'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

    return orig_key





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

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

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

    return orig_state_dict





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

    _a							:       Union[str, Any]       					=			convert_checkpoint_helper(config.max_position_embeddings      ,  a_	)

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

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


if __name__ == "__main__":
  a		:  Any   			=			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		:  str   			=			parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class     UpperCamelCase					(  unittest.TestCase					):



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			"""runwayml/stable-diffusion-v1-5"""
      _a							:       Tuple       					=			TextToVideoZeroPipeline.from_pretrained(__lowerCAmelCase		,    torch_dtype=torch.floataa   ).to("""cuda"""   )
      _a							:       Dict       					=			DDIMScheduler.from_config(pipe.scheduler.config   )
      _a							:       int       					=			torch.Generator(device="""cuda"""   ).manual_seed(0   )

      _a							:       Union[str, Any]       					=			"""A bear is playing a guitar on Times Square"""
      _a							:       List[Any]       					=			pipe(prompt=__lowerCAmelCase		,    generator=__lowerCAmelCase   ).images

      _a							:       Any       					=			load_pt(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"""   )

      assert_mean_pixel_difference(__lowerCAmelCase		,    __lowerCAmelCase   )


