase  (					lowerCAmelCase__      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     'layoutlmv2'



  def __init__(self       ,					_lowerCamelCase=30522       ,					_lowerCamelCase=768       ,					_lowerCamelCase=12       ,					_lowerCamelCase=12       ,					_lowerCamelCase=3072       ,					_lowerCamelCase="gelu"       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=512       ,					_lowerCamelCase=2       ,					_lowerCamelCase=0.02       ,					_lowerCamelCase=1e-12       ,					_lowerCamelCase=0       ,					_lowerCamelCase=1024       ,					_lowerCamelCase=128       ,					_lowerCamelCase=32       ,					_lowerCamelCase=True       ,					_lowerCamelCase=256       ,					_lowerCamelCase=64       ,					_lowerCamelCase=True       ,					_lowerCamelCase=[7, 7, 256]       ,					_lowerCamelCase=128       ,					_lowerCamelCase=128       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase=False       ,					_lowerCamelCase=None       ,					**_lowerCamelCase       ,					):


        """simple docstring"""

        super().__init__(
            vocab_size=_lowerCamelCase       ,					hidden_size=_lowerCamelCase       ,					num_hidden_layers=_lowerCamelCase       ,					num_attention_heads=_lowerCamelCase       ,					intermediate_size=_lowerCamelCase       ,					hidden_act=_lowerCamelCase       ,					hidden_dropout_prob=_lowerCamelCase       ,					attention_probs_dropout_prob=_lowerCamelCase       ,					max_position_embeddings=_lowerCamelCase       ,					type_vocab_size=_lowerCamelCase       ,					initializer_range=_lowerCamelCase       ,					layer_norm_eps=_lowerCamelCase       ,					pad_token_id=_lowerCamelCase       ,					**_lowerCamelCase       ,					)
        UpperCAmelCase__ :       List[str]						=						max_ad_position_embeddings
        UpperCAmelCase__ :       Optional[Any]						=						max_rel_pos
        UpperCAmelCase__ :       List[str]						=						rel_pos_bins
        UpperCAmelCase__ :       List[Any]						=						fast_qkv
        UpperCAmelCase__ :       Optional[Any]						=						max_rel_ad_pos
        UpperCAmelCase__ :       str						=						rel_ad_pos_bins
        UpperCAmelCase__ :       str						=						convert_sync_batchnorm
        UpperCAmelCase__ :       Union[str, Any]						=						image_feature_pool_shape
        UpperCAmelCase__ :       Any						=						coordinate_size
        UpperCAmelCase__ :       int						=						shape_size
        UpperCAmelCase__ :       int						=						has_relative_attention_bias
        UpperCAmelCase__ :       Union[str, Any]						=						has_spatial_attention_bias
        UpperCAmelCase__ :       Union[str, Any]						=						has_visual_segment_embedding
        UpperCAmelCase__ :       Tuple						=						(
            detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
        )



  @classmethod
  def    _a			(self  ):


        """simple docstring"""

        return {
            "MODEL.MASK_ON": True,
            "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
            "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
            "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
            "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
            "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
            "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
            "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
            "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
            "MODEL.POST_NMS_TOPK_TEST": 1000,
            "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
            "MODEL.ROI_HEADS.NUM_CLASSES": 5,
            "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
            "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
            "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
            "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
            "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
            "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
            "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
            "MODEL.RESNETS.DEPTH": 101,
            "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
            "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
            "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
            "MODEL.RESNETS.NUM_GROUPS": 32,
            "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
            "MODEL.RESNETS.STRIDE_IN_1X1": False,
        }




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						detectrona.config.get_cfg()
        for k, v in self.detectrona_config_args.items():
              UpperCAmelCase__ :       Dict						=						k.split("""."""  )
              UpperCAmelCase__ :       Optional[Any]						=						detectrona_config
              for attribute in attributes[:-1]:
                    UpperCAmelCase__ :       Any						=						getattr(_lowerCamelCase       ,					_lowerCamelCase  )
              setattr(_lowerCamelCase       ,					attributes[-1]       ,					_lowerCamelCase  )

        return detectrona_config








"""simple docstring"""
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
      # Initialise PyTorch model
      UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
      UpperCAmelCase__ :       Any						=						GPTNeoConfig(
          hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
      print(F"""Building PyTorch model from configuration: {config}"""						)
      UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
      model.save_pretrained(lowerCAmelCase						)


if __name__ == "__main__":
    _A						=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained mesh-tf model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    _A						=     parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

