import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'config.json'






      @register_to_config
      def __init__(      self  ,		__lowerCamelCase=2  ,		__lowerCamelCase=5  ,		__lowerCamelCase=(2, 5)  ,		__lowerCamelCase="for diffusion"  ,		__lowerCamelCase=[1, 3]  ,		)     ->				Optional[Any]:


          '''simple docstring'''







          pass





class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'config.json'






      @register_to_config
      def __init__(      self  ,		__lowerCamelCase=2  ,		__lowerCamelCase=5  ,		__lowerCamelCase=(2, 5)  ,		__lowerCamelCase="for diffusion"  ,		__lowerCamelCase=[1, 3]  ,		)     ->				List[Any]:


          '''simple docstring'''







          pass





class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'config.json'






      @register_to_config
      def __init__(      self  ,		__lowerCamelCase=2  ,		__lowerCamelCase=5  ,		__lowerCamelCase=(2, 5)  ,		__lowerCamelCase="for diffusion"  ,		__lowerCamelCase=[1, 3]  ,		__lowerCamelCase=[1, 3]  ,		)     ->				List[Any]:


          '''simple docstring'''







          pass





class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'config.json'






      @register_to_config
      def __init__(      self  ,		__lowerCamelCase=2  ,		__lowerCamelCase=5  ,		__lowerCamelCase=(2, 5)  ,		__lowerCamelCase="for diffusion"  ,		__lowerCamelCase=[1, 5]  ,		__lowerCamelCase=[5, 4]  ,		)     ->				List[Any]:


          '''simple docstring'''







          pass





class 				lowerCamelCase			(							unittest.TestCase					):






      def 			lowerCAmelCase_   (      self						)     ->				Union[str, Any]:


          '''simple docstring'''







          with self.assertRaises(__lowerCamelCase						):
              ConfigMixin.load_config("""dummy_path"""						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   SampleObject()
          snake_case:						Any         =   obj.config
          assert config["a"] == 2
          assert config["b"] == 5
          assert config["c"] == (2, 5)
          assert config["d"] == "for diffusion"
          assert config["e"] == [1, 3]

          # init ignore private arguments
          snake_case:						Dict         =   SampleObject(_name_or_path="""lalala"""						)
          snake_case:						Optional[int]         =   obj.config
          assert config["a"] == 2
          assert config["b"] == 5
          assert config["c"] == (2, 5)
          assert config["d"] == "for diffusion"
          assert config["e"] == [1, 3]

          # can override default
          snake_case:						Tuple         =   SampleObject(c=6						)
          snake_case:						str         =   obj.config
          assert config["a"] == 2
          assert config["b"] == 5
          assert config["c"] == 6
          assert config["d"] == "for diffusion"
          assert config["e"] == [1, 3]

          # can use positional arguments.
          snake_case:						Optional[int]         =   SampleObject(1  ,		c=6						)
          snake_case:						Optional[int]         =   obj.config
          assert config["a"] == 1
          assert config["b"] == 5
          assert config["c"] == 6
          assert config["d"] == "for diffusion"
          assert config["e"] == [1, 3]






      def 			lowerCAmelCase_   (      self						)     ->				Union[str, Any]:


          '''simple docstring'''







          snake_case:						Any         =   SampleObject()
          snake_case:						int         =   obj.config

          assert config["a"] == 2
          assert config["b"] == 5
          assert config["c"] == (2, 5)
          assert config["d"] == "for diffusion"
          assert config["e"] == [1, 3]

          with tempfile.TemporaryDirectory() as tmpdirname:
              obj.save_config(__lowerCamelCase						)
              snake_case:						Tuple         =   SampleObject.from_config(SampleObject.load_config(__lowerCamelCase						)						)
              snake_case:						Optional[int]         =   new_obj.config

          # unfreeze configs
          snake_case:						Optional[int]         =   dict(__lowerCamelCase						)
          snake_case:						Optional[int]         =   dict(__lowerCamelCase						)

          assert config.pop("""c"""						) == (2, 5)  # instantiated as tuple
          assert new_config.pop("""c"""						) == [2, 5]  # saved & loaded as list because of json
          config.pop("""_use_default_values"""						)
          assert config == new_config






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   logging.get_logger("""diffusers.configuration_utils"""						)
          # 30 for warning
          logger.setLevel(30						)

          with CaptureLogger(__lowerCamelCase						) as cap_logger:
              snake_case:						List[Any]         =   DDIMScheduler.from_pretrained(
                  """hf-internal-testing/tiny-stable-diffusion-torch"""  ,		subfolder="""scheduler"""						)

          assert ddim.__class__ == DDIMScheduler
          # no warning should be thrown
          assert cap_logger.out == ""






