




'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def UpperCAmelCase    (       UpperCAmelCase_						):
    if "model" in orig_key:
        _a				:							int							       =      orig_key.replace("""model."""   ,	""""""						)
    if "norm1" in orig_key:
        _a				:							Union[str, Any]							       =      orig_key.replace("""norm1"""   ,	"""attention.output.LayerNorm"""						)
    if "norm2" in orig_key:
        _a				:							List[str]							       =      orig_key.replace("""norm2"""   ,	"""output.LayerNorm"""						)
    if "norm" in orig_key:
        _a				:							Union[str, Any]							       =      orig_key.replace("""norm"""   ,	"""LayerNorm"""						)
    if "transformer" in orig_key:
        _a				:							int							       =      orig_key.split("""."""						)[0].split("""_"""						)[-1]
        _a				:							Dict							       =      orig_key.replace(F'''transformer_{layer_num}'''   ,	F'''encoder.layer.{layer_num}'''						)
    if "mha.attn" in orig_key:
        _a				:							Dict							       =      orig_key.replace("""mha.attn"""   ,	"""attention.self"""						)
    if "mha" in orig_key:
        _a				:							Union[str, Any]							       =      orig_key.replace("""mha"""   ,	"""attention"""						)
    if "W_q" in orig_key:
        _a				:							Union[str, Any]							       =      orig_key.replace("""W_q"""   ,	"""self.query"""						)
    if "W_k" in orig_key:
        _a				:							List[Any]							       =      orig_key.replace("""W_k"""   ,	"""self.key"""						)
    if "W_v" in orig_key:
        _a				:							str							       =      orig_key.replace("""W_v"""   ,	"""self.value"""						)
    if "ff.0" in orig_key:
        _a				:							Optional[Any]							       =      orig_key.replace("""ff.0"""   ,	"""intermediate.dense"""						)
    if "ff.2" in orig_key:
        _a				:							List[Any]							       =      orig_key.replace("""ff.2"""   ,	"""output.dense"""						)
    if "ff" in orig_key:
        _a				:							Union[str, Any]							       =      orig_key.replace("""ff"""   ,	"""output.dense"""						)
    if "mlm_class" in orig_key:
        _a				:							List[str]							       =      orig_key.replace("""mlm.mlm_class"""   ,	"""cls.predictions.decoder"""						)
    if "mlm" in orig_key:
        _a				:							Optional[Any]							       =      orig_key.replace("""mlm"""   ,	"""cls.predictions.transform"""						)
    if "backbone.backbone.encoders" in orig_key:
        _a				:							Optional[int]							       =      orig_key.replace("""backbone.backbone.encoders"""   ,	"""encoder.layer"""						)
    if "cls" not in orig_key:
        _a				:							Union[str, Any]							       =      """mra.""" + orig_key

    return orig_key





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    for key in orig_state_dict.copy().keys():
        _a				:							List[Any]							       =      orig_state_dict.pop(UpperCAmelCase_						)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            _a				:							List[Any]							       =      val

    _a				:							List[Any]							       =      orig_state_dict["""cls.predictions.decoder.bias"""]
    _a				:							Any							       =      torch.arange(UpperCAmelCase_						).expand((1, -1)						) + 2

    return orig_state_dict





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							List[Any]							       =      torch.load(UpperCAmelCase_   ,	map_location="""cpu"""						)["""model_state_dict"""]
    _a				:							Dict							       =      MraConfig.from_json_file(UpperCAmelCase_						)
    _a				:							Optional[int]							       =      MraForMaskedLM(UpperCAmelCase_						)

    _a				:							List[str]							       =      convert_checkpoint_helper(config.max_position_embeddings   ,	UpperCAmelCase_						)

    print(model.load_state_dict(UpperCAmelCase_						)						)
    model.eval()
    model.save_pretrained(UpperCAmelCase_						)

    print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)


if __name__ == "__main__":
  a		:	Optional[int]       =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The json file for Mra model config.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  a		:	Any       =      parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