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   logging.get_logger("""diffusers.configuration_utils"""						)
          # 30 for warning
          logger.setLevel(30						)

          with CaptureLogger(__lowerCamelCase						) as cap_logger:
              snake_case:						List[Any]         =   EulerDiscreteScheduler.from_pretrained(
                  """hf-internal-testing/tiny-stable-diffusion-torch"""  ,		subfolder="""scheduler"""						)

          assert euler.__class__ == EulerDiscreteScheduler
          # no warning should be thrown
          assert cap_logger.out == ""






      def 			lowerCAmelCase_   (      self						)     ->				Union[str, Any]:


          '''simple docstring'''







          snake_case:						str         =   logging.get_logger("""diffusers.configuration_utils"""						)
          # 30 for warning
          logger.setLevel(30						)

          with CaptureLogger(__lowerCamelCase						) as cap_logger:
              snake_case:						str         =   EulerAncestralDiscreteScheduler.from_pretrained(
                  """hf-internal-testing/tiny-stable-diffusion-torch"""  ,		subfolder="""scheduler"""						)

          assert euler.__class__ == EulerAncestralDiscreteScheduler
          # no warning should be thrown
          assert cap_logger.out == ""






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   logging.get_logger("""diffusers.configuration_utils"""						)
          # 30 for warning
          logger.setLevel(30						)

          with CaptureLogger(__lowerCamelCase						) as cap_logger:
              snake_case:						List[Any]         =   PNDMScheduler.from_pretrained(
                  """hf-internal-testing/tiny-stable-diffusion-torch"""  ,		subfolder="""scheduler"""						)

          assert pndm.__class__ == PNDMScheduler
          # no warning should be thrown
          assert cap_logger.out == ""






      def 			lowerCAmelCase_   (      self						)     ->				Union[str, Any]:


          '''simple docstring'''







          snake_case:						Dict         =   logging.get_logger("""diffusers.configuration_utils"""						)
          # 30 for warning
          logger.setLevel(30						)

          with CaptureLogger(__lowerCamelCase						) as cap_logger:
              snake_case:						str         =   DDPMScheduler.from_pretrained(
                  """hf-internal-testing/tiny-stable-diffusion-torch"""  ,		subfolder="""scheduler"""  ,		prediction_type="""sample"""  ,		beta_end=8  ,		)

          with CaptureLogger(__lowerCamelCase						) as cap_logger_a:
              snake_case:						List[Any]         =   DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""  ,		beta_start=88						)

          assert ddpm.__class__ == DDPMScheduler
          assert ddpm.config.prediction_type == "sample"
          assert ddpm.config.beta_end == 8
          assert ddpm_a.config.beta_start == 88

          # no warning should be thrown
          assert cap_logger.out == ""
          assert cap_logger_a.out == ""






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Optional[int]         =   logging.get_logger("""diffusers.configuration_utils"""						)
          # 30 for warning
          logger.setLevel(30						)

          with CaptureLogger(__lowerCamelCase						) as cap_logger:
              snake_case:						Any         =   DPMSolverMultistepScheduler.from_pretrained(
                  """hf-internal-testing/tiny-stable-diffusion-torch"""  ,		subfolder="""scheduler"""						)

          assert dpm.__class__ == DPMSolverMultistepScheduler
          # no warning should be thrown
          assert cap_logger.out == ""






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   SampleObject()

          snake_case:						Dict         =   {k: v for k, v in config.config.items() if not k.startswith("""_"""						)}

          # make sure that default config has all keys in `_use_default_values`
          assert set(config_dict.keys()						) == set(config.config._use_default_values						)

          with tempfile.TemporaryDirectory() as tmpdirname:
              config.save_config(__lowerCamelCase						)

              # now loading it with SampleObject2 should put f into `_use_default_values`
              snake_case:						int         =   SampleObjecta.from_config(__lowerCamelCase						)

              assert "f" in config._use_default_values
              assert config.f == [1, 3]

          # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
          # **BECAUSE** it is part of `config._use_default_values`
          snake_case:						Optional[int]         =   SampleObjecta.from_config(config.config						)
          assert new_config.f == [5, 4]

          config.config._use_default_values.pop()
          snake_case:						Optional[int]         =   SampleObjecta.from_config(config.config						)
          assert new_config_a.f == [1, 3]

          # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
          assert new_config_a.e == [1, 3]



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

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

    return orig_key

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Any       )->				Dict:
    for key in orig_state_dict.copy().keys():
        snake_case:						Tuple         =   orig_state_dict.pop(_lowerCAmelCase       )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            snake_case:						int         =   val

    snake_case:						str         =   orig_state_dict["""cls.predictions.decoder.bias"""]
    snake_case:						List[str]         =   torch.arange(_lowerCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   Any       )->				List[Any]:
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )["""model_state_dict"""]
    snake_case:						Optional[int]         =   MraConfig.from_json_file(_lowerCAmelCase       )
    snake_case:						List[str]         =   MraForMaskedLM(_lowerCAmelCase       )

    snake_case:						Any         =   convert_checkpoint_helper(config.max_position_embeddings			,	_lowerCAmelCase       )

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

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


if __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, 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.'
    )
    __lowerCAmelCase    :  Dict										= parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


